diff --git a/R/00jmv.R b/R/00jmv.R new file mode 100644 index 0000000..acc41ba --- /dev/null +++ b/R/00jmv.R @@ -0,0 +1,76 @@ + +# This file is automatically generated, you probably don't want to edit this + +.jmvrefs <- list( + `lc_median_bs`=list( + `type`="article", + `author`="Bonett, D.G. & Price, R. M.", + `year`=2002, + `title`="Statistical inference for a linear function of medians: Confidence intervals, hypothesis testing, and sample size requirements", + `publisher`="Psychological Methods", + `volume`=7, + `pages`="370-383", + `url`="https://psycnet.apa.org/doiLanding?doi=10.1037%2F1082-989X.7.3.370"), + `lc_stdmean_bs`=list( + `type`="article", + `author`="Bonett, D.G.", + `year`=2008, + `title`="Confidence intervals for standardized linear contrasts of means", + `publisher`="Psychological Methods", + `volume`=13, + `pages`="99-109", + `url`="https://psycnet.apa.org/doiLanding?doi=10.1037%2F1082-989X.13.2.99"), + `median_ps`=list( + `type`="article", + `author`="Bonett, D.G. & Price, R. M.", + `year`=2020, + `title`="Interval estimation for linear functions of medians in within-subjects and mixed designs", + `publisher`="British Journal of Mathematical and Statistical Psychology", + `volume`=73, + `pages`="333-346", + `url`="https://bpspsychub.onlinelibrary.wiley.com/doi/10.1111/bmsp.12171"), + `ratio_ps`=list( + `type`="article", + `author`="Bonett, D.G. & Price, R. M.", + `year`=2020, + `title`="Confidence intervals for ratios of means and medians", + `publisher`="Journal of Educational and Behavioral Statistics", + `volume`=45, + `pages`="750-770", + `url`="https://journals.sagepub.com/doi/10.3102/1076998620934125"), + `metafor`=list( + `type`="article", + `author`="Viechtbauer, W.", + `year`=2010, + `title`="Conducting Meta-Analyses in R with the metafor Package", + `publisher`="Journal of Statistical Software", + `volume`=36, + `pages`="1-48", + `url`="https://doi.org/10.18637/jss.v036.i03"), + `ggdist`=list( + `type`="article", + `author`="Kay, M.", + `year`=2002, + `title`="ggdist: Visualizations of Distributions and Uncertainty in the Grammar of Graphics", + `publisher`="IEEE Transactions on Visualization and Computer Graphics", + `volume`=30, + `pages`="414-424", + `url`="https://zenodo.org/records/7933524"), + `prop1`=list( + `type`="article", + `author`="Agresti, A. & Coull, B. A.", + `year`=1998, + `title`="Approximate is better than exact for interval estimation of binomial proportions", + `publisher`="The American Statistician", + `volume`=52, + `pages`="119-126", + `url`="https://www.tandfonline.com/doi/abs/10.1080/00031305.1998.10480550"), + `prop_paired`=list( + `type`="article", + `author`="Bonett, D.G. & Price, R. M.", + `year`=2012, + `title`="Adjusted wald confidence interval for a difference of binomial proportions based on paired data", + `publisher`="Journal of Educational and Behavioral Statistics", + `volume`=37, + `pages`="479-488", + `url`="https://journals.sagepub.com/doi/10.3102/1076998611411915")) diff --git a/R/estimate_mdiff_ind_contrast.R b/R/estimate_mdiff_ind_contrast.R index 580f5fe..08588ec 100644 --- a/R/estimate_mdiff_ind_contrast.R +++ b/R/estimate_mdiff_ind_contrast.R @@ -889,9 +889,8 @@ Invalid groups are those with n < 2. estimate$es_mean_ratio_properties <- list( message_html = paste( - if (no_negs) "" else "WARNING! Your data has negative values. ", - "This effect-size measure is appropriate only for true ratio scales where values < 0 are impossible. - For more information on this effect size, see Bonett & Price (2020) doi: 10.3102/1076998620934125.", + if (no_negs) "" else "WARNING! Your dataset includes negative values. ", + "This effect-size measure is appropriate only for true ratio scales.", sep = "" ) ) @@ -899,7 +898,7 @@ Invalid groups are those with n < 2. if (!no_negs) { estimate$warnings <- c( estimate$warnings, - "neg_values" = "The ratio between group effect size is appropriate only for true ratio scales where values < 0 are impossible. Your data has negative values and therefore any ratio between groups is invalid and should not be interpreted." + "neg_values" = "The ratio between group effect size is appropriate only for true ratio scales where values < 0 are impossible. Your data include at least one negative value, so the requested ratio effect size is not reported." ) } diff --git a/R/estimate_mdiff_paired.R b/R/estimate_mdiff_paired.R index 5b094d2..a5aa0d2 100644 --- a/R/estimate_mdiff_paired.R +++ b/R/estimate_mdiff_paired.R @@ -639,9 +639,8 @@ estimate_mdiff_paired.data.frame <- function( estimate$es_mean_ratio_properties <- list( message_html = paste( - if (no_negs) "" else "WARNING! Your data has negative values. ", - "This effect-size measure is appropriate only for true ratio scales where values < 0 are impossible. - For more information on this effect size, see Bonett & Price (2020) doi: 10.3102/1076998620934125.", + if (no_negs) "" else "WARNING! Your dataset includes negative values. ", + "This effect-size measure is appropriate only for true ratio scales.", sep = "" ) ) @@ -649,7 +648,7 @@ estimate_mdiff_paired.data.frame <- function( if (!no_negs) { estimate$warnings <- c( estimate$warnings, - "neg_values" = "The ratio between measures effect size is appropriate only for true ratio scales where values < 0 are impossible. Your data has negative values and therefore the any ratio between measures is invalid and should not be interpreted." + "neg_values" = "The ratio between group effect size is appropriate only for true ratio scales where values < 0 are impossible. Your data include at least one negative value, so the requested ratio effect size is not reported." ) } diff --git a/R/estimate_mdiff_two.R b/R/estimate_mdiff_two.R index 342cec4..28502c3 100644 --- a/R/estimate_mdiff_two.R +++ b/R/estimate_mdiff_two.R @@ -147,8 +147,8 @@ #' ) #' #' \dontrun{ -#' # To visualize the estimated median difference (raw data only) -#' plot_mdiff(estimate, effect_size = "median") +#' # To visualize the estimated mean difference +#' plot_mdiff(estimate, effect_size = "mean") #' } #' @@ -164,8 +164,8 @@ #' ) #' #' \dontrun{ -#' # To visualize the estimated mean difference -#' plot_mdiff(estimate, effect_size = "mean") +#' # To visualize the estimated median difference (raw data only) +#' plot_mdiff(estimate, effect_size = "median") #' } #' #' diff --git a/R/jamovidescribe.b.R b/R/jamovidescribe.b.R index ae14acf..a60305f 100644 --- a/R/jamovidescribe.b.R +++ b/R/jamovidescribe.b.R @@ -279,10 +279,10 @@ jamovidescribeClass <- if (requireNamespace('jmvcore', quietly=TRUE)) R6::R6Clas myplot <- myplot + ggplot2::theme( - axis.text.y = element_text(size = axis.text.y), - axis.title.y = element_text(size = axis.title.y), - axis.text.x = element_text(size = axis.text.x), - axis.title.x = element_text(size = axis.title.x) + axis.text.y = ggtext::element_markdown(size = axis.text.y), + axis.title.y = ggtext::element_markdown(size = axis.title.y), + axis.text.x = ggtext::element_markdown(size = axis.text.x), + axis.title.x = ggtext::element_markdown(size = axis.title.x) ) diff --git a/R/jamovimagnitude.b.R b/R/jamovimagnitude.b.R index 88185f9..e0fe944 100644 --- a/R/jamovimagnitude.b.R +++ b/R/jamovimagnitude.b.R @@ -276,10 +276,10 @@ jamovimagnitudeClass <- if (requireNamespace('jmvcore', quietly=TRUE)) R6::R6Cla myplot <- myplot + ggplot2::theme( - axis.text.y = element_text(size = axis.text.y), - axis.title.y = element_text(size = axis.title.y), - axis.text.x = element_text(size = axis.text.x), - axis.title.x = element_text(size = axis.title.x) + axis.text.y = ggtext::element_markdown(size = axis.text.y), + axis.title.y = ggtext::element_markdown(size = axis.title.y), + axis.text.x = ggtext::element_markdown(size = axis.text.x), + axis.title.x = ggtext::element_markdown(size = axis.title.x) ) diff --git a/R/jamovimagnitude.h.R b/R/jamovimagnitude.h.R index f217a6b..14cfe60 100644 --- a/R/jamovimagnitude.h.R +++ b/R/jamovimagnitude.h.R @@ -1098,7 +1098,8 @@ jamovimagnitudeResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6C requiresData=TRUE, width=400, height=300, - renderFun=".magnitude_plot"))})) + renderFun=".magnitude_plot", + refs="ggdist"))})) jamovimagnitudeBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class( "jamovimagnitudeBase", diff --git a/R/jamovimdiff2x2.h.R b/R/jamovimdiff2x2.h.R index ff21243..bc47128 100644 --- a/R/jamovimdiff2x2.h.R +++ b/R/jamovimdiff2x2.h.R @@ -2389,6 +2389,7 @@ jamovimdiff2x2Results <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Cl name="es_median_difference", title="Median Difference", visible="(effect_size == 'median_difference' & design == 'fully_between')", + refs="lc_median_bs", rows="15 - switch - outcome_variable - grouping_variable_A - grouping_variable_B - outcome_variable_level1 - outcome_variable_level2 - outcome_variable_name_bs - grouping_variable - repeated_measures_name - outcome_variable_name - conf_level - effect_size - assume_equal_variance - show_details", columns=list( list( @@ -2540,6 +2541,7 @@ jamovimdiff2x2Results <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Cl name="es_smd", title="Standardized Mean Difference", rows=5, + refs="lc_stdmean_bs", visible="(effect_size == 'mean_difference' & design == 'fully_between')", clearWith=list( "switch", @@ -2810,6 +2812,7 @@ jamovimdiff2x2Results <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Cl requiresData=TRUE, width=700, height=400, + refs="ggdist", renderFun=".estimation_plot")) self$add(jmvcore::Image$new( options=options, diff --git a/R/jamovimdiffindcontrast.h.R b/R/jamovimdiffindcontrast.h.R index 281e87a..355453a 100644 --- a/R/jamovimdiffindcontrast.h.R +++ b/R/jamovimdiffindcontrast.h.R @@ -2191,6 +2191,7 @@ jamovimdiffindcontrastResults <- if (requireNamespace("jmvcore", quietly=TRUE)) "effect_size", "assume_equal_variance", "show_details"), + refs="lc_median_bs", columns=list( list( `name`="outcome_variable_name", @@ -2300,6 +2301,7 @@ jamovimdiffindcontrastResults <- if (requireNamespace("jmvcore", quietly=TRUE)) title="Standardized Mean Difference", rows=1, visible="(effect_size == 'mean_difference')", + refs="lc_stdmean_bs", clearWith=list( "switch", "outcome_variable", @@ -2521,6 +2523,7 @@ jamovimdiffindcontrastResults <- if (requireNamespace("jmvcore", quietly=TRUE)) options=options, name="estimation_plots", title="Estimation Figure", + refs="ggdist", template=jmvcore::Image$new( options=options, title="$key", diff --git a/R/jamovimdiffpaired.b.R b/R/jamovimdiffpaired.b.R index 2c911bc..3be04dd 100644 --- a/R/jamovimdiffpaired.b.R +++ b/R/jamovimdiffpaired.b.R @@ -315,6 +315,10 @@ jamovi_mdiff_paired <- function(self, save_raw_data = FALSE) { if (!is.na(estimate$warnings["neg_values"])) { estimate$warnings <- estimate$warnings[names(estimate$warnings) != "neg_values"] } + } else { + to_show <- is.na(estimate$warnings["neg_values"]) + self$results$es_mean_ratio$setVisible(to_show & self$options$effect_size == "mean_difference") + self$results$es_median_ratio$setVisible(to_show & self$options$effect_size == "median_difference") } notes <- c(notes, estimate$warnings) diff --git a/R/jamovimdiffpaired.h.R b/R/jamovimdiffpaired.h.R index b0d781a..87131ef 100644 --- a/R/jamovimdiffpaired.h.R +++ b/R/jamovimdiffpaired.h.R @@ -2037,6 +2037,7 @@ jamovimdiffpairedResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R name="es_smd", title="Standardized Mean Difference", rows=1, + refs="lc_stdmean_bs", visible="(effect_size == 'mean_difference')", clearWith=list( "switch", @@ -2115,6 +2116,7 @@ jamovimdiffpairedResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R title="Ratio of Means", visible="(show_ratio & effect_size == 'mean_difference')", rows=1, + refs="ratio_ps", clearWith=list( "switch", "reference_measure", @@ -2175,6 +2177,7 @@ jamovimdiffpairedResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R name="es_median_difference", title="Median Difference", visible="(effect_size == 'median_difference')", + refs="median_ps", rows=3, clearWith=list( "switch", @@ -2234,6 +2237,7 @@ jamovimdiffpairedResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R title="Ratio of Medians", visible="(show_ratio & effect_size == 'median_difference')", rows=1, + refs="ratio_ps", clearWith=list( "switch", "reference_measure", @@ -2439,6 +2443,7 @@ jamovimdiffpairedResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R width=300, height=450, requiresData=TRUE, + refs="ggdist", renderFun=".estimation_plots"))})) jamovimdiffpairedBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class( diff --git a/R/jamovimdifftwo.b.R b/R/jamovimdifftwo.b.R index 88e0beb..77852a9 100644 --- a/R/jamovimdifftwo.b.R +++ b/R/jamovimdifftwo.b.R @@ -294,6 +294,10 @@ jamovi_mdiff_two <- function( if (!is.na(estimate$warnings["neg_values"])) { estimate$warnings <- estimate$warnings[names(estimate$warnings) != "neg_values"] } + } else { + to_show <- is.na(estimate$warnings["neg_values"]) + self$results$es_mean_ratio$setVisible(to_show & self$options$effect_size == "mean_difference") + self$results$es_median_ratio$setVisible(to_show & self$options$effect_size == "median_difference") } notes <- c(notes, estimate$warnings) diff --git a/R/jamovimdifftwo.h.R b/R/jamovimdifftwo.h.R index 0e3ddaa..6cc8bc6 100644 --- a/R/jamovimdifftwo.h.R +++ b/R/jamovimdifftwo.h.R @@ -1869,6 +1869,7 @@ jamovimdifftwoResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Cl name="es_smd", title="Standardized Mean Difference", rows=1, + refs="lc_stdmean_bs", visible="(effect_size == 'mean_difference')", clearWith=list( "switch", @@ -1953,6 +1954,7 @@ jamovimdifftwoResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Cl title="Ratio of Means", visible="(show_ratio & effect_size == 'mean_difference')", rows="(outcome_variable)", + refs="ratio_ps", clearWith=list( "switch", "outcome_variable", @@ -2015,6 +2017,7 @@ jamovimdifftwoResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Cl title="Median Difference", visible="(effect_size == 'median_difference')", rows=3, + refs="lc_median_bs", clearWith=list( "switch", "outcome_variable", @@ -2074,6 +2077,7 @@ jamovimdifftwoResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Cl title="Ratio of Medians", visible="(show_ratio & effect_size == 'median_difference')", rows="(outcome_variable)", + refs="ratio_ps", clearWith=list( "switch", "outcome_variable", @@ -2286,6 +2290,7 @@ jamovimdifftwoResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Cl options=options, name="estimation_plots", title="Estimation Figure", + refs="ggdist", template=jmvcore::Image$new( options=options, title="$key", diff --git a/R/jamovimetamdiff.h.R b/R/jamovimetamdiff.h.R index bc3178d..fa6843e 100644 --- a/R/jamovimetamdiff.h.R +++ b/R/jamovimetamdiff.h.R @@ -1909,6 +1909,7 @@ jamovimetamdiffResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6C name="es_meta", title="Meta-Analytic Effect Sizes", rows=1, + refs="metafor", clearWith=list( "switch", "comparison_means", diff --git a/R/jamovimetamean.h.R b/R/jamovimetamean.h.R index bd85a76..41582e6 100644 --- a/R/jamovimetamean.h.R +++ b/R/jamovimetamean.h.R @@ -1806,6 +1806,7 @@ jamovimetameanResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Cl name="es_meta", title="Meta-Analytic Effect Sizes", rows=1, + refs="metafor", clearWith=list( "means", "sds", diff --git a/R/jamovimetapdiff.h.R b/R/jamovimetapdiff.h.R index 489c210..e107c6d 100644 --- a/R/jamovimetapdiff.h.R +++ b/R/jamovimetapdiff.h.R @@ -1750,6 +1750,7 @@ jamovimetapdiffResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6C options=options, name="es_meta", title="Meta-Analytic Effect Sizes", + refs="metafor", rows=1, clearWith=list( "reference_cases", diff --git a/R/jamovimetaproportion.h.R b/R/jamovimetaproportion.h.R index 6ae4804..c1010ad 100644 --- a/R/jamovimetaproportion.h.R +++ b/R/jamovimetaproportion.h.R @@ -1699,6 +1699,7 @@ jamovimetaproportionResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6 name="es_meta", title="Meta-Analytic Effect Sizes", rows=1, + refs="metafor", clearWith=list( "cases", "ns", diff --git a/R/jamovimetar.h.R b/R/jamovimetar.h.R index 2780c14..54df477 100644 --- a/R/jamovimetar.h.R +++ b/R/jamovimetar.h.R @@ -1714,6 +1714,7 @@ jamovimetarResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class name="es_meta", title="Meta-Analytic Effect Sizes", rows=1, + refs="metafor", clearWith=list( "rs", "ns", diff --git a/R/jamovipdiffpaired.h.R b/R/jamovipdiffpaired.h.R index 12d0c46..5e30e0a 100644 --- a/R/jamovipdiffpaired.h.R +++ b/R/jamovipdiffpaired.h.R @@ -873,6 +873,7 @@ jamovipdiffpairedResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R name="es_proportion_difference", title="Proportion Difference", rows=3, + refs="prop_paired", clearWith=list( "reference_measure", "comparison_measure", @@ -1027,6 +1028,7 @@ jamovipdiffpairedResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R width=300, height=450, requiresData=TRUE, + refs="ggdist", renderFun=".estimation_plots"))})) jamovipdiffpairedBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class( diff --git a/R/jamovipdifftwo.h.R b/R/jamovipdifftwo.h.R index c14f396..a194b31 100644 --- a/R/jamovipdifftwo.h.R +++ b/R/jamovipdifftwo.h.R @@ -949,6 +949,7 @@ jamovipdifftwoResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Cl name="es_proportion_difference", title="Proportion Difference", rows=3, + refs="prop1", clearWith=list( "outcome_variable", "grouping_variable", @@ -1262,6 +1263,7 @@ jamovipdifftwoResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Cl options=options, name="estimation_plots", title="Estimation Figure", + refs="ggdist", template=jmvcore::Image$new( options=options, title="$key", diff --git a/R/jamoviproportion.b.R b/R/jamoviproportion.b.R index 7d5325b..832d006 100644 --- a/R/jamoviproportion.b.R +++ b/R/jamoviproportion.b.R @@ -284,10 +284,10 @@ jamoviproportionClass <- if (requireNamespace('jmvcore', quietly=TRUE)) R6::R6Cl myplot <- myplot + ggplot2::theme( - axis.text.y = element_text(size = axis.text.y), - axis.title.y = element_text(size = axis.title.y), - axis.text.x = element_text(size = axis.text.x), - axis.title.x = element_text(size = axis.title.x) + axis.text.y = ggtext::element_markdown(size = axis.text.y), + axis.title.y = ggtext::element_markdown(size = axis.title.y), + axis.text.x = ggtext::element_markdown(size = axis.text.x), + axis.title.x = ggtext::element_markdown(size = axis.title.x) ) diff --git a/R/jamoviproportion.h.R b/R/jamoviproportion.h.R index 688a6f7..9d5e197 100644 --- a/R/jamoviproportion.h.R +++ b/R/jamoviproportion.h.R @@ -473,6 +473,7 @@ jamoviproportionResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6 name="overview", title="Overview", rows=1, + refs="prop1", clearWith=list( "outcome_variable", "cases", @@ -627,6 +628,7 @@ jamoviproportionResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6 options=options, name="estimation_plots", title="Estimation Figure", + refs="ggdist", template=jmvcore::Image$new( options=options, title="$key", diff --git a/R/meta_any.R b/R/meta_any.R index f8d0e5a..fc54b96 100644 --- a/R/meta_any.R +++ b/R/meta_any.R @@ -639,7 +639,7 @@ After dropping any NA rows, current data has: ) res$es_heterogeneity_properties <- list( - message_html = "As of version 1.02 esci has implemented an improved method for calculating the CI for the diamond ratio; these will no longer match those presented in the 2nd edition of Introduction to the New Statistics." + message_html = "As of version 1.0.2 esci has implemented an improved method for calculating the CI for the diamond ratio; these will no longer match those presented in the 2nd edition of Introduction to the New Statistics." ) if (moderator) res$es_meta_difference <- es_meta_difference diff --git a/R/plot_magnitude.R b/R/plot_magnitude.R index 1041dd6..4fcbc64 100644 --- a/R/plot_magnitude.R +++ b/R/plot_magnitude.R @@ -276,6 +276,13 @@ plot_magnitude <- function( myplot <- myplot + ggplot2::xlab(xlab) + ggplot2::ylab(ylab) + myplot <- myplot + ggplot2::theme( + axis.text.y = ggtext::element_markdown(), + axis.title.y = ggtext::element_markdown(), + axis.text.x = ggtext::element_markdown(), + axis.title.x = ggtext::element_markdown() + ) + # Attach warnings and return ------------------- myplot$warnings <- c(myplot$warnings, warnings) @@ -751,6 +758,13 @@ plot_proportion <- function( xlab <- gdata$outcome_variable_name[[1]] myplot <- myplot + ggplot2::xlab(xlab) + ggplot2::ylab(ylab) + myplot <- myplot + ggplot2::theme( + axis.text.y = ggtext::element_markdown(), + axis.title.y = ggtext::element_markdown(), + axis.text.x = ggtext::element_markdown(), + axis.title.x = ggtext::element_markdown() + ) + # Limits ylow <- min(0, rope[[1]], na.rm = TRUE) yhigh <- max(1, rope[[2]], na.rm = TRUE) diff --git a/R/plot_mdiff.R b/R/plot_mdiff.R index 232c858..433030d 100644 --- a/R/plot_mdiff.R +++ b/R/plot_mdiff.R @@ -313,6 +313,12 @@ plot_mdiff <- function( myplot <- myplot + ggplot2::xlab(xlab) + ggplot2::ylab(ylab) + myplot <- myplot + ggplot2::theme( + axis.text.y = ggtext::element_markdown(), + axis.title.y = ggtext::element_markdown(), + axis.text.x = ggtext::element_markdown(), + axis.title.x = ggtext::element_markdown() + ) # Attach warnings and return ------------------- myplot$warnings <- c(myplot$warnings, warnings) @@ -1273,8 +1279,10 @@ plot_pdiff <- function( myplot <- myplot + ggplot2::theme( + axis.text.y = ggtext::element_markdown(), axis.title.y = ggtext::element_markdown(), - axis.title.x = ggtext::element_markdown(), + axis.text.x = ggtext::element_markdown(), + axis.title.x = ggtext::element_markdown() ) @@ -1451,9 +1459,10 @@ plot_rdiff <- function( myplot <- myplot + ggplot2::theme( - axis.title.x = ggtext::element_markdown(), + axis.text.y = ggtext::element_markdown(), axis.title.y = ggtext::element_markdown(), - axis.text.x = ggtext::element_markdown() + axis.text.x = ggtext::element_markdown(), + axis.title.x = ggtext::element_markdown() ) # Attach warnings and return ------------------- diff --git a/R/tools_jamovi_plots.R b/R/tools_jamovi_plots.R index ed6de84..85a0f96 100644 --- a/R/tools_jamovi_plots.R +++ b/R/tools_jamovi_plots.R @@ -234,10 +234,10 @@ jamovi_plot_mdiff <- function( myplot <- myplot + ggplot2::theme( - axis.text.y = element_text(size = axis.text.y), - axis.title.y = element_text(size = axis.title.y), - axis.text.x = element_text(size = axis.text.x), - axis.title.x = element_text(size = axis.title.x) + axis.text.y = ggtext::element_markdown(size = axis.text.y), + axis.title.y = ggtext::element_markdown(size = axis.title.y), + axis.text.x = ggtext::element_markdown(size = axis.text.x), + axis.title.x = ggtext::element_markdown(size = axis.title.x) ) diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 5c67bca..924b9c8 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -2,7 +2,7 @@ pandoc: 3.1.1 pkgdown: 2.0.7 pkgdown_sha: ~ articles: {} -last_built: 2024-03-08T19:22Z +last_built: 2024-03-11T02:09Z urls: reference: https://rcalinjageman.github.io/esci/reference article: https://rcalinjageman.github.io/esci/articles diff --git a/docs/reference/estimate_mdiff_paired.html b/docs/reference/estimate_mdiff_paired.html index 780e5fa..675607d 100644 --- a/docs/reference/estimate_mdiff_paired.html +++ b/docs/reference/estimate_mdiff_paired.html @@ -317,14 +317,14 @@

Examples#> 1 Posttest Pretest Posttest / Pretest 1.143885 #> LL UL comparison_mean reference_mean #> 1 1.05031 1.245797 13.25 11.58333 -#> [1] "\n For more information on this effect size, see Bonett & Price (2020) doi: 10.3102/1076998620934125." +#> [1] "This effect-size measure is appropriate only for true ratio scales." #> #> -- es_median_ratio -- #> comparison_measure_name reference_measure_name effect effect_size #> 1 Posttest Pretest Posttest / Pretest 1.125 #> LL UL comparison_median reference_median #> 1 0.8723319 1.450853 13.5 12 -#> [1] "\n For more information on this effect size, see Bonett & Price (2020) doi: 10.3102/1076998620934125." +#> [1] "This effect-size measure is appropriate only for true ratio scales." #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. diff --git a/docs/reference/estimate_mdiff_two.html b/docs/reference/estimate_mdiff_two.html index 8a5c904..babb1b9 100644 --- a/docs/reference/estimate_mdiff_two.html +++ b/docs/reference/estimate_mdiff_two.html @@ -268,8 +268,8 @@

Examples) if (FALSE) { -# To visualize the estimated median difference (raw data only) -plot_mdiff(estimate, effect_size = "median") +# To visualize the estimated mean difference +plot_mdiff(estimate, effect_size = "mean") } # From raw data @@ -284,8 +284,8 @@

Examples ) if (FALSE) { -# To visualize the estimated mean difference -plot_mdiff(estimate, effect_size = "mean") +# To visualize the estimated median difference (raw data only) +plot_mdiff(estimate, effect_size = "median") } diff --git a/docs/reference/plot_mdiff.html b/docs/reference/plot_mdiff.html index 24e523d..2b12f72 100644 --- a/docs/reference/plot_mdiff.html +++ b/docs/reference/plot_mdiff.html @@ -227,8 +227,8 @@

Examples) if (FALSE) { -# To visualize the estimated median difference (raw data only) -plot_mdiff(estimate, effect_size = "median") +# To visualize the estimated mean difference +plot_mdiff(estimate, effect_size = "mean") } # From raw data @@ -243,8 +243,8 @@

Examples ) if (FALSE) { -# To visualize the estimated mean difference -plot_mdiff(estimate, effect_size = "mean") +# To visualize the estimated median difference (raw data only) +plot_mdiff(estimate, effect_size = "median") } diff --git a/docs/search.json b/docs/search.json index 8337bbc..6afe865 100644 --- a/docs/search.json +++ b/docs/search.json @@ -1 +1 @@ -[{"path":"https://rcalinjageman.github.io/esci/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Robert J Calin-Jageman. Maintainer.","code":""},{"path":"https://rcalinjageman.github.io/esci/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Calin-Jageman RJ (2024). esci: Estimation Statistics Confidence Intervals. R package version 1.02.","code":"@Manual{, title = {esci: Estimation Statistics with Confidence Intervals}, author = {Robert J Calin-Jageman}, year = {2024}, note = {R package version 1.02}, }"},{"path":"https://rcalinjageman.github.io/esci/index.html","id":"esci-","dir":"","previous_headings":"","what":"Estimation Statistics with Confidence Intervals","title":"Estimation Statistics with Confidence Intervals","text":"esci provides student-friendly tools estimation statistics: effect sizes confidence intervals many research designs meta-analysis visualizations emphasizing effect sizes uncertainty strong hypothesis testing interval nulls esci R package module jamovi. ’re looking R package, stay . want esci jamovi, download install jamovi use module library add esci. Leave comments, bug reports, suggestions, questions esci esci still development; expect breaking changes future especially visualization functions. need production-ready estimation, turn statpsych esci built top statpsych metafor. , almost statistical calculations passed packages. exception confidence intervals Cohen’s d (see documentation). esci exist, ? provide design-based approach; function esci one type research design (e.g. two groups continuous variable); provides effect sizes relevant design one convenient function (e.g. mean difference, cohen’s d, median difference, ratio means, ratio medians). make visualization easier; esci provides visualizations emphasize effect sizes uncertainty integrate GUIs students; esci integrates jamovi integration JASP planned. visualiations produced esci exquisite large part lovely ggdist package Matthew Kay.","code":""},{"path":"https://rcalinjageman.github.io/esci/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Estimation Statistics with Confidence Intervals","text":"Assuming submission CRAN goes well, able install esci : , get stable branch directly github , try development branch:","code":"install.packages(\"esci\") # install.packages(\"devtools\") devtools::install_github('rcalinjageman/esci') # install.packages(\"devtools\") devtools::install_github('rcalinjageman/esci', branch = \"development\")"},{"path":"https://rcalinjageman.github.io/esci/index.html","id":"roadmap","dir":"","previous_headings":"","what":"Roadmap","title":"Estimation Statistics with Confidence Intervals","text":"Finish writing documentation tests Review functions consistency parameter names returned object names Rewrite visualization functions completely remove clunky approaches difference axis issues Complete JASP integration Rewrite jamovi integration Add prediction intervals basic designs Repeated measures 1 IV multiple groups Fully within-subjects 2x2 design Arbitrarily complex designs","code":""},{"path":"https://rcalinjageman.github.io/esci/index.html","id":"example","dir":"","previous_headings":"","what":"Example","title":"Estimation Statistics with Confidence Intervals","text":"","code":"library(esci) data(\"data_penlaptop1\") estimate <- estimate_mdiff_two(data_penlaptop1, transcription, condition) plot_mdiff(estimate)"},{"path":"https://rcalinjageman.github.io/esci/reference/CI_diamond_ratio.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the diamond ratio for a meta-analtyic effect, a measure of\r\nheterogeneity — CI_diamond_ratio","title":"Estimate the diamond ratio for a meta-analtyic effect, a measure of\r\nheterogeneity — CI_diamond_ratio","text":"CI_diamond_ratio returns diamond ratio CI meta-analytic effect, ratio random-effects CI width fixed-effects CI width. diamond ratio measure effect-size heterogeneity.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_diamond_ratio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the diamond ratio for a meta-analtyic effect, a measure of\r\nheterogeneity — CI_diamond_ratio","text":"","code":"CI_diamond_ratio(RE, FE, vi, conf_level = 0.95)"},{"path":"https://rcalinjageman.github.io/esci/reference/CI_diamond_ratio.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Estimate the diamond ratio for a meta-analtyic effect, a measure of\r\nheterogeneity — CI_diamond_ratio","text":"Cairns, Maxwell, Geoff Cumming, Robert Calin‐Jageman, Luke . Prendergast. “Diamond Ratio: Visual Indicator Extent Heterogeneity Meta‐analysis.” British Journal Mathematical Statistical Psychology 75, . 2 (May 2022): 201–19. https://doi.org/10.1111/bmsp.12258.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_diamond_ratio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the diamond ratio for a meta-analtyic effect, a measure of\r\nheterogeneity — CI_diamond_ratio","text":"RE metafor object random effects result FE metafor object fixed effects result vi vector effect size variances conf_level confidence level confidence interval. Given decimal form. Defaults 0.95.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_diamond_ratio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the diamond ratio for a meta-analtyic effect, a measure of\r\nheterogeneity — CI_diamond_ratio","text":"Returns list 3 properties: diamond_ratio LL - lower limit conf_level% CI, Sub-Q approach UL - upper limit conf_level% CI, Sub-Q approach LL_bWT_DL - lower limit conf_level% CI, bWT-DL approach UL_bWT_DL - upper limit conf_level% CI, bWT-DL approach","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_diamond_ratio.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate the diamond ratio for a meta-analtyic effect, a measure of\r\nheterogeneity — CI_diamond_ratio","text":"Calculation CI based code provided Maxwell Cairns (see Cairns et al., 2022). Specifically, function implements Cairns et al (2022) called Sub-Q approach, generaly provides best CI coverage simulations. comparison, function also returns CI produced bWT-DL approach (generally worse performance).","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_diamond_ratio.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the diamond ratio for a meta-analtyic effect, a measure of\r\nheterogeneity — CI_diamond_ratio","text":"","code":"mydata <- esci::data_mccabemichael_brain # Use esci to obtain effect sizes and sample variances, storing only raw_data mydata <- esci::meta_mdiff_two( data = mydata, comparison_means = \"M Brain\", comparison_ns = \"n Brain\", comparison_sds = \"s Brain\", reference_means = \"M No Brain\", reference_ns = \"n No Brain\", reference_sds = \"s No Brain\", random_effects = FALSE )$raw_data # Conduct fixed effects meta-analysis FE <- metafor::rma( data = mydata, yi = effect_size, vi = sample_variance, method=\"FE\" ) # Conduct random effect meta-analysis RE <- metafor::rma( data = mydata, yi = effect_size, vi = sample_variance, method=\"DL\" ) # Get the diamond ratio esci::CI_diamond_ratio( RE = RE, FE = FE, vi = mydata$sample_variance ) #> $diamond_ratio #> [1] 1.399091 #> #> $LL #> [1] 1 #> #> $UL #> [1] 2.667069 #> #> $LL_bWT_DL #> [1] 1 #> #> $UL_bWT_DL #> [1] 3.091891 #>"},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_ind_contrast.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","title":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","text":"CI_smd_ind_contrast returns point estimate confidence interval standardized mean difference (smd aka Cohen's d aka Hedges g). standardized mean difference difference means standardized standard deviation: \\[d = \\frac{ \\psi }{s}\\]","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_ind_contrast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","text":"","code":"CI_smd_ind_contrast( means, sds, ns, contrast, conf_level = 0.95, assume_equal_variance = FALSE, correct_bias = TRUE )"},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_ind_contrast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","text":"means vector 2 means sds vector standard deviations, length means ns vector sample sizes, length means contrast vector group weights, length means conf_level confidence level confidence interval, decimal form. Defaults 0.95. assume_equal_variance Defaults FALSE correct_bias Defaults TRUE; attempts correct slight upward bias d derived sample. 8/9/2023 - Bias correction added 2 groups equal variance assumed, based recent updates statpscyh","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_ind_contrast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","text":"Returns list named elements: effect_size - point estimate sample lower - lower bound CI upper - upper bound CI numerator - numerator Cohen's d_biased; mean difference contrast denominator - denominator Cohen's d_biased; equal variance assumed sd_pooled, otherwise sd_avg df - degrees freedom used correction CI calculation se - standard error estimate; warning totally sure yet moe - margin error; 1/2 length CI d_biased - Cohen's d without correction applied properties - list properties result Properties effect_size_name - equal variance assumed d_s, otherwise d_avg effect_size_name_html - html representation d_name denominator_name - equal variance assumed sd_pooled otherwise sd_avg denominator_name_html - html representation denominator name bias_corrected - TRUE/FALSE bias correction applied message - message explaining denominator correction status message_html - html representation message","code":""},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_ind_contrast.html","id":"it-s-a-bit-complicated","dir":"Reference","previous_headings":"","what":"It's a bit complicated","title":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","text":"standardized mean difference turns complicated. First, many names: standardized mean difference (smd) Cohen's d bias sample d corrected, also called Hedge's g Second, choice standardizer requires thought: sd_pooled - used assuming groups exact variance sd_avg - require assumption equal variance possibilities, , dealt function choice standardizer important, noted subscript: d_s -- assumes equal variance, standardized sd_pooled d_avg - assume equal variance, standardized sd_avg third complication issue bias: d estimated sample slight upward bias smaller sample sizes. total sample size > 30, slight bias becomes fairly neglible (kind like small upward bias sample standard deviation). bias can corrected equal variance assumed design study simple (2 groups). complex designs (>2 groups) without assumption equal variance, now also approximate approach correcting bias Bonett. Corrections bias produce long-run reduction average bias. Corrections bias approximate.","code":""},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_ind_contrast.html","id":"when-equal-variance-is-assumed","dir":"Reference","previous_headings":"","what":"When equal variance is assumed","title":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","text":"equal variance assumed, standardized mean difference d_s, defined Kline, p. 196: \\[ d_s = \\frac{ \\psi }{ sd_{pooled} } \\] psi defined Kline, equation 7.8: \\[ \\psi =\\sum_{=1}^{}c_iM_i \\] sd_pooled defined Kline, equation 3.11 \\[sd_{pooled} = { \\frac{ \\sum_{=1}^{} (n_i -1) s_i^2 } { \\sum_{=1}^{} (n_i-1) } }\\] CI d_s derived lambda-prime transformation Lecoutre, 2007 code adapted Cousineau & Goulet-Pelletier, 2020. Kelley, 2007 explains general approach linear contrasts. approach generating CI 'exact', meaning coverage desired assumptions met (ha!). Correction upward bias can applied.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_ind_contrast.html","id":"when-equal-variance-is-not-assumed","dir":"Reference","previous_headings":"","what":"When equal variance is not assumed","title":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","text":"equal variance assumed, standardized mean difference d_avg, defined Bonett, equation 6: \\[ d_{avg} = \\frac{ \\psi }{ sd_{avg} }\\] sd_avg square root average group variances, given Bonett, explanation equation 6: \\[sd_{avg} = \\sqrt{ \\frac{ \\sum_{=1}^{} s_i^2 }{ } }\\]","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_ind_contrast.html","id":"if-only-groups","dir":"Reference","previous_headings":"","what":"If only 2 groups","title":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","text":"CI derived lambda-prime transformation using df se Huynh, 1989 -- see especially Delacre et al., 2021 also 'exact' approach, correction can applied","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_ind_contrast.html","id":"if-more-than-groups","dir":"Reference","previous_headings":"","what":"If more than 2 groups","title":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","text":"CI approximated using approach Bonett, 2008 approximate correction developed Bonett used","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_ind_contrast.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","text":"Bonett D. G. (2023). statpsych: Statistical Methods Psychologists. R package version 1.4.0. https://dgbonett.github.io/statpsych Bonett, D. G. (2018). R code posted personal website (now removed). Formally https://people.ucsc.edu/~dgbonett/psyc204.html Bonett, D. G. (2008). Confidence Intervals Standardized Linear Contrasts Means. Psychological Methods, 13(2), 99–109. https://doi.org/10.1037/1082-989X.13.2.99 Cousineau & Goulet-Pelletier (2020) https://psyarxiv.com/s2597/ Delacre et al., 2021, https://psyarxiv.com/tu6mp/ Huynh, C.-L. (1989). unified approach estimation effect size meta-analysis. NBER Working Paper Series, 58(58), 99–104. Kelley, K. (2007). Confidence intervals standardized effect sizes: Theory, application, implementation. Journal Statistical Software, 20(8), 1–24. https://doi.org/10.18637/jss.v020.i08 Lecoutre, B. (2007). Another Look Confidence Intervals Noncentral T Distribution. Journal Modern Applied Statistical Methods, 6(1), 107–116. https://doi.org/10.22237/jmasm/1177992600","code":""},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_ind_contrast.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","text":"","code":"# Example from Kline, 2013 # Data in Table 3.4 # Worked out in Chapter 7 # See p. 202, non-central approach # With equal variance assumed and no correction, should give: # d_s = -0.8528028 [-2.121155, 0.4482578] CI_smd_ind_contrast( means = c(13, 11, 15), sds = c(2.738613, 2.236068, 2.000000), ns = c(5, 5, 5), contrast = contrast <- c(1, 0, -1), conf_level = 0.95, assume_equal_variance = TRUE, correct_bias = FALSE ) #> $effect_size #> [1] -0.8528028 #> #> $LL #> [1] -2.121155 #> #> $UL #> [1] 0.4482578 #> #> $numerator #> [1] -2 #> #> $denominator #> [1] 2.345208 #> #> $SE #> [1] 0.6554747 #> #> $df #> [1] 12 #> #> $d_biased #> [1] -0.8528028 #> #> $properties #> $properties$effect_size_name #> [1] \"d_s\" #> #> $properties$effect_size_name_html #> [1] \"d<\/i>s.biased<\/sub>\" #> #> $properties$denominator_name #> [1] \"s_p\" #> #> $properties$denominator_name_html #> [1] \"s<\/i>p<\/sub>\" #> #> $properties$bias_corrected #> [1] FALSE #> #> $properties$effect_size_category #> [1] \"difference\" #> #> $properties$effect_size_precision #> [1] \"magnitude\" #> #> $properties$conf_level #> [1] 0.95 #> #> $properties$assume_equal_variance #> [1] TRUE #> #> $properties$error_distribution #> [1] \"t_dist\" #> #> $properties$message #> This standardized mean difference is called d_s because the standardizer used was s_p. d_s has *not* been corrected for bias. Correction for bias can be important when df < 50. #> #> $properties$message_html #> This standardized mean difference is called d<\/i>s.biased<\/sub> #> because the standardizer used was s<\/i>p<\/sub>.
#> d<\/i>s.biased<\/sub> has *not* #> been corrected for bias. #> Correction for bias can be important when df<\/i> < 50.
#> #> # Example from [statpsych::ci.lc.stdmean.bs()] should give: # Estimate SE LL UL # Unweighted standardizer: -1.273964 0.3692800 -2.025039 -0.5774878 # Weighted standardizer: -1.273964 0.3514511 -1.990095 -0.6124317 # Group 1 standardizer: -1.273810 0.4849842 -2.343781 -0.4426775 CI_smd_ind_contrast( means = c(33.5, 37.9, 38.0, 44.1), sds = c(3.84, 3.84, 3.65, 4.98), ns = c(10,10,10,10), contrast = c(.5, .5, -.5, -.5), conf_level = 0.95, assume_equal_variance = FALSE, correct_bias = TRUE ) #> $effect_size #> [1] -1.273964 #> #> $LL #> [1] -2.025039 #> #> $UL #> [1] -0.5774878 #> #> $numerator #> [1] -5.35 #> #> $denominator #> [1] 4.11139 #> #> $SE #> [1] 0.36928 #> #> $df #> [1] 36 #> #> $d_biased #> [1] -1.301263 #> #> $properties #> $properties$effect_size_name #> [1] \"d_avg\" #> #> $properties$effect_size_name_html #> [1] \"d<\/i>avg<\/sub>\" #> #> $properties$denominator_name #> [1] \"s_avg\" #> #> $properties$denominator_name_html #> [1] \"s<\/i>avg<\/sub>\" #> #> $properties$bias_corrected #> [1] TRUE #> #> $properties$effect_size_category #> [1] \"difference\" #> #> $properties$effect_size_precision #> [1] \"magnitude\" #> #> $properties$conf_level #> [1] 0.95 #> #> $properties$assume_equal_variance #> [1] FALSE #> #> $properties$error_distribution #> [1] \"t_dist\" #> #> $properties$message #> This standardized mean difference is called d_avg because the standardizer used was s_avg. d_avg has been corrected for bias. Correction for bias can be important when df < 50. See the rightmost column for the biased value. #> #> $properties$message_html #> This standardized mean difference is called d<\/i>avg<\/sub> #> because the standardizer used was s<\/i>avg<\/sub>.
#> d<\/i>avg<\/sub> has #> been corrected for bias. #> Correction for bias can be important when df<\/i> < 50. See the rightmost column for the biased value.
#> #>"},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_one.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate standardized mean difference (Cohen's d1) for a single group — CI_smd_one","title":"Estimate standardized mean difference (Cohen's d1) for a single group — CI_smd_one","text":"CI_smd_one STILL NEEDS WORK VERIFY APPROACH SE MoE","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_one.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate standardized mean difference (Cohen's d1) for a single group — CI_smd_one","text":"","code":"CI_smd_one(mean, sd, n, reference_mean, conf_level = 0.95, correct_bias = TRUE)"},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_one.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate standardized mean difference (Cohen's d1) for a single group — CI_smd_one","text":"mean Mean single group outcome measure sd Standard deviation, > 0 n Sample size, integer > 2 reference_mean defaults 0 conf_level confidence level confidence interval, decimal form. Defaults 0.95. correct_bias Defaults TRUE","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_one.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate standardized mean difference (Cohen's d1) for a single group — CI_smd_one","text":"Returns list named elements: effect_size - point estimate sample lower - lower bound CI upper - upper bound CI numerator - numerator Cohen's d_biased; mean difference contrast denominator - denominator Cohen's d_biased; equal variance assumed sd_pooled, otherwise sd_avg df - degrees freedom used correction CI calculation se - standard error estimate; warning totally sure yet moe - margin error; 1/2 length CI d_biased - Cohen's d without correction applied properties - list properties result Properties effect_size_name - equal variance assumed d_s, otherwise d_avg effect_size_name_html - html representation d_name denominator_name - equal variance assumed sd_pooled otherwise sd_avg denominator_name_html - html representation denominator name bias_corrected - TRUE/FALSE bias correction applied message - message explaining denominator correction status message_html - html representation message","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_one.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate standardized mean difference (Cohen's d1) for a single group — CI_smd_one","text":"","code":"# example code esci::CI_smd_one(24.5, 3.65, 40, 20) #> $effect_size #> [1] 1.208989 #> #> $LL #> [1] 0.7951381 #> #> $UL #> [1] 1.613746 #> #> $numerator #> [1] 4.5 #> #> $denominator #> [1] 3.65 #> #> $SE #> [1] 0.2088481 #> #> $df #> [1] 39 #> #> $d_biased #> [1] 1.232877 #> #> $properties #> $properties$effect_size_name #> [1] \"d_1\" #> #> $properties$effect_size_name_html #> [1] \"d<\/i>1<\/sub>\" #> #> $properties$denominator_name #> [1] \"s\" #> #> $properties$denominator_name_html #> [1] \"s<\/i>\" #> #> $properties$bias_corrected #> [1] TRUE #> #> $properties$effect_size_category #> [1] \"difference\" #> #> $properties$effect_size_precision #> [1] \"magnitude\" #> #> $properties$conf_level #> [1] 0.95 #> #> $properties$message #> This standardized mean difference is called d_1 because the standardizer used was s. d_1 has been corrected for bias. Correction for bias can be important when df < 50. See the rightmost column for the biased value. #> #> $properties$message_html #> This standardized mean difference is called d<\/i>1<\/sub> #> because the standardizer used was s<\/i>.
#> d<\/i>1<\/sub> has #> been corrected for bias. #> Correction for bias can be important when df<\/i> < 50. See the rightmost column for the biased value.
#> #>"},{"path":"https://rcalinjageman.github.io/esci/reference/data_altruism_happiness.html","id":null,"dir":"Reference","previous_headings":"","what":"Altruism Happiness - Ch12 - from Brethel-Haurwitz and Marsh (2014) — data_altruism_happiness","title":"Altruism Happiness - Ch12 - from Brethel-Haurwitz and Marsh (2014) — data_altruism_happiness","text":"Happiness may important just person feeling ; happiness may also promote kind, altruistic behavior. Brethel-Haurwitz Marsh (2014) examined idea collecting data U.S. states. Gallup poll 2010 used measure state's well-index, measure mean happiness state's residents scale 0 100. Next, kidney donation database 1999-2010 used figure state's rate (number donations per 1 million people) non-directed kidney donations-giving one kidney stranger, extremely generous altruistic thing !","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_altruism_happiness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Altruism Happiness - Ch12 - from Brethel-Haurwitz and Marsh (2014) — data_altruism_happiness","text":"","code":"data_altruism_happiness"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_altruism_happiness.html","id":"data-altruism-happiness","dir":"Reference","previous_headings":"","what":"data_altruism_happiness","title":"Altruism Happiness - Ch12 - from Brethel-Haurwitz and Marsh (2014) — data_altruism_happiness","text":"data frame 50 rows 6 columns: State factor - State data collected Abbreviation factor - State data collected Well_Being_2010 numeric - State data collected Well_Being_2013 numeric - State data collected Kidney_Rate, per million population numeric - State data collected WB Change 2013-2010 numeric - State data collected","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_altruism_happiness.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Altruism Happiness - Ch12 - from Brethel-Haurwitz and Marsh (2014) — data_altruism_happiness","text":"https://journals.sagepub.com/doi/full/10.1177/0956797613516148","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_anchor_estimate_ma.html","id":null,"dir":"Reference","previous_headings":"","what":"Anchor Estimate ma - Ch9 - Many Labs replications of Jacowitz and Kahneman (1995) — data_anchor_estimate_ma","title":"Anchor Estimate ma - Ch9 - Many Labs replications of Jacowitz and Kahneman (1995) — data_anchor_estimate_ma","text":"extent wording question influence one's judgment? classic study, Jacowitz Kahneman (1995) asked participants estimate many babies born day United States. Participants given either low anchor (100 babies/day) high anchor (less 50,000 babies/day). saw low anchor estimated many fewer births/day saw high anchor, suggests wording can profound influence. correct answer, happens, ~11,000 births/day 2014. investigate extent results replicable, Many Labs project repeated classic study many different labs around world. can find summary data 30 labs Anchor Estimate ma data file","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_anchor_estimate_ma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Anchor Estimate ma - Ch9 - Many Labs replications of Jacowitz and Kahneman (1995) — data_anchor_estimate_ma","text":"","code":"data_anchor_estimate_ma"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_anchor_estimate_ma.html","id":"data-anchor-estimate-ma","dir":"Reference","previous_headings":"","what":"data_anchor_estimate_ma","title":"Anchor Estimate ma - Ch9 - Many Labs replications of Jacowitz and Kahneman (1995) — data_anchor_estimate_ma","text":"data frame 30 rows 9 columns: Location factor M Low numeric s Low numeric n Low integer M High numeric s High numeric n High integer USAorNot factor Country factor","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_anchor_estimate_ma.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Anchor Estimate ma - Ch9 - Many Labs replications of Jacowitz and Kahneman (1995) — data_anchor_estimate_ma","text":"https://econtent.hogrefe.com/doi/10.1027/1864-9335/a000178","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_basol_badnews.html","id":null,"dir":"Reference","previous_headings":"","what":"Basol badnews - Ch07 - from Basol et al. (2020) — data_basol_badnews","title":"Basol badnews - Ch07 - from Basol et al. (2020) — data_basol_badnews","text":"Climate change? Vaccines? Fake news conspiracy theories numerous issues can highly damaging, thriving social media age. Trying debunk conspiracy theory presenting facts evidence often work, alas. Psychological inoculation, also similar prebunking, presents mild form misinformation, preferably explanation, hope building resistance real-life fake news-sort vaccine fake news. Bad News game spin-research psychological inoculation. Basol et al. (2020) assessed possible effectiveness game fake news vaccine. getbadnews.com can click '' information, just start playing game- easy maybe even fun. encounter mock Twitter (now X) fake news messages illustrate common strategies making fake news memorable believable. make choices messages decide ones 'forward' try spread fake news building credibility score number 'followers'-rather like real life conspiracy theorist wanting spread word. Compete friends credibility number followers. Basol's online participants first saw 18 fictitious fake news tweets rated reliability (accuracy, believability), also rated confidence reliability rating. ratings 1 7 scale. BadNews group played game 15 minutes, whereas Control group played Tetris. gave reliability confidence ratings 18 tweets.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_basol_badnews.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Basol badnews - Ch07 - from Basol et al. (2020) — data_basol_badnews","text":"","code":"data_basol_badnews"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_basol_badnews.html","id":"data-basol-badnews","dir":"Reference","previous_headings":"","what":"data_basol_badnews","title":"Basol badnews - Ch07 - from Basol et al. (2020) — data_basol_badnews","text":"data frame 198 rows 3 columns: Diff reliability numeric Diff confidence numeric Condition factor","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_basol_badnews.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Basol badnews - Ch07 - from Basol et al. (2020) — data_basol_badnews","text":"https://doi.org/10.5334%2Fjoc.91","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bem_and_honorton_1994.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample data for estimate_proportion: Ten Ganzfeld studies from Bem and\r\nHonorton (1994) — data_bem_and_honorton_1994","title":"Sample data for estimate_proportion: Ten Ganzfeld studies from Bem and\r\nHonorton (1994) — data_bem_and_honorton_1994","text":"Three pilot studies helped refine procedures, four studies used novice receivers. Study 5 used 20 students music, drama, dance receivers, response suggestions creative people might likely show telepathy. Studies 6 7 used receivers participated earlier study. proportion hits expected chance .25","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bem_and_honorton_1994.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample data for estimate_proportion: Ten Ganzfeld studies from Bem and\r\nHonorton (1994) — data_bem_and_honorton_1994","text":"","code":"data_bem_and_honorton_1994"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_bem_and_honorton_1994.html","id":"data-bem-and-honorton-a-data-frame-with-rows-and-","dir":"Reference","previous_headings":"","what":"data_bem_and_honorton_1994 A data frame with 10 rows and 3","title":"Sample data for estimate_proportion: Ten Ganzfeld studies from Bem and\r\nHonorton (1994) — data_bem_and_honorton_1994","text":"columns: study Name study hits Number correct responses trials Number trials","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bem_and_honorton_1994.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Sample data for estimate_proportion: Ten Ganzfeld studies from Bem and\r\nHonorton (1994) — data_bem_and_honorton_1994","text":"Bem, D. J., & Honorton, C. (1994). Psi exist? Replicable evidence anomalous process information transfer. Psychological Bulletin, 115, 4–-18. doi:10.1037/0033-2909.115.1.4","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bem_psychic.html","id":null,"dir":"Reference","previous_headings":"","what":"Bem Psychic - Ch13 - from Bem and Honorton (1994) — data_bem_psychic","title":"Bem Psychic - Ch13 - from Bem and Honorton (1994) — data_bem_psychic","text":"Daryl Bem experienced mentalist research psychologist, , decade earlier, one several outside experts invited scrutinize laboratory experimental procedures parapsychology researcher Charles Honorton. Bem judged adequate, joined research effort became coauthor Honorton. Bem Honorton (1994) first reviewed early ganzfeld studies described experimental procedure improved reduce chance results influenced various possible biases, leakages information sender receiver. example, randomization procedure carried automatically computer, stimuli presented computer control. Bem Honorton presented data studies conducted improved procedure. Table 13.1 presents basic data 10 studies reported Bem Honorton (1994). Participants made single judgment, Pilot 1, example, 22 participants responded, 8 giving correct response. Three pilot studies helped refine procedures, four studies used novice receivers. Study 5 used 20 students music, drama, dance receivers, response suggestions creative people might likely show telepathy. Studies 6 7 used receivers participated earlier study. proportion hits expected chance .25, Table 13.1 shows Study 1 found proportions higher .25.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bem_psychic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bem Psychic - Ch13 - from Bem and Honorton (1994) — data_bem_psychic","text":"","code":"data_bem_psychic"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_bem_psychic.html","id":"data-bem-psychic","dir":"Reference","previous_headings":"","what":"data_bem_psychic","title":"Bem Psychic - Ch13 - from Bem and Honorton (1994) — data_bem_psychic","text":"data frame 10 rows 5 columns: Study factor Participants factor N(Trials) integer N(Hits) integer Proportion Hits numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bem_psychic.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Bem Psychic - Ch13 - from Bem and Honorton (1994) — data_bem_psychic","text":"https://psycnet.apa.org/record/1994-20286-001","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bodywellf.html","id":null,"dir":"Reference","previous_headings":"","what":"BodyWellF - Ch12 - Body Satisfaction and Well-being data for females from Figure 11.24 right panel — data_bodywellf","title":"BodyWellF - Ch12 - Body Satisfaction and Well-being data for females from Figure 11.24 right panel — data_bodywellf","text":"subset data_bodywell_fm, reports participants identified female. Data Subjective Wellbeing abd Body Satisfaction.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bodywellf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BodyWellF - Ch12 - Body Satisfaction and Well-being data for females from Figure 11.24 right panel — data_bodywellf","text":"","code":"data_bodywellf"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_bodywellf.html","id":"data-bodywellf","dir":"Reference","previous_headings":"","what":"data_bodywellf","title":"BodyWellF - Ch12 - Body Satisfaction and Well-being data for females from Figure 11.24 right panel — data_bodywellf","text":"data frame 59 rows 2 columns: Body Satisfaction numeric Well-numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bodywellfm.html","id":null,"dir":"Reference","previous_headings":"","what":"BodyWellFM - Ch12 - Body Satisfaction and Well-being data from Figure 11.1 — data_bodywellfm","title":"BodyWellFM - Ch12 - Body Satisfaction and Well-being data from Figure 11.1 — data_bodywellfm","text":"Survey data convenience sample Dominican University students. Reported measures Subjective Wellbeing abd Body Satisfaction.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bodywellfm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BodyWellFM - Ch12 - Body Satisfaction and Well-being data from Figure 11.1 — data_bodywellfm","text":"","code":"data_bodywellfm"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_bodywellfm.html","id":"data-bodywellfm","dir":"Reference","previous_headings":"","what":"data_bodywellfm","title":"BodyWellFM - Ch12 - Body Satisfaction and Well-being data from Figure 11.1 — data_bodywellfm","text":"data frame 106 rows 2 columns: Body Satisfaction numeric Well-numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bodywellm.html","id":null,"dir":"Reference","previous_headings":"","what":"BodyWellM - Ch12 - Body Satisfaction and Well-being data for males from Figure 11.24 left panel — data_bodywellm","title":"BodyWellM - Ch12 - Body Satisfaction and Well-being data for males from Figure 11.24 left panel — data_bodywellm","text":"subset data_bodywell_fm, reports participants identified male. Data Subjective Wellbeing abd Body Satisfaction.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bodywellm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BodyWellM - Ch12 - Body Satisfaction and Well-being data for males from Figure 11.24 left panel — data_bodywellm","text":"","code":"data_bodywellm"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_bodywellm.html","id":"data-bodywellm","dir":"Reference","previous_headings":"","what":"data_bodywellm","title":"BodyWellM - Ch12 - Body Satisfaction and Well-being data for males from Figure 11.24 left panel — data_bodywellm","text":"data frame 47 rows 2 columns: Body Satisfaction numeric Well-numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bushman_2005.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample data for estimate_mdiff_ind_contrast: Bushman (2005) — data_bushman_2005","title":"Sample data for estimate_mdiff_ind_contrast: Bushman (2005) — data_bushman_2005","text":"Bushman (2005) reported investigation people’s memory ads presented different types television show. wanted estimate extent violent content, compared neutral content, might lead reduced memory ads, perhaps reduced purchasing intentions advertised products. investigated questions shows sexual content. chose three- group design efficient way investigate types content. sample 252 typical TV viewers randomly assigned watch one three types show. watched Neutral show (e.g., America’s Funniest Animals), show Violent content (e.g., Cops), others show Sexual content (e.g., Sex City). viewers watched different shows, saw 12 ads inserted shows. ads genuine advertisements, little-known products, viewers never seen ads . enhance realism, viewers watched shows easy chairs, snacks soda available. viewing came surprise memory test ads (participants told purpose study). ’ll report data memory recognition: Participants saw list 12 products, four 4 brand names type product, just one appeared ad. set four 4 brands, participants choose one felt recognized ads just seen, maximum score 12. summary data set, providing sample sizes, means, standard deviations group study.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bushman_2005.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample data for estimate_mdiff_ind_contrast: Bushman (2005) — data_bushman_2005","text":"","code":"data_bushman_2005"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_bushman_2005.html","id":"data-bushman-a-data-frame-with-rows-and-columns-","dir":"Reference","previous_headings":"","what":"data_bushman_2005 A data frame with 3 rows and 4 columns:","title":"Sample data for estimate_mdiff_ind_contrast: Bushman (2005) — data_bushman_2005","text":"condition Factor indicating group: Neutral, Violent, Sexual n Group sample sizes m Group means s Group standard deviation","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bushman_2005.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Sample data for estimate_mdiff_ind_contrast: Bushman (2005) — data_bushman_2005","text":"Bushman, B. (2005). Violence sex television programs sell products advertisements. Psychological Science, 16, 702-–708. doi:10.1111/j.1467-9280.2005.01599.x","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_campus_involvement.html","id":null,"dir":"Reference","previous_headings":"","what":"Campus Involvement - Ch11 - for End-of-Chapter Exercise 11.7 — data_campus_involvement","title":"Campus Involvement - Ch11 - for End-of-Chapter Exercise 11.7 — data_campus_involvement","text":"Clinton conducted survey college students determine extent subjective well-related campus involvement (Campus Involvement data set book website). Participants completed measure subjective well-(scale 1 5) measure campus involvement (scale 1 5). Participants also reported gender (male female) commuter status (resident commuter). Synthetic data simulated mimic survey data class project.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_campus_involvement.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Campus Involvement - Ch11 - for End-of-Chapter Exercise 11.7 — data_campus_involvement","text":"","code":"data_campus_involvement"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_campus_involvement.html","id":"data-campus-involvement","dir":"Reference","previous_headings":"","what":"data_campus_involvement","title":"Campus Involvement - Ch11 - for End-of-Chapter Exercise 11.7 — data_campus_involvement","text":"data frame 113 rows 6 columns: ID integer Gender factor GPA numeric CommuterStatus factor SWB numeric Campus Involvement numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_chap_8_paired_ex_8.18.html","id":null,"dir":"Reference","previous_headings":"","what":"Fictituous data from an unrealistically small HEAT\r\nstudy comparing scores for a single group of students before and after a\r\nworkshop on climate change. — data_chap_8_paired_ex_8.18","title":"Fictituous data from an unrealistically small HEAT\r\nstudy comparing scores for a single group of students before and after a\r\nworkshop on climate change. — data_chap_8_paired_ex_8.18","text":"Fictituous data unrealistically small HEAT study comparing scores single group students workshop climate change.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_chap_8_paired_ex_8.18.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fictituous data from an unrealistically small HEAT\r\nstudy comparing scores for a single group of students before and after a\r\nworkshop on climate change. — data_chap_8_paired_ex_8.18","text":"","code":"data_chap_8_paired_ex_8.18"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_chap_8_paired_ex_8.18.html","id":"data-chap-paired-ex-","dir":"Reference","previous_headings":"","what":"data_chap_8_paired_ex_8.18","title":"Fictituous data from an unrealistically small HEAT\r\nstudy comparing scores for a single group of students before and after a\r\nworkshop on climate change. — data_chap_8_paired_ex_8.18","text":"data frame 8 rows 2 columns: numeric numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_clean_moral.html","id":null,"dir":"Reference","previous_headings":"","what":"Clean moral - Ch07 - from Schnall et al. (2008), Study 1, and Johnson et al. (2014) — data_clean_moral","title":"Clean moral - Ch07 - from Schnall et al. (2008), Study 1, and Johnson et al. (2014) — data_clean_moral","text":"researchers claim moral judgments based rational considerations also one's current emotional state. extent can recent emotional experiences influence moral judgments? Schnall et al. (2008) examined question manipulating feelings cleanliness purity observing extent changes harshly participants judge morality others. Inscho Study 1, Schnall et al. asked participants complete word scramble task either neutral words (neutral prime) words related cleanliness (cleanliness prime). students completed set moral judgments controversial scenarios: Moral judgment average six items, rated scale 0 9, high meaning harsh. data study Clean moral file, also contains data replication Johnson et al. (2014)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_clean_moral.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clean moral - Ch07 - from Schnall et al. (2008), Study 1, and Johnson et al. (2014) — data_clean_moral","text":"","code":"data_clean_moral"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_clean_moral.html","id":"data-clean-moral","dir":"Reference","previous_headings":"","what":"data_clean_moral","title":"Clean moral - Ch07 - from Schnall et al. (2008), Study 1, and Johnson et al. (2014) — data_clean_moral","text":"data frame 208 rows 4 columns: Schnall Condition factor Schnall Moral judgment numeric Johnson Condition factor Johnson Moral judgment numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_clean_moral.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Clean moral - Ch07 - from Schnall et al. (2008), Study 1, and Johnson et al. (2014) — data_clean_moral","text":"https://econtent.hogrefe.com/doi/full/10.1027/1864-9335/a000186","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_college_survey_1.html","id":null,"dir":"Reference","previous_headings":"","what":"College survey 1 - Ch03 - for End-of-Chapter Exercise 3.3 — data_college_survey_1","title":"College survey 1 - Ch03 - for End-of-Chapter Exercise 3.3 — data_college_survey_1","text":"Data additional survey Dominican University students; reports various psychological behavioral measures.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_college_survey_1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"College survey 1 - Ch03 - for End-of-Chapter Exercise 3.3 — data_college_survey_1","text":"","code":"data_college_survey_1"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_college_survey_1.html","id":"data-college-survey-","dir":"Reference","previous_headings":"","what":"data_college_survey_1","title":"College survey 1 - Ch03 - for End-of-Chapter Exercise 3.3 — data_college_survey_1","text":"data frame 243 rows 23 columns: ID integer Gender factor Gender_Code factor Age integer Shool_Year factor School_Year_Code factor Transfer factor Transfer_Code logical Student_Athlete factor Student_Athlete_Code logical Wealth_SR numeric GPA numeric ACT integer Subjective_Well_Being numeric Positive_Affect numeric Negative_Affect numeric Relationship_Confidence numeric Exercise numeric Academic_Motivation_Intrinsic numeric Academic_Motivation_Extrinsic numeric Academic_Motivation_Amotivation numeric Intelligence_Value numeric Raven_Score numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_college_survey_2.html","id":null,"dir":"Reference","previous_headings":"","what":"College survey 2 - Ch05 - for End-of-Chapter Exercise 5.4 — data_college_survey_2","title":"College survey 2 - Ch05 - for End-of-Chapter Exercise 5.4 — data_college_survey_2","text":"College survey 2 - Ch05 - End--Chapter Exercise 5.4","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_college_survey_2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"College survey 2 - Ch05 - for End-of-Chapter Exercise 5.4 — data_college_survey_2","text":"","code":"data_college_survey_2"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_college_survey_2.html","id":"data-college-survey-","dir":"Reference","previous_headings":"","what":"data_college_survey_2","title":"College survey 2 - Ch05 - for End-of-Chapter Exercise 5.4 — data_college_survey_2","text":"data frame 138 rows 17 columns: ID integer Gender factor Gender_Code factor Age numeric Wealth_SR numeric School_Year factor School_Year_Code factor Transfer factor Transfer_Code logical GPA numeric Subjective_Well_Being numeric Positive_Affect numeric Negative_Affect numeric Academic_Engagement numeric Religious_Meaning numeric Health numeric Emotion_Recognition factor","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_damischrcj.html","id":null,"dir":"Reference","previous_headings":"","what":"DamischRCJ - Ch9 - from 6 Damisch studies, and Calin-Jageman and Caldwell (2014) — data_damischrcj","title":"DamischRCJ - Ch9 - from 6 Damisch studies, and Calin-Jageman and Caldwell (2014) — data_damischrcj","text":"DamischRCJ - Ch9 - 6 Damisch studies, Calin-Jageman Caldwell (2014)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_damischrcj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DamischRCJ - Ch9 - from 6 Damisch studies, and Calin-Jageman and Caldwell (2014) — data_damischrcj","text":"","code":"data_damischrcj"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_damischrcj.html","id":"data-damischrcj","dir":"Reference","previous_headings":"","what":"data_damischrcj","title":"DamischRCJ - Ch9 - from 6 Damisch studies, and Calin-Jageman and Caldwell (2014) — data_damischrcj","text":"data frame 8 rows 5 columns: Study factor Cohen's d unbiased numeric n Control integer n Lucky integer Research Group factor","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_effronraj_fakenews.html","id":null,"dir":"Reference","previous_headings":"","what":"EffronRaj fakenews - Ch8 - from Effron and Raj (2020) — data_effronraj_fakenews","title":"EffronRaj fakenews - Ch8 - from Effron and Raj (2020) — data_effronraj_fakenews","text":"Synthetic data meant represent Experiment 1 Effron & Raj, 2020. 138 U.S. adults, recruited August 2018 Prolific Academic, worked online. First, saw six fake headlines four times, time asked rate interesting/engaging/ funny/well-written headline . rating task simply ensured participants paid attention headline. stimuli 12 actual fake-news headlines American politics, accompanying photographs. Half appealed Republicans half Democrats. Later, 12 fake headlines presented one time, random mix six Old headlines-seen -six New headlines seen previously. stated clearly independent, non-partisan fact-checking established headlines true. Participants first rated, 0 () 100 (extremely) scale, degree judged unethical publish headline. Unethicality DV. also rated likely share headline saw posted acquaintance social media; three similar ratings. Finally, rated accurate believed headline .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_effronraj_fakenews.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"EffronRaj fakenews - Ch8 - from Effron and Raj (2020) — data_effronraj_fakenews","text":"","code":"data_effronraj_fakenews"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_effronraj_fakenews.html","id":"data-effronraj-fakenews","dir":"Reference","previous_headings":"","what":"data_effronraj_fakenews","title":"EffronRaj fakenews - Ch8 - from Effron and Raj (2020) — data_effronraj_fakenews","text":"data frame 138 rows 5 columns: ID factor UnethOld numeric UnethNew numeric AccurOld numeric AccurNew numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_effronraj_fakenews.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"EffronRaj fakenews - Ch8 - from Effron and Raj (2020) — data_effronraj_fakenews","text":"https://journals.sagepub.com/doi/full/10.1177/0956797619887896","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_emotion_heartrate.html","id":null,"dir":"Reference","previous_headings":"","what":"Emotion heartrate - Ch8 - from Lakens (2013) — data_emotion_heartrate","title":"Emotion heartrate - Ch8 - from Lakens (2013) — data_emotion_heartrate","text":"Anger powerful emotion. extent can feeling angry actually change heart rate? investigate, Lakens (2013) asked students record heart rate (beats per minute) rest (baseline) recalling time intense anger. conceptual replication classic study Ekman et al. (1983). Load Emotion heartrate data set book website.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_emotion_heartrate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Emotion heartrate - Ch8 - from Lakens (2013) — data_emotion_heartrate","text":"","code":"data_emotion_heartrate"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_emotion_heartrate.html","id":"data-emotion-heartrate","dir":"Reference","previous_headings":"","what":"data_emotion_heartrate","title":"Emotion heartrate - Ch8 - from Lakens (2013) — data_emotion_heartrate","text":"data frame 68 rows 3 columns: ID integer HR_baseline numeric HR_anger numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_emotion_heartrate.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Emotion heartrate - Ch8 - from Lakens (2013) — data_emotion_heartrate","text":"https://ieeexplore.ieee.org/abstract/document/6464255","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_exam_scores.html","id":null,"dir":"Reference","previous_headings":"","what":"Exam Scores - Ch11 - for End-of-Chapter Exercise 11.2 — data_exam_scores","title":"Exam Scores - Ch11 - for End-of-Chapter Exercise 11.2 — data_exam_scores","text":"extent initial performance class relate performance final exam? First exam final exam scores nine students enrolled introductory psychology course. Exam scores percentages, 0 = answers correct 100 = answers correct. Data synthetic represent patterns found previous psych stats course.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_exam_scores.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exam Scores - Ch11 - for End-of-Chapter Exercise 11.2 — data_exam_scores","text":"","code":"data_exam_scores"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_exam_scores.html","id":"data-exam-scores","dir":"Reference","previous_headings":"","what":"data_exam_scores","title":"Exam Scores - Ch11 - for End-of-Chapter Exercise 11.2 — data_exam_scores","text":"data frame 9 rows 3 columns: StudentID factor First Exam numeric Final Exam numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_flag_priming_ma.html","id":null,"dir":"Reference","previous_headings":"","what":"Flag Priming ma - Ch9 - Many Labs replications of Carter et al. (2011) — data_flag_priming_ma","title":"Flag Priming ma - Ch9 - Many Labs replications of Carter et al. (2011) — data_flag_priming_ma","text":"extent exposed American flag influence political attitudes? One seminal study (Carter et al., 2011) explored issue subtly exposing participants either images American flag control images. Next, participants asked political attitudes, using 1-7 rating scale high scores indicate conservative attitudes. Participants exposed flag found express substantially conservative attitudes. Many Labs project replicated finding 25 different locations United States.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_flag_priming_ma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flag Priming ma - Ch9 - Many Labs replications of Carter et al. (2011) — data_flag_priming_ma","text":"","code":"data_flag_priming_ma"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_flag_priming_ma.html","id":"data-flag-priming-ma","dir":"Reference","previous_headings":"","what":"data_flag_priming_ma","title":"Flag Priming ma - Ch9 - Many Labs replications of Carter et al. (2011) — data_flag_priming_ma","text":"data frame 25 rows 7 columns: Location factor M Flag numeric s Flag numeric n Flag integer M Noflag numeric s Noflag numeric n Noflag integer","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_flag_priming_ma.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Flag Priming ma - Ch9 - Many Labs replications of Carter et al. (2011) — data_flag_priming_ma","text":"https://openpsychologydata.metajnl.com/articles/10.5334/jopd.ad","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_gender_math_iat.html","id":null,"dir":"Reference","previous_headings":"","what":"Gender math IAT - Ch07 - Ithaca and SDSU replications of Nosek et al. (2002) — data_gender_math_iat","title":"Gender math IAT - Ch07 - Ithaca and SDSU replications of Nosek et al. (2002) — data_gender_math_iat","text":"extent men women differ attitudes towards mathematics? investigate, Nosek et al. (2002) asked male female students complete Implicit Association Test (IAT)-task designed measure participant's implicit (non-conscious) feelings towards topic. (never heard IAT, try : tiny.cc/harvardiat) IAT, students tested negative feelings towards mathematics art. Scores reflect degree student negative implicit attitudes mathematics art (positive score: negative feelings mathematics; 0: level negativity ; negative score: negative feelings art). data_gender_math_iat data two labs participated large-scale replication original study (Klein et al., 2014a, 2014b)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_gender_math_iat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gender math IAT - Ch07 - Ithaca and SDSU replications of Nosek et al. (2002) — data_gender_math_iat","text":"","code":"data_gender_math_iat"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_gender_math_iat.html","id":"data-gender-math-iat","dir":"Reference","previous_headings":"","what":"data_gender_math_iat","title":"Gender math IAT - Ch07 - Ithaca and SDSU replications of Nosek et al. (2002) — data_gender_math_iat","text":"data frame 155 rows 4 columns: Ithaca gender factor Ithaca IAT numeric SDSU gender factor SDSU IAT numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_gender_math_iat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Gender math IAT - Ch07 - Ithaca and SDSU replications of Nosek et al. (2002) — data_gender_math_iat","text":"https://psycnet.apa.org/fulltext/2014-20922-002.html","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_gender_math_iat_ma.html","id":null,"dir":"Reference","previous_headings":"","what":"Gender math IAT ma - Ch9 - Many Labs replications of Nosek et al. (2002) — data_gender_math_iat_ma","title":"Gender math IAT ma - Ch9 - Many Labs replications of Nosek et al. (2002) — data_gender_math_iat_ma","text":"EOC Exercise 4 Chapter 7 encountered classic study Nosek et al. (2002), male female participants completed Implicit Association Test (IAT) measured extent negative attitudes towards mathematics, compared art. study found women, compared men, tended negative implicit attitudes towards mathematics. Many Labs project repeated study locations around world (Klein et al., 2014a, 2014b). Summary data 30 labs available Gender math IAT ma. Higher scores indicate implicit bias mathematics. See also data_gender_math_iat raw data two specific sites replication effort.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_gender_math_iat_ma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gender math IAT ma - Ch9 - Many Labs replications of Nosek et al. (2002) — data_gender_math_iat_ma","text":"","code":"data_gender_math_iat_ma"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_gender_math_iat_ma.html","id":"data-gender-math-iat-ma","dir":"Reference","previous_headings":"","what":"data_gender_math_iat_ma","title":"Gender math IAT ma - Ch9 - Many Labs replications of Nosek et al. (2002) — data_gender_math_iat_ma","text":"data frame 30 rows 9 columns: Location factor M Male numeric s Male numeric n Male integer M Female numeric s Female numeric n Female integer USAorNot factor Country factor","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_gender_math_iat_ma.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Gender math IAT ma - Ch9 - Many Labs replications of Nosek et al. (2002) — data_gender_math_iat_ma","text":"https://psycnet.apa.org/fulltext/2014-20922-002.html","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_halagappa.html","id":null,"dir":"Reference","previous_headings":"","what":"Halagappa - Ch14 - from Halagappa et al. (2007) — data_halagappa","title":"Halagappa - Ch14 - from Halagappa et al. (2007) — data_halagappa","text":"eating much less delay Alzheimer's? , great news. Halagappa et al. (2007) investigated possibility using mouse model, meaning used Alzheimer-prone mice, genetically predisposed develop neural degeneration typical Alzheimer's. researchers used six independent groups mice, three tested mouse middle age 10 months old, three mouse old age 17 months. age control group normal mice ate freely (NFree10 NFree17 groups), group Alzheimer-prone mice also ate freely (AFree10 AFree17 groups), another Alzheimer-prone group restricted 40% less food normal (ADiet10 ADiet17 groups). Table 14.2 lists factors define groups, group labels. discuss one measure mouse cognition: percent time spent near target water maze, higher values indicating better learning memory. Table 14.2 reports means standard deviations measure, group sizes.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_halagappa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Halagappa - Ch14 - from Halagappa et al. (2007) — data_halagappa","text":"","code":"data_halagappa"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_halagappa.html","id":"data-halagappa","dir":"Reference","previous_headings":"","what":"data_halagappa","title":"Halagappa - Ch14 - from Halagappa et al. (2007) — data_halagappa","text":"data frame 6 rows 4 columns: Groups factor Mean numeric SD numeric n integer","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_halagappa.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Halagappa - Ch14 - from Halagappa et al. (2007) — data_halagappa","text":"https://www.sciencedirect.com/science/article/abs/pii/S0969996106003251","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_halagappa_et_al_2007.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample data for estimate_mdiff_ind_contrast: Halagappa (2007) — data_halagappa_et_al_2007","title":"Sample data for estimate_mdiff_ind_contrast: Halagappa (2007) — data_halagappa_et_al_2007","text":"eating much less delay Alzheimer’s? , great news. Halagappa et al. (2007) investigated possibility using mouse model, meaning used Alzheimer-prone mice, genetically predisposed develop neural degeneration typical Alzheimer’s. researchers used six independent groups mice, three tested mouse middle age 10 months old, three mouse old age 17 months. age control group normal mice ate freely (NFree10 NFree17 groups), group Alzheimer-prone mice also ate freely (AFree10 AFree17 groups), another Alzheimer-prone group restricted 40% less food normal (ADiet10 ADiet17 groups). Table 14.2 lists factors define groups, group labels, means, M1, M2, … (’ll see displayed ESCI). ’ll discuss one measure mouse cognition: percent time spent near target water maze, higher values indicating better learning memory. summary data set, providing sample sizes, means, standard deviations group study.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_halagappa_et_al_2007.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample data for estimate_mdiff_ind_contrast: Halagappa (2007) — data_halagappa_et_al_2007","text":"","code":"data_halagappa_et_al_2007"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_halagappa_et_al_2007.html","id":"data-halagappa-et-al-a-data-frame-with-rows-and-","dir":"Reference","previous_headings":"","what":"data_halagappa_et_al_2007 A data frame with 3 rows and 4","title":"Sample data for estimate_mdiff_ind_contrast: Halagappa (2007) — data_halagappa_et_al_2007","text":"columns: condition Factor indicating group: NFree10, AFree10, ADiet10, NFree17, AFree17, ADiet17 n Group sample sizes m Group means s Group standard deviation","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_halagappa_et_al_2007.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Sample data for estimate_mdiff_ind_contrast: Halagappa (2007) — data_halagappa_et_al_2007","text":"Halagappa, V. K. M., Guo, Z., Pearson, M., Matsuoka, Y., Cutler, R. G., LaFerla, F. M., & Mattson, M. P. (2007). Intermittent fasting caloric restriction ameliorate age-related behavioral deficits triple-transgenic mouse model Alzheimer's disease. Neurobiology Disease, 26, 212–220. doi:10.1016/j.nbd.2006.12.019","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_home_prices.html","id":null,"dir":"Reference","previous_headings":"","what":"Home Prices - Ch12 - for End-of-Chapter Exercise 12.2 — data_home_prices","title":"Home Prices - Ch12 - for End-of-Chapter Exercise 12.2 — data_home_prices","text":"Maybe thinking buying house college? Regression can help hunt bargain. Download Home Prices data set. file contains real estate listings 1997 2003 city California. explore extent size home (square meters) predicts sale price.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_home_prices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Home Prices - Ch12 - for End-of-Chapter Exercise 12.2 — data_home_prices","text":"","code":"data_home_prices"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_home_prices.html","id":"data-home-prices","dir":"Reference","previous_headings":"","what":"data_home_prices","title":"Home Prices - Ch12 - for End-of-Chapter Exercise 12.2 — data_home_prices","text":"data frame 300 rows 8 columns: MLS integer Location factor Price integer Bedrooms integer Bathrooms integer Size (m2) numeric Status factor Status_Code logical","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_kardas_expt_3.html","id":null,"dir":"Reference","previous_headings":"","what":"Kardas Expt 3 - Ch07 - from Kardas and O'Brien (2018), Experiment 3 — data_kardas_expt_3","title":"Kardas Expt 3 - Ch07 - from Kardas and O'Brien (2018), Experiment 3 — data_kardas_expt_3","text":"Suppose want change battery phone, cook perfect souffle, perform three-ball juggle. Just numerous people every day, might search online find video shows . Suppose watch video just . First question: well predict perform task? Second question: well actually perform task, first time tried? Now suppose watch video many times: consider two questions. questions investigated series studies Kardas O'Brien (2018). first quick analyses Kardas Experiments 3 4-call Expt 3 Expt 4-focusing effect watching video many times rather . Expt 3, participants first watched brief video person performing moonwalk. Low Exposure group watched video , High Exposure group 20 times. participants predicted, 1 10 scale, well felt able perform moonwalk . Finally, attempted single performance moonwalk, videoed. videos rated, 1 10 scale, independent raters.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_kardas_expt_3.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kardas Expt 3 - Ch07 - from Kardas and O'Brien (2018), Experiment 3 — data_kardas_expt_3","text":"","code":"data_kardas_expt_3"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_kardas_expt_3.html","id":"data-kardas-expt-","dir":"Reference","previous_headings":"","what":"data_kardas_expt_3","title":"Kardas Expt 3 - Ch07 - from Kardas and O'Brien (2018), Experiment 3 — data_kardas_expt_3","text":"data frame 100 rows 3 columns: Exposure factor Prediction numeric Performance numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_kardas_expt_3.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Kardas Expt 3 - Ch07 - from Kardas and O'Brien (2018), Experiment 3 — data_kardas_expt_3","text":"https://journals.sagepub.com/doi/abs/10.1177/0956797617740646","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_kardas_expt_4.html","id":null,"dir":"Reference","previous_headings":"","what":"Kardas Expt 4 - Ch07 - from Kardas and O'Brien (2018), Experiment 4 — data_kardas_expt_4","title":"Kardas Expt 4 - Ch07 - from Kardas and O'Brien (2018), Experiment 4 — data_kardas_expt_4","text":"Suppose want change battery phone, cook perfect souffle, perform three-ball juggle. Just numerous people every day, might search online find video shows . Suppose watch video just . First question: well predict perform task? Second question: well actually perform task, first time tried? Now suppose watch video many times: consider two questions. questions investigated series studies Kardas O'Brien (2018). first quick analyses Kardas Experiments 3 4-call Expt 3 Expt 4-focusing effect watching video many times rather . Expt 4 conducted online participants recruited Amazon's Mechanical Turk, typically diverse students. online task based mirror-drawing game developed Bob students (Cusack et al., 2015, tiny.cc/bobmirrortrace). Participants first read description game scoring procedure. play, use computer trackpad trace target line, accurately quickly can. task tricky can see mirror image path tracing finger trackpad. running score displayed. final score percentage match target line path traced, scores can range 0 100","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_kardas_expt_4.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kardas Expt 4 - Ch07 - from Kardas and O'Brien (2018), Experiment 4 — data_kardas_expt_4","text":"","code":"data_kardas_expt_4"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_kardas_expt_4.html","id":"data-kardas-expt-","dir":"Reference","previous_headings":"","what":"data_kardas_expt_4","title":"Kardas Expt 4 - Ch07 - from Kardas and O'Brien (2018), Experiment 4 — data_kardas_expt_4","text":"data frame 270 rows 4 columns: Exposure factor Prediction integer Performance integer Time numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_kardas_expt_4.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Kardas Expt 4 - Ch07 - from Kardas and O'Brien (2018), Experiment 4 — data_kardas_expt_4","text":"https://journals.sagepub.com/doi/abs/10.1177/0956797617740646","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_labels_flavor.html","id":null,"dir":"Reference","previous_headings":"","what":"Labels flavor - Ch8 - from Floretta-Schiller et al. (2015) — data_labels_flavor","title":"Labels flavor - Ch8 - from Floretta-Schiller et al. (2015) — data_labels_flavor","text":"extent brand labels influence perceptions product? investigate, participants asked participate taste test. participants actually given grape juice, one glass poured bottle labeled 'Organic' glass bottle labeled 'Generic'. tasting (counterbalanced order), participants asked rate much enjoyed juice scale 1 () 10 (much). Participants also asked say much willing pay large container juice scale $1 $10. Load Labels flavor data set book website. data collected part class project Floretta-Schiller et al. (2015), whose work inspired clever study looking effects fast-food wrappers children's enjoyment food (Robinson et al., 2007).","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_labels_flavor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Labels flavor - Ch8 - from Floretta-Schiller et al. 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Working France, created three independent groups, comprising 95 adults. Participants worked online seven learning modules DNA. Reread group worked module worked second time, going next module. Quiz group worked module complete quiz going next module. Prequiz group work quiz seeing presentation module, went quiz presentation next module. Participants received feedback brief explanation answering question quiz, take long wished work module quiz. Seven days later participants completed final test.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample data for estimate_mdiff_ind_contrast: Test score and times from\r\nLatimier et al (2019). — data_latimier","text":"","code":"data_latimier"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier.html","id":"data-latimier-a-data-frame-with-rows-and-columns-","dir":"Reference","previous_headings":"","what":"data_latimier A data frame with 285 rows and 3 columns:","title":"Sample data for estimate_mdiff_ind_contrast: Test score and times from\r\nLatimier et al (2019). — data_latimier","text":"Group factor indicating membership Reread, Quiz, Prequiz group Test % correct final test Time Time taken final test","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Sample data for estimate_mdiff_ind_contrast: Test score and times from\r\nLatimier et al (2019). — data_latimier","text":"https://osf.io/23yad Latimier, ., Riegert, ., Peyre, H., Ly, S. T., Casati, R., & Ramus, F. (2019). pre-testing promote better retention post-testing? Npj Science Learning, 4(1), 1–7. https://doi.org/10.1038/s41539-019-0053-1","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sample data for estimate_mdiff_ind_contrast: Test score and times from\r\nLatimier et al (2019). — data_latimier","text":"data set suitable estimating magnitude difference contrast independent groups.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_3groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Latimier 3Groups - Ch14 - 3 groups in Latimier et al. (2019) — data_latimier_3groups","title":"Latimier 3Groups - Ch14 - 3 groups in Latimier et al. (2019) — data_latimier_3groups","text":"researchers interested different study approaches might impact learning. Working France, created three independent groups, comprising 95 adults. Participants worked online seven learning modules DNA. Reread group worked module, worked second time going next module. Quiz group worked module, complete quiz going next module. Prequiz group work quiz seeing presentation module, went quiz presentation next module. Participants received feedback brief explanation answering question quiz, take long wished work module quiz. Seven days later, participants completed final test. data_latimier_3groups full data set. facilitate different student exercises, also seperate data entities group (data_latimier_prequiz, data_latimier_reread, etc.), every pair groups (data_latimier_quiz_prequiz, etc.).","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_3groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Latimier 3Groups - Ch14 - 3 groups in Latimier et al. 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(2019) — data_latimier_prequiz","text":"Just Prequiz group Latimier et al., 2019 See full details data_latimier_3_groups","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_prequiz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Latimier Prequiz - Ch03 - Prequiz group in Latimier et al. (2019) — data_latimier_prequiz","text":"","code":"data_latimier_prequiz"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_prequiz.html","id":"data-latimier-prequiz","dir":"Reference","previous_headings":"","what":"data_latimier_prequiz","title":"Latimier Prequiz - Ch03 - Prequiz group in Latimier et al. (2019) — data_latimier_prequiz","text":"data frame 95 rows 3 columns: Group factor Test% numeric TIme numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_prequiz.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Latimier Prequiz - Ch03 - Prequiz group in Latimier et al. (2019) — data_latimier_prequiz","text":"https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XPYPMF","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_quiz.html","id":null,"dir":"Reference","previous_headings":"","what":"Latimier Quiz - Ch03 - Quiz group in Latimier et al. (2019) — data_latimier_quiz","title":"Latimier Quiz - Ch03 - Quiz group in Latimier et al. (2019) — data_latimier_quiz","text":"Just Quiz group Latimier et al., 2019 See full details data_latimier_3_groups","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_quiz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Latimier Quiz - Ch03 - Quiz group in Latimier et al. (2019) — data_latimier_quiz","text":"","code":"data_latimier_quiz"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_quiz.html","id":"data-latimier-quiz","dir":"Reference","previous_headings":"","what":"data_latimier_quiz","title":"Latimier Quiz - Ch03 - Quiz group in Latimier et al. (2019) — data_latimier_quiz","text":"data frame 95 rows 3 columns: Group factor Test% numeric Time numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_quiz.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Latimier Quiz - Ch03 - Quiz group in Latimier et al. 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(2019) — data_latimier_reread_quiz","text":"","code":"data_latimier_reread_quiz"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_reread_quiz.html","id":"data-latimier-reread-quiz","dir":"Reference","previous_headings":"","what":"data_latimier_reread_quiz","title":"Latimier Reread Quiz - Ch07 - Reread and Quiz groups in Latimier et al. (2019) — data_latimier_reread_quiz","text":"data frame 190 rows 3 columns: Group factor Test% numeric Time numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_reread_quiz.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Latimier Reread Quiz - Ch07 - Reread and Quiz groups in Latimier et al. (2019) — data_latimier_reread_quiz","text":"https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XPYPMF","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_macnamara_r_ma.html","id":null,"dir":"Reference","previous_headings":"","what":"Macnamara r ma - Ch11 - from Macnamara et al. (2014) — data_macnamara_r_ma","title":"Macnamara r ma - Ch11 - from Macnamara et al. (2014) — data_macnamara_r_ma","text":"genius born made? us Michael Jordan Mozart worked sufficiently hard develop requisite skills? Meta-analysis correlations can help answer questions. issue extent practice effort may sufficient achieving highest levels expertise. Ericsson et al. (1993) argued years effort matters : 'Many characteristics believed reflect innate talent actually result intense practice extended minimum 10 years' (p. 363). view enormously popularized Malcolm Gladwell (2008), argued book Outliers 10,000 hours focused practice key achieving expertise. However, view now challenged, one important contribution large meta-analysis correlations amount intense practice level achievement: Macnamara et al. (2014) combined 157 correlations reported wide range fields, sports music education, found correlation r = .35 (.30, .39). Table 11.1 shows 16 main correlations music, Macnamara et al. (2014).","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_macnamara_r_ma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Macnamara r ma - Ch11 - from Macnamara et al. (2014) — data_macnamara_r_ma","text":"","code":"data_macnamara_r_ma"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_macnamara_r_ma.html","id":"data-macnamara-r-ma","dir":"Reference","previous_headings":"","what":"data_macnamara_r_ma","title":"Macnamara r ma - Ch11 - from Macnamara et al. (2014) — data_macnamara_r_ma","text":"data frame 16 rows 4 columns: Study factor r numeric N integer Instrument_Type factor","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_macnamara_r_ma.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Macnamara r ma - Ch11 - from Macnamara et al. (2014) — data_macnamara_r_ma","text":"https://journals.sagepub.com/doi/full/10.1177/0956797614535810","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_mccabemichael_brain.html","id":null,"dir":"Reference","previous_headings":"","what":"McCabeMichael brain - Ch9 - from Michael et al. (2013) — data_mccabemichael_brain","title":"McCabeMichael brain - Ch9 - from Michael et al. (2013) — data_mccabemichael_brain","text":"probably seen cross sections brain brightly colored areas indicating brain regions active particular type cognition emotion. Search online fMRI (functional magnetic resonance imaging) brain scans see pictures learn made. can fascinating-last able see thinking works? 2008, McCabe Castel published studies investigated adding brain picture might alter judgments credibility scientific article. one group participants, article accompanied brain image irrelevant article. second, independent group, image. Participants read article, gave rating statement 'scientific reasoning article made sense'. response options 1 (strongly disagree), 2 (disagree), 3 (agree), 4 (strongly agree). researchers reported mean ratings higher brain picture without, difference small. seemed irrelevant brain picture may , small influence. authors drew appropriately cautious conclusions, result quickly attracted attention many media reports greatly overstated . least according popular media, seemed adding brain picture made story convincing. Search 'McCabe seeing believing', similar, find media reports blog posts. warned readers watch brain pictures, , said, can trick believing things true. result intrigued New Zealander colleagues mine discovered , despite wide recognition, finding replicated. ran replication studies using materials used original researchers, found generally small ESs. joined team data analysis stage research published (Michael et al., 2013). discuss meta-analysis two original studies eight replications team. studies sufficiently similar meta-analysis, especially considering Michael studies designed many features matched original studies. data set include two additional critique studies run Michael team. See also data_mccabemichael_brain2","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_mccabemichael_brain.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"McCabeMichael brain - Ch9 - from Michael et al. (2013) — data_mccabemichael_brain","text":"","code":"data_mccabemichael_brain"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_mccabemichael_brain.html","id":"data-mccabemichael-brain","dir":"Reference","previous_headings":"","what":"data_mccabemichael_brain","title":"McCabeMichael brain - Ch9 - from Michael et al. (2013) — data_mccabemichael_brain","text":"data frame 10 rows 9 columns: Study name factor M Brain numeric s Brain numeric n Brain numeric M Brain numeric s Brain numeric n Brain numeric SimpleCritique factor Research group factor","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_mccabemichael_brain.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"McCabeMichael brain - Ch9 - from Michael et al. (2013) — data_mccabemichael_brain","text":"https://link.springer.com/article/10.3758/s13423-013-0391-6","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_mccabemichael_brain2.html","id":null,"dir":"Reference","previous_headings":"","what":"McCabeMichael brain2 - Ch9 - from Michael et al. (2013) — data_mccabemichael_brain2","title":"McCabeMichael brain2 - Ch9 - from Michael et al. (2013) — data_mccabemichael_brain2","text":"data_mccabemichael_brain includes two additional critique studies run Michael team.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_mccabemichael_brain2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"McCabeMichael brain2 - Ch9 - from Michael et al. (2013) — data_mccabemichael_brain2","text":"","code":"data_mccabemichael_brain2"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_mccabemichael_brain2.html","id":"data-mccabemichael-brain-","dir":"Reference","previous_headings":"","what":"data_mccabemichael_brain2","title":"McCabeMichael brain2 - Ch9 - from Michael et al. (2013) — data_mccabemichael_brain2","text":"data frame 12 rows 9 columns: Study name factor M Brain numeric s Brain numeric n Brain numeric M Brain numeric s Brain numeric n Brain numeric SimpleCritique factor Research group factor","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_mccabemichael_brain2.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"McCabeMichael brain2 - Ch9 - from Michael et al. (2013) — data_mccabemichael_brain2","text":"https://link.springer.com/article/10.3758/s13423-013-0391-6","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_meditationbrain.html","id":null,"dir":"Reference","previous_headings":"","what":"MeditationBrain - Ch15 - from Holzel et al. (2011) — data_meditationbrain","title":"MeditationBrain - Ch15 - from Holzel et al. (2011) — data_meditationbrain","text":"example well-known study mindfulness meditation Holzel et al. (2011). People wanted reduce stress, experienced meditators, assigned Meditation (n = 16) Control (n = 17) group. Meditation group participated 8 weeks intensive training practice mindfulness meditation. researchers used questionnaire assess range emotional cognitive variables (Pretest) (Posttest) 8-week period. assessment conducted participants meditating. study notable including brain imaging assess possible changes participants' brains Pretest Posttest. researchers measured gray matter concentration, increases brain regions experience higher frequent activation. researchers expected hippocampus may especially responsive meditation implicated regulation emotion, arousal, general responsiveness. therefore included planned analysis assessment changes gray matter concentration hippocampus.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_meditationbrain.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MeditationBrain - Ch15 - from Holzel et al. (2011) — data_meditationbrain","text":"","code":"data_meditationbrain"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_meditationbrain.html","id":"data-meditationbrain","dir":"Reference","previous_headings":"","what":"data_meditationbrain","title":"MeditationBrain - Ch15 - from Holzel et al. (2011) — data_meditationbrain","text":"data frame 33 rows 7 columns: Pretest numeric Posttest numeric Group factor ControlPre numeric ControlPost numeric MeditationPre numeric MeditationPost numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_meditationbrain.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"MeditationBrain - Ch15 - from Holzel et al. (2011) — data_meditationbrain","text":"https://link.springer.com/chapter/10.1007/978-94-007-2079-4_9","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_organicmoral.html","id":null,"dir":"Reference","previous_headings":"","what":"OrganicMoral - Ch14 - from Eskine (2013) — data_organicmoral","title":"OrganicMoral - Ch14 - from Eskine (2013) — data_organicmoral","text":"extent might choosing organic foods make us morally smug? investigate, Eskine (2013) asked participants rate images organic food, neutral (control) food, comfort food. Next, guise different study, participants completed moral judgment scale read different controversial scenarios rated morally wrong judged (scale 1-7, high judgments mean wrong). Table 14.7 shows summary data, also available first four variables OrganicMoral file. file can see two variables, report full data-come shortly. use summary data. results Eskine (2013) published, Moery Calin-Jageman (2016) conducted series close replications. obtained original materials Eskine, piloted procedure, preregistered sampling analysis plan. OSF page, osf.io/atkn7, details. data one close replications last two variables OrganicMoral file. replication study, group names variable ReplicationGroup moral judgments MoralJudgment. (may need scroll right see variables.)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_organicmoral.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"OrganicMoral - Ch14 - from Eskine (2013) — data_organicmoral","text":"","code":"data_organicmoral"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_organicmoral.html","id":"data-organicmoral","dir":"Reference","previous_headings":"","what":"data_organicmoral","title":"OrganicMoral - Ch14 - from Eskine (2013) — data_organicmoral","text":"data frame 106 rows 6 columns: Group factor Mean numeric SD numeric N integer ReplicationGroup factor MoralJudgmentment numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_organicmoral.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"OrganicMoral - Ch14 - from Eskine (2013) — data_organicmoral","text":"https://journals.sagepub.com/doi/full/10.1177/1948550616639649","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_penlaptop1.html","id":null,"dir":"Reference","previous_headings":"","what":"% transcription scores from pen and laptop group of Meuller et al., 2014 — data_penlaptop1","title":"% transcription scores from pen and laptop group of Meuller et al., 2014 — data_penlaptop1","text":"% transcription scores pen laptop group Meuller et al., 2014","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_penlaptop1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"% transcription scores from pen and laptop group of Meuller et al., 2014 — data_penlaptop1","text":"","code":"data_penlaptop1"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_penlaptop1.html","id":"data-penlaptop-","dir":"Reference","previous_headings":"","what":"data_penlaptop1","title":"% transcription scores from pen and laptop group of Meuller et al., 2014 — data_penlaptop1","text":"data frame 65 rows 2 columns: condition factor transcription numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_penlaptop1.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"% transcription scores from pen and laptop group of Meuller et al., 2014 — data_penlaptop1","text":"https://journals.sagepub.com/doi/full/10.1177/0956797614524581","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_powerperformance_ma.html","id":null,"dir":"Reference","previous_headings":"","what":"PowerPerformance ma - Ch9 - from Burgmer and Englich (2012), and Cusack et al. (2015) — data_powerperformance_ma","title":"PowerPerformance ma - Ch9 - from Burgmer and Englich (2012), and Cusack et al. (2015) — data_powerperformance_ma","text":"extent feeling powerful affect performance motor skills? investigate, Burgmer Englich (2012) assigned German participants either power control conditions asked play golf (Experiment 1) darts (Experiment 2). found participants manipulated feel powerful performed substantially better control condition. study finding , Cusack et al. (2015) conducted five replications United States. Across replications tried different ways manipulating power, different types tasks (golf, mirror tracing, cognitive task), different levels difficulty, different types participant pools (undergraduates online). Summary data seven studies available PowerPerformance ma.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_powerperformance_ma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PowerPerformance ma - Ch9 - from Burgmer and Englich (2012), and Cusack et al. (2015) — data_powerperformance_ma","text":"","code":"data_powerperformance_ma"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_powerperformance_ma.html","id":"data-powerperformance-ma","dir":"Reference","previous_headings":"","what":"data_powerperformance_ma","title":"PowerPerformance ma - Ch9 - from Burgmer and Englich (2012), and Cusack et al. (2015) — data_powerperformance_ma","text":"data frame 8 rows 12 columns: StudyName factor Country factor Population factor Difficulty factor Task factor M Control numeric s Control numeric M Power numeric s Power numeric Cohen d unb numeric n Control integer n Power integer","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_powerperformance_ma.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"PowerPerformance ma - Ch9 - from Burgmer and Englich (2012), and Cusack et al. (2015) — data_powerperformance_ma","text":"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0140806","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_rattanmotivation.html","id":null,"dir":"Reference","previous_headings":"","what":"RattanMotivation - Ch14 - from Rattan et al. (2012) — data_rattanmotivation","title":"RattanMotivation - Ch14 - from Rattan et al. (2012) — data_rattanmotivation","text":"think react feedback gave encouragement reassurance, , instead, encouragement challenge? Carol Dweck colleagues investigated many questions people respond different types feedback. next example comes Dweck's research group illustrates data analysis starts full data, rather summary statistics. Rattan et al. (2012) asked college student participants imagine undertaking mathematics course just received low score (65%) first test year. Participants assigned randomly three groups, received different feedback along low score. Comfort group received positive encouragement also reassurance, Challenge group received positive encouragement also challenge, Control group received just positive encouragement. Participants responded range questions felt course professor. discuss data ratings motivation toward mathematics, made received feedback.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_rattanmotivation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RattanMotivation - Ch14 - from Rattan et al. (2012) — data_rattanmotivation","text":"","code":"data_rattanmotivation"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_rattanmotivation.html","id":"data-rattanmotivation","dir":"Reference","previous_headings":"","what":"data_rattanmotivation","title":"RattanMotivation - Ch14 - from Rattan et al. (2012) — data_rattanmotivation","text":"data frame 54 rows 2 columns: Group factor Motivation numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_rattanmotivation.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RattanMotivation - Ch14 - from Rattan et al. (2012) — data_rattanmotivation","text":"https://www.sciencedirect.com/science/article/pii/S0022103111003027","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_religionsharing.html","id":null,"dir":"Reference","previous_headings":"","what":"ReligionSharing - Ch14 - for End-of-Chapter Exercise 14.3 — data_religionsharing","title":"ReligionSharing - Ch14 - for End-of-Chapter Exercise 14.3 — data_religionsharing","text":"extent religious upbringing related prosocial behavior childhood? investigate, large international sample children asked play game given 10 stickers asked give stickers away another child able tested day. number stickers donated considered measure altruistic sharing. addition, parents child reported family's religion. Summary data provided.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_religionsharing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ReligionSharing - Ch14 - for End-of-Chapter Exercise 14.3 — data_religionsharing","text":"","code":"data_religionsharing"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_religionsharing.html","id":"data-religionsharing","dir":"Reference","previous_headings":"","what":"data_religionsharing","title":"ReligionSharing - Ch14 - for End-of-Chapter Exercise 14.3 — data_religionsharing","text":"data frame 3 rows 4 columns: Group factor Mean numeric SD numeric N integer","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_religious_belief.html","id":null,"dir":"Reference","previous_headings":"","what":"Religious belief - Ch03 - for End-of-Chapter Exercise 3.5 — data_religious_belief","title":"Religious belief - Ch03 - for End-of-Chapter Exercise 3.5 — data_religious_belief","text":"look data religious beliefs. Religious belief file data large online survey participants asked report, scale 0 100, belief existence God. Age also reported.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_religious_belief.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Religious belief - Ch03 - for End-of-Chapter Exercise 3.5 — data_religious_belief","text":"","code":"data_religious_belief"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_religious_belief.html","id":"data-religious-belief","dir":"Reference","previous_headings":"","what":"data_religious_belief","title":"Religious belief - Ch03 - for End-of-Chapter Exercise 3.5 — data_religious_belief","text":"data frame 213 rows 3 columns: Response_ID character Belief_in_God factor Age integer","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_selfexplain.html","id":null,"dir":"Reference","previous_headings":"","what":"SelfExplain - Ch15 - from McEldoon et al. (2013) — data_selfexplain","title":"SelfExplain - Ch15 - from McEldoon et al. (2013) — data_selfexplain","text":"Self-explaining learning strategy students write say explanations material studying. Self-explaining generally found effective standard studying, may also take time. raises question whether study strategy extra time benefits learning. explore issue, grade school children took pretest mathematics conceptual knowledge, studied mathematics problems, took similar posttest (McEldoon et al., 2013). Participants randomly assigned one two study conditions: normal study + practice (Practice group), self-explaining (Self-Explain group). first condition intended make time spent learning similar two groups. can find part data study SelfExplain, scores percent correct.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_selfexplain.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SelfExplain - Ch15 - from McEldoon et al. (2013) — data_selfexplain","text":"","code":"data_selfexplain"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_selfexplain.html","id":"data-selfexplain","dir":"Reference","previous_headings":"","what":"data_selfexplain","title":"SelfExplain - Ch15 - from McEldoon et al. (2013) — data_selfexplain","text":"data frame 52 rows 4 columns: Student ID factor Condition factor Pretest numeric Posttest numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_selfexplain.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"SelfExplain - Ch15 - from McEldoon et al. (2013) — data_selfexplain","text":"https://bpspsychub.onlinelibrary.wiley.com/doi/abs/10.1111/j.2044-8279.2012.02083.x","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_simmonscredibility.html","id":null,"dir":"Reference","previous_headings":"","what":"SimmonsCredibility - Ch14 - from Simmons and Nelson (2020) — data_simmonscredibility","title":"SimmonsCredibility - Ch14 - from Simmons and Nelson (2020) — data_simmonscredibility","text":"excited! company developed wonderful new weight-loss program, now job develop ad campaign. choose BeforeAfter pair pictures, Figure 14.1, top panel? might Progressive sequence pictures person, bottom panel, effective? Pause, think, discuss. choose, ? might think BeforeAfter simpler dramatic. hand, Progressive highlights steady improvement claim program deliver. probably surprised learn BeforeAfter used often long favorite advertising industry, whereas Progressive used rarely. Luca Cian colleagues (Cian et al., 2020) curious know extent BeforeAfter actually effective, appealing, credible Progressive, , indeed, whether Progressive might score highly. reported seven studies various aspects question. focus Study 2, used three independent groups compare three conditions illustrated Figure 14.1. BeforeAfterInfo condition, middle panel, comprises three BeforeAfter pairs, thus providing extra information endpoints. researchers included condition case advantage Progressive might stem simply images, rather illustrates clear progressive sequence. randomly assigned 213 participants MTurk one three groups. Participants asked 'imagine decided lose weight', saw one three ads weight loss program called MRMDiets. answered question 'evaluate MRMDiets?' choosing 1-7 response several scales, including Unlikeable-Likable, Ineffective-Effective, credible-Credible. researchers averaged six scores give overall Credibility score, 1-7 scale, 7 credible. Simmons Nelson (2020) sufficiently intrigued carry two substantial close replications. cooperation original researchers, used materials procedure. used much larger groups preregistered research plan, including data analysis plan. focus first replication, 761 participants MTurk randomized three groups.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_simmonscredibility.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SimmonsCredibility - Ch14 - from Simmons and Nelson (2020) — data_simmonscredibility","text":"","code":"data_simmonscredibility"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_simmonscredibility.html","id":"data-simmonscredibility","dir":"Reference","previous_headings":"","what":"data_simmonscredibility","title":"SimmonsCredibility - Ch14 - from Simmons and Nelson (2020) — data_simmonscredibility","text":"data frame 3 rows 4 columns: Groups factor Mean numeric SD numeric n integer","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_simmonscredibility.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"SimmonsCredibility - Ch14 - from Simmons and Nelson (2020) — data_simmonscredibility","text":"http://datacolada.org/94","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_sleep_beauty.html","id":null,"dir":"Reference","previous_headings":"","what":"Sleep Beauty - Ch11 - for End-of-Chapter Exercise 11.6 — data_sleep_beauty","title":"Sleep Beauty - Ch11 - for End-of-Chapter Exercise 11.6 — data_sleep_beauty","text":"really thing beauty sleep? investigate, researchers decided examine extent sleep relates attractiveness. 70 college students self-reported amount sleep night . addition, photograph taken participant rated attractiveness scale 1 10 two judges opposite gender. average rating score used. can download data set (Sleep Beauty) book website.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_sleep_beauty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sleep Beauty - Ch11 - for End-of-Chapter Exercise 11.6 — data_sleep_beauty","text":"","code":"data_sleep_beauty"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_sleep_beauty.html","id":"data-sleep-beauty","dir":"Reference","previous_headings":"","what":"data_sleep_beauty","title":"Sleep Beauty - Ch11 - for End-of-Chapter Exercise 11.6 — data_sleep_beauty","text":"data frame 70 rows 2 columns: Sleep (hours) numeric Rated_Attractiveness numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_smithrecall.html","id":null,"dir":"Reference","previous_headings":"","what":"SmithRecall - Ch15 - from Smith et al. (2016) — data_smithrecall","title":"SmithRecall - Ch15 - from Smith et al. (2016) — data_smithrecall","text":"SmithRecall - Ch15 - Smith et al. (2016)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_smithrecall.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SmithRecall - Ch15 - from Smith et al. (2016) — data_smithrecall","text":"","code":"data_smithrecall"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_smithrecall.html","id":"data-smithrecall","dir":"Reference","previous_headings":"","what":"data_smithrecall","title":"SmithRecall - Ch15 - from Smith et al. (2016) — data_smithrecall","text":"data frame 120 rows 6 columns: ID integer Study Technique factor Stress Status factor %Recalled numeric Items Recalled numeric Group factor","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_stickgold.html","id":null,"dir":"Reference","previous_headings":"","what":"Stickgold - Ch06 - from Stickgold et al. (2000) — data_stickgold","title":"Stickgold - Ch06 - from Stickgold et al. (2000) — data_stickgold","text":"Stickgold et al. (2000) found , remarkably, performance visual discrimination task actually improved 48-96 hours initial training, even without practice time. However, participants sleep deprived period? trained 11 participants new skill, sleep deprived. data (-14.7, -10.7, -10.7, 2.2, 2.4, 4.5, 7.2, 9.6, 10, 21.3, 21.8)-download Stickgold data set book website. data changes performance scores immediately training night without sleep: 0 represents change, positive scores represent improvement, negative scores represent decline. Data set courtesy DataCrunch (tiny.cc/Stickgold)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_stickgold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stickgold - Ch06 - from Stickgold et al. (2000) — data_stickgold","text":"","code":"data_stickgold"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_stickgold.html","id":"data-stickgold","dir":"Reference","previous_headings":"","what":"data_stickgold","title":"Stickgold - Ch06 - from Stickgold et al. (2000) — data_stickgold","text":"data frame 11 rows 3 columns: Sleep deprived numeric B factor C factor","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_stickgold.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Stickgold - Ch06 - from Stickgold et al. (2000) — data_stickgold","text":"https://www.statcrunch.com/app/index.html?dataid=1053539","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_studystrategies.html","id":null,"dir":"Reference","previous_headings":"","what":"StudyStrategies - Ch14 - from O'Reilly et al. (1998) — data_studystrategies","title":"StudyStrategies - Ch14 - from O'Reilly et al. (1998) — data_studystrategies","text":"extent study strategy influence learning? investigate, psychology students randomly assigned three groups asked learn biology facts using one three different strategies: ) Self-Explain (explaining fact new knowledge gained relates already known), b) Elab Interrogation (elaborative interrogation: stating fact makes sense), c) Repetition Control (stating fact ). studying, students took 25-point fill--blank test (O'Reilly et al., 1998)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_studystrategies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"StudyStrategies - Ch14 - from O'Reilly et al. (1998) — data_studystrategies","text":"","code":"data_studystrategies"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_studystrategies.html","id":"data-studystrategies","dir":"Reference","previous_headings":"","what":"data_studystrategies","title":"StudyStrategies - Ch14 - from O'Reilly et al. (1998) — data_studystrategies","text":"data frame 3 rows 10 columns: Group factor TestMean numeric TestSD numeric TestN integer PrevKnowMean numeric PrevKnowSD numeric PrevKnowN integer EaseUseMean numeric EaseUseSD numeric EaseUseN integer","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_studystrategies.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"StudyStrategies - Ch14 - from O'Reilly et al. (1998) — data_studystrategies","text":"https://doi.org/10.1006/ceps.1997.0977","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_thomason1.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample data for estimate_mdiff_paired: LSAT scores from Thomason (2014) — data_thomason1","title":"Sample data for estimate_mdiff_paired: LSAT scores from Thomason (2014) — data_thomason1","text":"Thomason et al. (2014) reported 7 studies evaluating promising approach teaching critical thinking combines argument mapping form mastery learning (Mazur, 1997). Studies conducted United States, Canada, United Kingdom, study used single group students. Students tested various well-established measures critical thinking, (Pretest) (Posttest) training. Group sizes ranged 7 39. Thomason studies compared two conditions, Pretest Posttest, within participants, therefore used paired design. dataset first Thomason study, Thomason 1. used group N = 12 students, whose critical- thinking ability assessed Pretest Posttest using Logical Reasoning section Law School Aptitude Test (LSAT).","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_thomason1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample data for estimate_mdiff_paired: LSAT scores from Thomason (2014) — data_thomason1","text":"","code":"data_thomason1"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_thomason1.html","id":"data-thomason-a-data-frame-with-rows-and-columns-","dir":"Reference","previous_headings":"","what":"data_thomason1 A data frame with 12 rows and 2 columns:","title":"Sample data for estimate_mdiff_paired: LSAT scores from Thomason (2014) — data_thomason1","text":"pretest LSAT score argument mapping mastery training posttest LSAT score argument mapping mastery training","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_thomason1.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Sample data for estimate_mdiff_paired: LSAT scores from Thomason (2014) — data_thomason1","text":"Thomason, N. R., Adajian, T., Barnett, . E., Boucher, S., van der Brugge, E., Campbell, J., Knorpp, W., Lempert, R., Lengbeyer, L., Mandel, D. R., Rider, Y., van Gelder, T., & Wilkins, J. (2014). Critical thinking final report. University Melbourne, N66001-12-C-2004.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_thomason_1.html","id":null,"dir":"Reference","previous_headings":"","what":"Thomason 1 - Ch11 - from Thomason 1 — data_thomason_1","title":"Thomason 1 - Ch11 - from Thomason 1 — data_thomason_1","text":"Summary data unpublished study Neil Thomason colleagues, interested ways enhance students' critical thinking. investigating argument mapping, promising way use diagrams represent structure arguments. Students study completed established test critical thinking (Pretest), critical thinking course based argument mapping, second version test (Posttest).","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_thomason_1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Thomason 1 - Ch11 - from Thomason 1 — data_thomason_1","text":"","code":"data_thomason_1"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_thomason_1.html","id":"data-thomason-","dir":"Reference","previous_headings":"","what":"data_thomason_1","title":"Thomason 1 - Ch11 - from Thomason 1 — data_thomason_1","text":"data frame 12 rows 3 columns: Participant ID factor Pretest numeric Posttest numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_videogameaggression.html","id":null,"dir":"Reference","previous_headings":"","what":"VideogameAggression - Ch15 - from Hilgard (2015) — data_videogameaggression","title":"VideogameAggression - Ch15 - from Hilgard (2015) — data_videogameaggression","text":"Video games can violent can also challenging. extent might factors cause aggressive behavior? explore, Hilgard (2015) asked male participants play one four versions video game 15 minutes. game customized vary violence (shooting zombies helping aliens) difficulty (targets controlled tough AI dumb AI). game, players provoked given insulting evaluation confederate. Participants got decide long confederate hold hand painfully cold ice water (0 80 seconds), taken measure aggressive behavior. can find materials analysis plan study Open Science Framework: osf. io/cwenz. simplified version full data set.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_videogameaggression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"VideogameAggression - Ch15 - from Hilgard (2015) — data_videogameaggression","text":"","code":"data_videogameaggression"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_videogameaggression.html","id":"data-videogameaggression","dir":"Reference","previous_headings":"","what":"data_videogameaggression","title":"VideogameAggression - Ch15 - from Hilgard (2015) — data_videogameaggression","text":"data frame 223 rows 3 columns: Violence factor Difficulty factor Agression numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_videogameaggression.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"VideogameAggression - Ch15 - from Hilgard (2015) — data_videogameaggression","text":"https://journals.sagepub.com/doi/10.1177/0956797619829688","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/esci_document_data.html","id":null,"dir":"Reference","previous_headings":"","what":"also promote kind, altruistic behavior. Brethel-Haurwitz and Marsh (2014)\r\nexamined this idea by collecting data on U.S. states. A Gallup poll in 2010\r\nwas used to measure each state’s well-being index, a measure of mean\r\nhappiness for the state’s residents on a scale from 0 to 100. Next, a kidney\r\ndonation database for 1999–2010 was used to figure out each state’s rate\r\n(number of donations per 1 million people) of non-directed kidney\r\ndonations—that’s giving one kidney to a stranger, an extremely generous and\r\naltruistic thing to do!\r\ntheir classic study, Jacowitz and Kahneman (1995) asked participants to\r\nestimate how many babies are born each day in the United States. Participants\r\nwere given either a low anchor (“more than 100 babies/day”) or a high anchor\r\n(“less than 50,000 babies/day”). Those who saw the low anchor estimated many\r\nfewer births/day than those who saw the high anchor, which suggests that the\r\nwording can have a profound influence. The correct answer, as it happens, is\r\n~11,000 births/day in 2014. To investigate the extent that these results are\r\nreplicable, the Many Labs project repeated this classic study at many\r\ndifferent labs around the world. You can find the summary data for 30 of\r\nthese labs in the Anchor Estimate ma data file\r\nnumerous other issues can be highly damaging, but are thriving in this social\r\nmedia age. Trying to debunk a conspiracy theory by presenting facts and\r\nevidence often doesn’t work, alas. Psychological inoculation, also similar to\r\nprebunking, presents a mild form of misinformation, preferably with\r\nexplanation, in the hope of building resistance to real-life fake news—a sort\r\nof vaccine for fake news. The Bad News game is a spin-off from research on\r\npsychological inoculation. Basol et al. (2020) assessed the possible\r\neffectiveness of this game as a fake news vaccine. At getbadnews.com you can\r\nclick “About” for information, or just start playing the game— it’s easy and\r\nmaybe even fun. You encounter mock Twitter (now X) fake news messages that\r\nillustrate common strategies for making fake news memorable or believable.\r\nYou make choices between messages and decide which ones to “forward” as you\r\ntry to spread fake news while building your credibility score and number of\r\n“followers”—rather like real life for a conspiracy theorist wanting to spread\r\nthe word. Compete with your friends for credibility and number of followers.\r\nBasol’s online participants first saw 18 fictitious fake news tweets and\r\nrated each for reliability (accuracy, believability), and also rated their\r\nconfidence in that reliability rating. Both ratings were on a 1 to 7 scale.\r\nThose in the BadNews group then played the game for 15 minutes, whereas those\r\nin the Control group played Tetris. Then all once again gave reliability and\r\nconfidence ratings for the 18 tweets.\r\ndecade earlier, had been one of several outside experts invited to scrutinize\r\nthe laboratory and experimental procedures of parapsychology researcher\r\nCharles Honorton. Bem not only judged them adequate, but joined the research\r\neffort and became a coauthor with Honorton. Bem and Honorton (1994) first\r\nreviewed early ganzfeld studies and described how the experimental procedure\r\nhad been improved to reduce the chance that results could be influenced by\r\nvarious possible biases, or leakages of information from sender to receiver.\r\nFor example, the randomization procedure was carried out automatically by\r\ncomputer, and all stimuli were presented under computer control. Bem and\r\nHonorton then presented data from studies conducted with the improved\r\nprocedure. Table 13.1 presents basic data from 10 studies reported by Bem and\r\nHonorton (1994). Participants each made a single judgment, so in Pilot 1, for\r\nexample, 22 participants responded, with 8 of them giving a correct response.\r\nThree pilot studies helped refine the procedures, then four studies used\r\nnovice receivers. Study 5 used 20 students of music, drama, or dance as\r\nreceivers, in response to suggestions that creative people might be more\r\nlikely to show telepathy. Studies 6 and 7 used receivers who had participated\r\nin an earlier study. The proportion of hits expected by chance is .25, and\r\nTable 13.1 shows that all but Study 1 found proportions higher than .25.\r\nwho identified as female. Data is Subjective Wellbeing abd Body Satisfaction.\r\nwho identified as male. Data is Subjective Wellbeing abd Body Satisfaction.\r\nReported are measures of Subjective Wellbeing abd Body Satisfaction. — esci_document_data","title":"also promote kind, altruistic behavior. Brethel-Haurwitz and Marsh (2014)\r\nexamined this idea by collecting data on U.S. states. A Gallup poll in 2010\r\nwas used to measure each state’s well-being index, a measure of mean\r\nhappiness for the state’s residents on a scale from 0 to 100. Next, a kidney\r\ndonation database for 1999–2010 was used to figure out each state’s rate\r\n(number of donations per 1 million people) of non-directed kidney\r\ndonations—that’s giving one kidney to a stranger, an extremely generous and\r\naltruistic thing to do!\r\ntheir classic study, Jacowitz and Kahneman (1995) asked participants to\r\nestimate how many babies are born each day in the United States. Participants\r\nwere given either a low anchor (“more than 100 babies/day”) or a high anchor\r\n(“less than 50,000 babies/day”). Those who saw the low anchor estimated many\r\nfewer births/day than those who saw the high anchor, which suggests that the\r\nwording can have a profound influence. The correct answer, as it happens, is\r\n~11,000 births/day in 2014. To investigate the extent that these results are\r\nreplicable, the Many Labs project repeated this classic study at many\r\ndifferent labs around the world. You can find the summary data for 30 of\r\nthese labs in the Anchor Estimate ma data file\r\nnumerous other issues can be highly damaging, but are thriving in this social\r\nmedia age. Trying to debunk a conspiracy theory by presenting facts and\r\nevidence often doesn’t work, alas. Psychological inoculation, also similar to\r\nprebunking, presents a mild form of misinformation, preferably with\r\nexplanation, in the hope of building resistance to real-life fake news—a sort\r\nof vaccine for fake news. The Bad News game is a spin-off from research on\r\npsychological inoculation. Basol et al. (2020) assessed the possible\r\neffectiveness of this game as a fake news vaccine. At getbadnews.com you can\r\nclick “About” for information, or just start playing the game— it’s easy and\r\nmaybe even fun. You encounter mock Twitter (now X) fake news messages that\r\nillustrate common strategies for making fake news memorable or believable.\r\nYou make choices between messages and decide which ones to “forward” as you\r\ntry to spread fake news while building your credibility score and number of\r\n“followers”—rather like real life for a conspiracy theorist wanting to spread\r\nthe word. Compete with your friends for credibility and number of followers.\r\nBasol’s online participants first saw 18 fictitious fake news tweets and\r\nrated each for reliability (accuracy, believability), and also rated their\r\nconfidence in that reliability rating. Both ratings were on a 1 to 7 scale.\r\nThose in the BadNews group then played the game for 15 minutes, whereas those\r\nin the Control group played Tetris. Then all once again gave reliability and\r\nconfidence ratings for the 18 tweets.\r\ndecade earlier, had been one of several outside experts invited to scrutinize\r\nthe laboratory and experimental procedures of parapsychology researcher\r\nCharles Honorton. Bem not only judged them adequate, but joined the research\r\neffort and became a coauthor with Honorton. Bem and Honorton (1994) first\r\nreviewed early ganzfeld studies and described how the experimental procedure\r\nhad been improved to reduce the chance that results could be influenced by\r\nvarious possible biases, or leakages of information from sender to receiver.\r\nFor example, the randomization procedure was carried out automatically by\r\ncomputer, and all stimuli were presented under computer control. Bem and\r\nHonorton then presented data from studies conducted with the improved\r\nprocedure. Table 13.1 presents basic data from 10 studies reported by Bem and\r\nHonorton (1994). Participants each made a single judgment, so in Pilot 1, for\r\nexample, 22 participants responded, with 8 of them giving a correct response.\r\nThree pilot studies helped refine the procedures, then four studies used\r\nnovice receivers. Study 5 used 20 students of music, drama, or dance as\r\nreceivers, in response to suggestions that creative people might be more\r\nlikely to show telepathy. Studies 6 and 7 used receivers who had participated\r\nin an earlier study. The proportion of hits expected by chance is .25, and\r\nTable 13.1 shows that all but Study 1 found proportions higher than .25.\r\nwho identified as female. Data is Subjective Wellbeing abd Body Satisfaction.\r\nwho identified as male. Data is Subjective Wellbeing abd Body Satisfaction.\r\nReported are measures of Subjective Wellbeing abd Body Satisfaction. — esci_document_data","text":"also promote kind, altruistic behavior. Brethel-Haurwitz Marsh (2014) examined idea collecting data U.S. states. Gallup poll 2010 used measure state’s well-index, measure mean happiness state’s residents scale 0 100. Next, kidney donation database 1999–2010 used figure state’s rate (number donations per 1 million people) non-directed kidney donations—’s giving one kidney stranger, extremely generous altruistic thing ! classic study, Jacowitz Kahneman (1995) asked participants estimate many babies born day United States. Participants given either low anchor (“100 babies/day”) high anchor (“less 50,000 babies/day”). saw low anchor estimated many fewer births/day saw high anchor, suggests wording can profound influence. correct answer, happens, ~11,000 births/day 2014. investigate extent results replicable, Many Labs project repeated classic study many different labs around world. can find summary data 30 labs Anchor Estimate ma data file numerous issues can highly damaging, thriving social media age. Trying debunk conspiracy theory presenting facts evidence often doesn’t work, alas. Psychological inoculation, also similar prebunking, presents mild form misinformation, preferably explanation, hope building resistance real-life fake news—sort vaccine fake news. Bad News game spin-research psychological inoculation. Basol et al. (2020) assessed possible effectiveness game fake news vaccine. getbadnews.com can click “” information, just start playing game— ’s easy maybe even fun. encounter mock Twitter (now X) fake news messages illustrate common strategies making fake news memorable believable. make choices messages decide ones “forward” try spread fake news building credibility score number “followers”—rather like real life conspiracy theorist wanting spread word. Compete friends credibility number followers. Basol’s online participants first saw 18 fictitious fake news tweets rated reliability (accuracy, believability), also rated confidence reliability rating. ratings 1 7 scale. BadNews group played game 15 minutes, whereas Control group played Tetris. gave reliability confidence ratings 18 tweets. decade earlier, one several outside experts invited scrutinize laboratory experimental procedures parapsychology researcher Charles Honorton. Bem judged adequate, joined research effort became coauthor Honorton. Bem Honorton (1994) first reviewed early ganzfeld studies described experimental procedure improved reduce chance results influenced various possible biases, leakages information sender receiver. example, randomization procedure carried automatically computer, stimuli presented computer control. Bem Honorton presented data studies conducted improved procedure. Table 13.1 presents basic data 10 studies reported Bem Honorton (1994). Participants made single judgment, Pilot 1, example, 22 participants responded, 8 giving correct response. Three pilot studies helped refine procedures, four studies used novice receivers. Study 5 used 20 students music, drama, dance receivers, response suggestions creative people might likely show telepathy. Studies 6 7 used receivers participated earlier study. proportion hits expected chance .25, Table 13.1 shows Study 1 found proportions higher .25. identified female. Data Subjective Wellbeing abd Body Satisfaction. identified male. Data Subjective Wellbeing abd Body Satisfaction. Reported measures Subjective Wellbeing abd Body Satisfaction.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/esci_document_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"also promote kind, altruistic behavior. Brethel-Haurwitz and Marsh (2014)\r\nexamined this idea by collecting data on U.S. states. A Gallup poll in 2010\r\nwas used to measure each state’s well-being index, a measure of mean\r\nhappiness for the state’s residents on a scale from 0 to 100. Next, a kidney\r\ndonation database for 1999–2010 was used to figure out each state’s rate\r\n(number of donations per 1 million people) of non-directed kidney\r\ndonations—that’s giving one kidney to a stranger, an extremely generous and\r\naltruistic thing to do!\r\ntheir classic study, Jacowitz and Kahneman (1995) asked participants to\r\nestimate how many babies are born each day in the United States. Participants\r\nwere given either a low anchor (“more than 100 babies/day”) or a high anchor\r\n(“less than 50,000 babies/day”). Those who saw the low anchor estimated many\r\nfewer births/day than those who saw the high anchor, which suggests that the\r\nwording can have a profound influence. The correct answer, as it happens, is\r\n~11,000 births/day in 2014. To investigate the extent that these results are\r\nreplicable, the Many Labs project repeated this classic study at many\r\ndifferent labs around the world. You can find the summary data for 30 of\r\nthese labs in the Anchor Estimate ma data file\r\nnumerous other issues can be highly damaging, but are thriving in this social\r\nmedia age. Trying to debunk a conspiracy theory by presenting facts and\r\nevidence often doesn’t work, alas. Psychological inoculation, also similar to\r\nprebunking, presents a mild form of misinformation, preferably with\r\nexplanation, in the hope of building resistance to real-life fake news—a sort\r\nof vaccine for fake news. The Bad News game is a spin-off from research on\r\npsychological inoculation. Basol et al. (2020) assessed the possible\r\neffectiveness of this game as a fake news vaccine. At getbadnews.com you can\r\nclick “About” for information, or just start playing the game— it’s easy and\r\nmaybe even fun. You encounter mock Twitter (now X) fake news messages that\r\nillustrate common strategies for making fake news memorable or believable.\r\nYou make choices between messages and decide which ones to “forward” as you\r\ntry to spread fake news while building your credibility score and number of\r\n“followers”—rather like real life for a conspiracy theorist wanting to spread\r\nthe word. Compete with your friends for credibility and number of followers.\r\nBasol’s online participants first saw 18 fictitious fake news tweets and\r\nrated each for reliability (accuracy, believability), and also rated their\r\nconfidence in that reliability rating. Both ratings were on a 1 to 7 scale.\r\nThose in the BadNews group then played the game for 15 minutes, whereas those\r\nin the Control group played Tetris. Then all once again gave reliability and\r\nconfidence ratings for the 18 tweets.\r\ndecade earlier, had been one of several outside experts invited to scrutinize\r\nthe laboratory and experimental procedures of parapsychology researcher\r\nCharles Honorton. Bem not only judged them adequate, but joined the research\r\neffort and became a coauthor with Honorton. Bem and Honorton (1994) first\r\nreviewed early ganzfeld studies and described how the experimental procedure\r\nhad been improved to reduce the chance that results could be influenced by\r\nvarious possible biases, or leakages of information from sender to receiver.\r\nFor example, the randomization procedure was carried out automatically by\r\ncomputer, and all stimuli were presented under computer control. Bem and\r\nHonorton then presented data from studies conducted with the improved\r\nprocedure. Table 13.1 presents basic data from 10 studies reported by Bem and\r\nHonorton (1994). Participants each made a single judgment, so in Pilot 1, for\r\nexample, 22 participants responded, with 8 of them giving a correct response.\r\nThree pilot studies helped refine the procedures, then four studies used\r\nnovice receivers. Study 5 used 20 students of music, drama, or dance as\r\nreceivers, in response to suggestions that creative people might be more\r\nlikely to show telepathy. Studies 6 and 7 used receivers who had participated\r\nin an earlier study. The proportion of hits expected by chance is .25, and\r\nTable 13.1 shows that all but Study 1 found proportions higher than .25.\r\nwho identified as female. Data is Subjective Wellbeing abd Body Satisfaction.\r\nwho identified as male. Data is Subjective Wellbeing abd Body Satisfaction.\r\nReported are measures of Subjective Wellbeing abd Body Satisfaction. — esci_document_data","text":"","code":"esci_document_data(save_files = FALSE)"},{"path":"https://rcalinjageman.github.io/esci/reference/esci_plot_difference_axis_x.html","id":null,"dir":"Reference","previous_headings":"","what":"Add a difference axis to the x axis of an esci forest plot — esci_plot_difference_axis_x","title":"Add a difference axis to the x axis of an esci forest plot — esci_plot_difference_axis_x","text":"esci_plot_difference_axis_x can used redraw difference axis forest plot created plot_meta. must pass plot returned plot_meta effect size table containing estimated difference.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/esci_plot_difference_axis_x.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add a difference axis to the x axis of an esci forest plot — esci_plot_difference_axis_x","text":"","code":"esci_plot_difference_axis_x( myplot, difference_table, dlim = c(NA, NA), d_n.breaks = NULL, d_lab = NULL )"},{"path":"https://rcalinjageman.github.io/esci/reference/esci_plot_difference_axis_x.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add a difference axis to the x axis of an esci forest plot — esci_plot_difference_axis_x","text":"myplot required ggplot2 plot returned plot_meta function difference_table required data frame esci-estimate difference-based effect size dlim Optional 2-item vector provide lower uppper boundaries difference axis. Defaults c(NA, NA) auto-set boundaries. d_n.breaks Optional numeric > 2 suggest number breaks difference axis; defaults NULL case number breaks handled automatically ggplot d_lab Optional character serve label difference axis; defaults NULL","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_correlation.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_correlation","title":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_correlation","text":"estimate_correlation suitable design two continuous variables. estimates linear correlation two variables (Pearson's r). can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_correlation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_correlation","text":"","code":"estimate_correlation( data = NULL, x = NULL, y = NULL, r = NULL, n = NULL, x_variable_name = \"My x variable\", y_variable_name = \"My y variable\", conf_level = 0.95, save_raw_data = TRUE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_correlation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_correlation","text":"data raw data - data frame tibble x raw data - column name outcome variable, vector numeric data y raw data - column name outcome variable, vector numeric data r summary data - pearson's r correlation coefficient n summary data - Sample size, integer > 0 x_variable_name Optional friendly name x variable. Defaults 'x variable' outcome variable column name data frame passed. y_variable_name Optional friendly name y variable. Defaults 'y variable' outcome variable column name data frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. save_raw_data raw data; defaults TRUE; set FALSE save memory returning raw data estimate object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_correlation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_correlation","text":"Returns object class esci_estimate","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_correlation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_correlation","text":"generate estimate function, can visualize plot_correlation() can test hypotheses test_correlation(). addition, can use plot_scatter() visualize raw data conduct regression analysis returns predicted Y' values given X value. estimated correlation statpsych::ci.cor(), uses Fisher r--z approach.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_magnitude.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a continuous variable with no grouping (single-group design) — estimate_magnitude","title":"Estimates for a continuous variable with no grouping (single-group design) — estimate_magnitude","text":"estimate_magnitude suitable single group design continuous outcome variable. estimates population mean population median (raw data ) confidence intervals. can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_magnitude.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a continuous variable with no grouping (single-group design) — estimate_magnitude","text":"","code":"estimate_magnitude( data = NULL, outcome_variable = NULL, mean = NULL, sd = NULL, n = NULL, outcome_variable_name = \"My outcome variable\", conf_level = 0.95, save_raw_data = TRUE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_magnitude.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a continuous variable with no grouping (single-group design) — estimate_magnitude","text":"data raw data - data frame tibble outcome_variable raw data - column name outcome variable, vector numeric data mean summary data - numeric representing mean outcome variable sd summary data - numeric > 0, standard deviation outcome variable n summary data - integer > 0, sample size outcome variable outcome_variable_name Optional friendly name outcome variable. Defaults 'outcome variable' outcome variable column name data frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. save_raw_data raw data; defaults TRUE; set FALSE save memory returning raw data estimate object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_magnitude.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a continuous variable with no grouping (single-group design) — estimate_magnitude","text":"Returns object class esci_estimate overview outcome_variable_name - mean - mean_LL - mean_UL - median - median_LL - median_UL - sd - min - max - q1 - q3 - n - missing - df - mean_SE - median_SE - es_mean outcome_variable_name - effect - effect_size - LL - UL - SE - df - ta_LL - ta_UL - es_median outcome_variable_name - effect - effect_size - LL - UL - SE - df - ta_LL - ta_UL - raw_data grouping_variable - outcome_variable -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_magnitude.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a continuous variable with no grouping (single-group design) — estimate_magnitude","text":"Reach function place one-sample t-test z-test. generate estimate function, can visualize plot_magnitude(). want compare sample known value reference, use estimate_mdiff_one(). estimated mean statpsych::ci.mean1(). estimated median statpsych::ci.median1()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_magnitude.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates for a continuous variable with no grouping (single-group design) — estimate_magnitude","text":"","code":"# From raw data data(\"data_penlaptop1\") estimate <- esci::estimate_magnitude( data = data_penlaptop1[data_penlaptop1$condition == \"Pen\", ], outcome_variable = transcription ) estimate #> Analysis of raw data: #> Data frame = data #> Outcome variable(s) = transcription #> #> ---Overview--- #> outcome_variable_name mean mean_LL mean_UL median median_LL median_UL #> 1 transcription 8.811765 7.154642 10.46889 8.6 6.5 11.1 #> sd min max q1 q3 n missing df mean_SE median_SE #> 1 4.749339 1 20.1 5.2 11.275 34 0 33 0.8145049 1.021201 #> #> -- es_mean -- #> outcome_variable_name effect effect_size LL UL SE #> 1 transcription transcription 8.811765 7.154642 10.46889 0.8145049 #> df ta_LL ta_UL #> 1 33 7.746606 9.876923 #> #> -- es_median -- #> outcome_variable_name effect effect_size LL UL SE df ta_LL #> 1 transcription transcription 8.6 6.5 11.1 1.021201 33 7.1 #> ta_UL #> 1 10.8 #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimate plot_magnitude(estimate) } # From summary data mymean <- 24.5 mysd <- 3.65 myn <- 40 estimate <- esci::estimate_magnitude( mean = mymean, sd = mysd, n = myn ) estimate #> Analysis of raw data: #> Outcome variable(s) = My outcome variable #> #> ---Overview--- #> outcome_variable_name mean mean_LL mean_UL sd n df mean_SE #> 1 My outcome variable 24.5 23.33267 25.66733 3.65 40 39 0.5771157 #> #> -- es_mean -- #> outcome_variable_name effect effect_size LL UL #> 1 My outcome variable My outcome variable 24.5 23.33267 25.66733 #> SE df ta_LL ta_UL #> 1 0.5771157 39 23.74765 25.25235 #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimate plot_mdiff(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_between.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a 2x2 between-subjects design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_between","title":"Estimates for a 2x2 between-subjects design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_between","text":"estimate_mdiff_2x2_between suitable 2x2 -subjects design continuous outcome variable. estimates main effect, simple effects first factor, interaction. can express estimates mean differences, standardized mean differences (Cohen's d), median differences (raw data ). can pass raw data summary data (summary data return medians).","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_between.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a 2x2 between-subjects design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_between","text":"","code":"estimate_mdiff_2x2_between( data = NULL, outcome_variable = NULL, grouping_variable_A = NULL, grouping_variable_B = NULL, means = NULL, sds = NULL, ns = NULL, grouping_variable_A_levels = NULL, grouping_variable_B_levels = NULL, outcome_variable_name = \"My outcome variable\", grouping_variable_A_name = \"A\", grouping_variable_B_name = \"A\", conf_level = 0.95, assume_equal_variance = FALSE, save_raw_data = TRUE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_between.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a 2x2 between-subjects design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_between","text":"data raw data - data frame tibble outcome_variable raw data - column name outcome variable, vector numeric data grouping_variable_A raw data - column name grouping variable, vector group names, 2 levels allowed grouping_variable_B raw data - column name grouping variable, vector group names, 2 levels allowed means summary data - vector 4 means: A1B1, A1B2, A2B1, A2B2 sds summary data - vector 4 standard deviations, order ns summary data - vector 4 sample sizes grouping_variable_A_levels summary data - optional vector 2 group labels grouping_variable_B_levels summary data - optional vector 2 group labels outcome_variable_name Optional friendly name outcome variable. Defaults 'outcome variable' outcome variable column name data frame passed. grouping_variable_A_name Optional friendly name grouping variable. Defaults '' grouping variable column name data.frame passed. grouping_variable_B_name Optional friendly name grouping variable. Defaults '' grouping variable column name data.frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. assume_equal_variance Defaults FALSE save_raw_data raw data; defaults TRUE; set FALSE save memory returning raw data estimate object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_between.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a 2x2 between-subjects design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_between","text":"Returns object class esci_estimate es_mean_difference type - outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - SE - df - ta_LL - ta_UL - effect_type - effects_complex - es_median_difference type - outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - SE - ta_LL - ta_UL - effect_type - effects_complex - es_smd outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - numerator - denominator - SE - df - d_biased - effect_type - effects_complex - overview outcome_variable_name - grouping_variable_name - grouping_variable_level - mean - mean_LL - mean_UL - median - median_LL - median_UL - sd - min - max - q1 - q3 - n - missing - df - mean_SE - median_SE - raw_data grouping_variable - outcome_variable - grouping_variable_A - grouping_variable_B -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_between.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a 2x2 between-subjects design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_between","text":"Reach function place 2x2 -subjects ANOVA. generate estimate function, can visualize plot_mdiff() can visualize interaction specifically plot_interaction(). can test hypotheses test_mdiff(). estimated mean differences statpsych::ci.2x2.mean.bs(). estimated SMDs statpsych::ci.2x2.stdmean.bs(). estimated median differences statpsych::ci.2x2.median.bs()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_between.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates for a 2x2 between-subjects design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_between","text":"","code":"# From summary data means <- c(1.5, 1.14, 1.38, 2.22) sds <- c(1.38, .96,1.5, 1.68) ns <- c(26, 26, 25, 26) grouping_variable_A_levels <- c(\"Evening\", \"Morning\") grouping_variable_B_levels <- c(\"Sleep\", \"No Sleep\") estimates <- estimate_mdiff_2x2_between( means = means, sds = sds, ns = ns, grouping_variable_A_levels = grouping_variable_A_levels, grouping_variable_B_levels = grouping_variable_B_levels, grouping_variable_A_name = \"Testing Time\", grouping_variable_B_name = \"Rest\", outcome_variable_name = \"False Memory Score\", assume_equal_variance = TRUE ) if (FALSE) { # To visualize the estimated mean difference for the interaction plot_mdiff(estimate$interaction, effect_size = \"mean\") # To visualize the interaction as a line plot plot_interaction(estimates) # Same but with fan effect representing each simple-effect CI plot_interaction(estimates, show_CI = TRUE) }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_mixed.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a 2x2 mixed factorial design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_mixed","title":"Estimates for a 2x2 mixed factorial design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_mixed","text":"estimate_mdiff_2x2_mixed suitable 2x2 mixed-factorial design continuous outcome variable. estimates main effect, simple effects repeated-measures factor, interaction. can express estimates mean differences. function accepts raw data . Standardized mean differences (yet) available; stay tuned. Median differences also yet available.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_mixed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a 2x2 mixed factorial design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_mixed","text":"","code":"estimate_mdiff_2x2_mixed( data, outcome_variable_level1, outcome_variable_level2, grouping_variable, outcome_variable_name = \"My outcome variable\", repeated_measures_name = \"Time\", conf_level = 0.95, save_raw_data = TRUE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_mixed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a 2x2 mixed factorial design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_mixed","text":"data raw data - dataframe tibble outcome_variable_level1 column name outcome variable level 1 repeated-measures factor outcome_variable_level2 column name outcome variable level 2 repeated-measures factor grouping_variable column name grouping variable; 2 levels allowed; must factor outcome_variable_name Optional friendly name outcome variable. Defaults 'outcome variable' outcome variable column name data frame passed. repeated_measures_name Optional friendly name repeated measures factor. Defaults 'Time' conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. save_raw_data raw data; defaults TRUE; set FALSE save memory returning raw data estimate object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_mixed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a 2x2 mixed factorial design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_mixed","text":"Returnsobject class esci_estimate es_mean_difference type - outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - SE - df - ta_LL - ta_UL - effect_type - effects_complex - t - p - es_smd outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - numerator - denominator - SE - df - d_biased - effect_type - effects_complex - overview outcome_variable_name - grouping_variable_name - grouping_variable_level - mean - mean_LL - mean_UL - median - median_LL - median_UL - sd - min - max - q1 - q3 - n - missing - df - mean_SE - median_SE - raw_data grouping_variable - outcome_variable - grouping_variable_A - grouping_variable_B - paired -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_mixed.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a 2x2 mixed factorial design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_mixed","text":"Reach function place 2x2 mixed-factorial ANOVA. generate estimate function, can visualize plot_mdiff() can visualize interaction specifically plot_interaction(). can test hypotheses test_mdiff(). estimated mean differences statpsych::ci.2x2.mean.mixed().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_mixed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates for a 2x2 mixed factorial design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_mixed","text":"","code":"# From raw data (summary data mode not available for this function) example_data <- data.frame( pretest = c( 19, 18, 19, 20, 17, 16, 16, 10, 12, 9, 13, 15 ), posttest = c( 18, 19, 20, 17, 20, 16, 19, 16, 16, 14, 16, 18 ), condition = as.factor( c( rep(\"Control\", times = 6), rep(\"Treated\", times = 6) ) ) ) estimates <- esci::estimate_mdiff_2x2_mixed( data = example_data, outcome_variable_level1 = pretest, outcome_variable_level2 = posttest, grouping_variable = condition, repeated_measures_name = \"Time\" ) if (FALSE) { # To visualize the estimated mean difference for the interaction plot_mdiff(estimate$interaction, effect_size = \"mean\") }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_ind_contrast.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a multi-group design with a continuous outcome\r\nvariable — estimate_mdiff_ind_contrast","title":"Estimates for a multi-group design with a continuous outcome\r\nvariable — estimate_mdiff_ind_contrast","text":"estimate_mdiff_ind_contrast suitable multi-group design (subjects) continuous outcome variable. accepts user-defined set contrast weights allows estimation 1-df contrast. can express estimates mean differences, standardized mean differences (Cohen's d) median differences (raw data ). can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_ind_contrast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a multi-group design with a continuous outcome\r\nvariable — estimate_mdiff_ind_contrast","text":"","code":"estimate_mdiff_ind_contrast( data = NULL, outcome_variable = NULL, grouping_variable = NULL, means = NULL, sds = NULL, ns = NULL, contrast = NULL, grouping_variable_levels = NULL, outcome_variable_name = \"My outcome variable\", grouping_variable_name = \"My grouping variable\", conf_level = 0.95, assume_equal_variance = FALSE, save_raw_data = TRUE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_ind_contrast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a multi-group design with a continuous outcome\r\nvariable — estimate_mdiff_ind_contrast","text":"data raw data - data frame tibble outcome_variable raw data - column name outcome variable, vector numeric data grouping_variable raw data - column name grouping variable, vector group names means summary data - vector 2 means sds summary data - vector standard deviations, length means ns summary data - vector sample sizes, length means contrast vector group weights, length number groups. grouping_variable_levels summary data - optional vector group labels, length means outcome_variable_name Optional friendly name outcome variable. Defaults 'outcome variable' outcome variable column name data frame passed. grouping_variable_name Optional friendly name grouping variable. Defaults 'grouping variable' grouping variable column name data.frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. assume_equal_variance Defaults FALSE save_raw_data raw data; defaults TRUE; set FALSE save memory returning raw data estimate object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_ind_contrast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a multi-group design with a continuous outcome\r\nvariable — estimate_mdiff_ind_contrast","text":"Returnsobject class esci_estimate es_mean_difference type - outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - SE - df - ta_LL - ta_UL - es_median_difference type - outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - SE - ta_LL - ta_UL - es_smd outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - numerator - denominator - SE - df - d_biased - overview outcome_variable_name - grouping_variable_name - grouping_variable_level - mean - mean_LL - mean_UL - median - median_LL - median_UL - sd - min - max - q1 - q3 - n - missing - df - mean_SE - median_SE - raw_data grouping_variable - outcome_variable -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_ind_contrast.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a multi-group design with a continuous outcome\r\nvariable — estimate_mdiff_ind_contrast","text":"Reach function place one-way ANOVA. generate estimate function, can visualize plot_mdiff() can test hypotheses test_mdiff(). estimated mean differences statpsych::ci.lc.mean.bs(). estimated SMDs CI_smd_ind_contrast() relies statpsych::ci.lc.stdmean.bs() unless 2 groups. estimated median differences statpsych::ci.lc.median.bs()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_ind_contrast.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates for a multi-group design with a continuous outcome\r\nvariable — estimate_mdiff_ind_contrast","text":"","code":"# From summary data data(\"data_halagappa\") estimate <- estimate_mdiff_ind_contrast( means = data_halagappa$Mean, sds = data_halagappa$SD, ns = data_halagappa$n, grouping_variable_levels = as.character(data_halagappa$Groups), assume_equal_variance = TRUE, contrast = c( \"NFree10\" = 1/3, \"AFree10\" = 1/3, \"ADiet10\" = -1/3, \"NFree17\" = -1/3, \"AFree17\" = 1/3, \"ADiet17\" = -1/3 ), grouping_variable_name = \"Diet\", outcome_variable_name = \"% time near target\" ) estimate #> Analysis of raw data: #> Outcome variable(s) = % time near target #> Grouping variable(s) = Diet #> #> ---Overview--- #> outcome_variable_name grouping_variable_name grouping_variable_level mean #> 1 % time near target Diet NFree10 37.5 #> 2 % time near target Diet AFree10 31.9 #> 3 % time near target Diet ADiet10 41.2 #> 4 % time near target Diet NFree17 33.4 #> 5 % time near target Diet AFree17 29.9 #> 6 % time near target Diet ADiet17 38.3 #> mean_LL mean_UL sd n df mean_SE #> 1 32.32519 42.67481 10.0 19 108 2.610673 #> 2 26.72519 37.07481 13.5 19 108 2.610673 #> 3 36.02519 46.37481 14.8 19 108 2.610673 #> 4 28.22519 38.57481 10.0 19 108 2.610673 #> 5 24.72519 35.07481 8.7 19 108 2.610673 #> 6 33.12519 43.47481 10.0 19 108 2.610673 #> #> -- es_mean_difference -- #> type outcome_variable_name grouping_variable_name #> 1 Comparison % time near target Diet #> 2 Reference % time near target Diet #> 3 Difference % time near target Diet #> effect #> 1 (NFree10 and AFree10 and AFree17) #> 2 (ADiet10 and NFree17 and ADiet17) #> 3 (NFree10 and AFree10 and AFree17) ‒ (ADiet10 and NFree17 and ADiet17) #> effect_size LL UL SE df ta_LL ta_UL #> 1 33.100000 30.112324 36.0876762 1.507273 108 30.599306 35.6006939 #> 2 37.633333 34.645657 40.6210095 1.507273 108 35.132639 40.1340273 #> 3 -4.533333 -8.758546 -0.3081211 2.131606 108 -8.069849 -0.9968181 #> #> -- es_smd -- #> outcome_variable_name grouping_variable_name #> 1 % time near target Diet #> effect #> 1 (NFree10 and AFree10 and AFree17) ‒ (ADiet10 and NFree17 and ADiet17) #> effect_size LL UL numerator denominator SE df #> 1 -0.3955976 -0.7655978 -0.02380328 -4.533333 11.37966 0.1892368 108 #> d_biased #> 1 -0.3983716 #> This standardized mean difference is called d_s because the standardizer used was s_p. d_s has been corrected for bias. Correction for bias can be important when df < 50. See the rightmost column for the biased value. #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimated mean difference plot_mdiff(estimate, effect_size = \"mean\") }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_one.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a single-group design with a continuous outcome\r\nvariable compared to a reference or population value — estimate_mdiff_one","title":"Estimates for a single-group design with a continuous outcome\r\nvariable compared to a reference or population value — estimate_mdiff_one","text":"estimate_mdiff_one suitable single-group design continuous outcome variable compared reference population value. can express estimates mean differences, standardized mean differences (Cohen's d) median differences (raw data ). can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_one.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a single-group design with a continuous outcome\r\nvariable compared to a reference or population value — estimate_mdiff_one","text":"","code":"estimate_mdiff_one( data = NULL, outcome_variable = NULL, comparison_mean = NULL, comparison_sd = NULL, comparison_n = NULL, reference_mean = 0, outcome_variable_name = \"My outcome variable\", conf_level = 0.95, save_raw_data = TRUE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_one.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a single-group design with a continuous outcome\r\nvariable compared to a reference or population value — estimate_mdiff_one","text":"data raw data - data frame tibble outcome_variable raw data - column name outcome variable, vector numeric data comparison_mean summary data, numeric comparison_sd summary data, numeric > 0 comparison_n summary data, numeric integer > 0 reference_mean Reference value, defaults 0 outcome_variable_name Optional friendly name outcome variable. Defaults 'outcome variable' outcome variable column name data frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. save_raw_data raw data; defaults TRUE; set FALSE save memory returning raw data estimate object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_one.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a single-group design with a continuous outcome\r\nvariable compared to a reference or population value — estimate_mdiff_one","text":"Returns object class esci_estimate overview outcome_variable_name - mean - mean_LL - mean_UL - median - median_LL - median_UL - sd - min - max - q1 - q3 - n - missing - df - mean_SE - median_SE - es_mean outcome_variable_name - effect - effect_size - LL - UL - SE - df - ta_LL - ta_UL - es_median outcome_variable_name - effect - effect_size - LL - UL - SE - df - ta_LL - ta_UL - raw_data grouping_variable - outcome_variable - es_mean_difference outcome_variable_name - effect - effect_size - LL - UL - SE - df - ta_LL - ta_UL - type - es_median_difference outcome_variable_name - effect - effect_size - LL - UL - SE - df - ta_LL - ta_UL - type - es_smd outcome_variable_name - effect - effect_size - LL - UL - numerator - denominator - SE - df - d_biased -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_one.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a single-group design with a continuous outcome\r\nvariable compared to a reference or population value — estimate_mdiff_one","text":"Reach function place z-test one-sample t-test. generate estimate function, can visualize plot_mdiff() can test hypotheses test_mdiff(). estimated mean differences statpsych::ci.mean1(). estimated SMDs CI_smd_one(). estimated median differences statpsych::ci.median1()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_one.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates for a single-group design with a continuous outcome\r\nvariable compared to a reference or population value — estimate_mdiff_one","text":"","code":"# From raw data data(\"data_penlaptop1\") estimate <- esci::estimate_mdiff_one( data = data_penlaptop1[data_penlaptop1$condition == \"Pen\", ], outcome_variable = transcription, reference_mean = 10 ) estimate #> Analysis of raw data: #> Data frame = data #> Outcome variable(s) = transcription #> #> ---Overview--- #> outcome_variable_name mean mean_LL mean_UL median median_LL median_UL #> 1 transcription 8.811765 7.154642 10.46889 8.6 6.5 11.1 #> sd min max q1 q3 n missing df mean_SE median_SE #> 1 4.749339 1 20.1 5.2 11.275 34 0 33 0.8145049 1.021201 #> #> -- es_mean -- #> outcome_variable_name effect effect_size LL UL SE #> 1 transcription transcription 8.811765 7.154642 10.46889 0.8145049 #> df ta_LL ta_UL #> 1 33 7.746606 9.876923 #> #> -- es_median -- #> outcome_variable_name effect effect_size LL UL SE df ta_LL #> 1 transcription transcription 8.6 6.5 11.1 1.021201 33 7.1 #> ta_UL #> 1 10.8 #> #> -- es_mean_difference -- #> outcome_variable_name effect effect_size LL #> 1 transcription transcription 8.811765 7.154642 #> 2 transcription Reference value 10.000000 NA #> 3 transcription transcription ‒ Reference value -1.188235 -2.845358 #> UL SE df ta_LL ta_UL type #> 1 10.4688874 0.8145049 33 7.746606 9.876923 Comparison #> 2 NA NA NA 7.746606 9.876923 Reference #> 3 0.4688874 0.8145049 33 -2.253394 -0.123077 Difference #> #> -- es_median_difference -- #> outcome_variable_name effect effect_size LL UL #> 1 transcription transcription 8.6 6.5 11.1 #> 2 transcription Reference value 10.0 NA NA #> 3 transcription transcription ‒ Reference value -1.4 -3.5 1.1 #> SE df ta_LL ta_UL type #> 1 1.021201 33 7.1 10.8 Comparison #> 2 NA NA NA NA Reference #> 3 1.021201 33 -2.9 0.8 Difference #> #> -- es_smd -- #> outcome_variable_name effect effect_size LL #> 1 transcription transcription ‒ Reference value -0.2444527 -0.5838873 #> UL numerator denominator SE df d_biased #> 1 0.09856549 -1.188235 4.749339 0.1740983 33 -0.2501896 #> This standardized mean difference is called d_1 because the standardizer used was s. d_1 has been corrected for bias. Correction for bias can be important when df < 50. See the rightmost column for the biased value. #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimate plot_mdiff(estimate) } if (FALSE) { # To conduct a hypothesis test test_mdiff(estimate, effect_size = \"mean\", rope = c(-2, 2)) } # From summary data mymean <- 12.09 mysd <- 5.52 myn <- 103 estimate <- esci::estimate_mdiff_one( comparison_mean = mymean, comparison_sd = mysd, comparison_n = myn, reference_mean = 12 ) estimate #> Analysis of raw data: #> Outcome variable(s) = My outcome variable #> #> ---Overview--- #> outcome_variable_name mean mean_LL mean_UL sd n df mean_SE #> 1 My outcome variable 12.09 11.01117 13.16883 5.52 103 102 0.5439018 #> #> -- es_mean -- #> outcome_variable_name effect effect_size LL UL #> 1 My outcome variable My outcome variable 12.09 11.01117 13.16883 #> SE df ta_LL ta_UL #> 1 0.5439018 102 11.38842 12.79158 #> #> -- es_mean_difference -- #> outcome_variable_name effect effect_size #> 1 My outcome variable My outcome variable 12.09 #> 2 My outcome variable Reference value 12.00 #> 3 My outcome variable My outcome variable ‒ Reference value 0.09 #> LL UL SE df ta_LL ta_UL type #> 1 11.0111734 13.168827 0.5439018 102 11.3884176 12.7915824 Comparison #> 2 NA NA NA NA 11.3884176 12.7915824 Reference #> 3 -0.9888266 1.168827 0.5439018 102 -0.6115824 0.7915824 Difference #> #> -- es_smd -- #> outcome_variable_name effect effect_size #> 1 My outcome variable My outcome variable ‒ Reference value 0.01618412 #> LL UL numerator denominator SE df d_biased #> 1 -0.1769892 0.2092782 0.09 5.52 0.09853943 102 0.01630435 #> This standardized mean difference is called d_1 because the standardizer used was s. d_1 has been corrected for bias. Correction for bias can be important when df < 50. See the rightmost column for the biased value. #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimate plot_mdiff(estimate) } if (FALSE) { # To conduct a hypothesis test test_mdiff(estimate, effect_size = \"mean\", rope = c(-2, 2)) }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_paired.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a repeated-measures study with two measures of a continuous\r\nvariable — estimate_mdiff_paired","title":"Estimates for a repeated-measures study with two measures of a continuous\r\nvariable — estimate_mdiff_paired","text":"estimate_mdiff_paired suitable simple paired design continuous outcome variable. provides estimates CIs population mean difference repeated measures, standardized mean difference (SMD; Cohen's d) repeated measures, median difference repeated measures (raw data ). can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_paired.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a repeated-measures study with two measures of a continuous\r\nvariable — estimate_mdiff_paired","text":"","code":"estimate_mdiff_paired( data = NULL, comparison_measure = NULL, reference_measure = NULL, comparison_mean = NULL, comparison_sd = NULL, reference_mean = NULL, reference_sd = NULL, n = NULL, correlation = NULL, comparison_measure_name = \"Comparison measure\", reference_measure_name = \"Reference measure\", conf_level = 0.95, save_raw_data = TRUE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_paired.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a repeated-measures study with two measures of a continuous\r\nvariable — estimate_mdiff_paired","text":"data raw data - data frame tibble comparison_measure raw data - column name comparison measure outcome variable, vector numeric data reference_measure raw data - column name reference measure outcome variable, vector numeric data comparison_mean summary data, numeric comparison_sd summary data, numeric > 0 reference_mean summary data, numeric reference_sd summary data, numeric > 0 n summary data, numeric integer > 0 correlation summary data, correlation measures, numeric > -1 < 1 comparison_measure_name summary data - optional character label comparison measure. Defaults 'Comparison measure' reference_measure_name summary data - optional character label reference measure. Defaults 'Reference measure' conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. save_raw_data raw data; defaults TRUE; set FALSE save memory returning raw data estimate object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_paired.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a repeated-measures study with two measures of a continuous\r\nvariable — estimate_mdiff_paired","text":"Returns object class esci_estimate overview outcome_variable_name - mean - mean_LL - mean_UL - median - median_LL - median_UL - sd - min - max - q1 - q3 - n - missing - df - mean_SE - median_SE - es_mean_difference type - comparison_measure_name - reference_measure_name - effect - effect_size - LL - UL - SE - df - ta_LL - ta_UL - es_smd comparison_measure_name - reference_measure_name - effect - effect_size - LL - UL - numerator - denominator - SE - d_biased - df - es_r x_variable_name - y_variable_name - effect - effect_size - LL - UL - SE - n - df - ta_LL - ta_UL - es_median_difference type - comparison_measure_name - reference_measure_name - effect - effect_size - LL - UL - SE - ta_LL - ta_UL - es_mean_ratio comparison_measure_name - reference_measure_name - effect - effect_size - LL - UL - comparison_mean - reference_mean - es_median_ratio comparison_measure_name - reference_measure_name - effect - effect_size - LL - UL - comparison_median - reference_median - raw_data comparison_measure - reference_measure -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_paired.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a repeated-measures study with two measures of a continuous\r\nvariable — estimate_mdiff_paired","text":"Reach function place paired-samples t-test. generate estimate function, can visualize plot_mdiff() can test hypotheses test_mdiff(). estimated mean differences statpsych::ci.mean.ps(). estimated SMDs CI_smd_ind_contrast(). estimated median differences statpsych::ci.median.ps().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_paired.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates for a repeated-measures study with two measures of a continuous\r\nvariable — estimate_mdiff_paired","text":"","code":"# From raw data data(\"data_thomason_1\") estimate <- esci::estimate_mdiff_paired( data = data_thomason_1, comparison_measure = Posttest, reference_measure = Pretest ) estimate #> Analysis of raw data: #> Data frame = structure(list(\"Participant ID\" = structure(1:12, levels = c(\"1\", #> Analysis of raw data: #> Data frame = \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\"), class = \"factor\"), #> Analysis of raw data: #> Data frame = Pretest = c(13, 12, 12, 9, 14, 17, 14, 9, 6, 7, 11, 15), #> Analysis of raw data: #> Data frame = Posttest = c(14, 13, 16, 12, 15, 18, 13, 10, 10, 8, 14, 16 #> Analysis of raw data: #> Data frame = )), removedRows = list(), addedRows = list(list(start = 0L, #> Analysis of raw data: #> Data frame = end = 11L)), transforms = list(), row.names = c(NA, 12L), class = \"data.frame\") #> #> ---Overview--- #> outcome_variable_name mean mean_LL mean_UL median median_LL median_UL #> 1 Pretest 11.58333 9.476776 13.68989 12.0 9 14 #> 2 Posttest 13.25000 11.410019 15.08998 13.5 10 16 #> sd min max q1 q3 n missing df mean_SE median_SE #> 1 3.315483 6 17 9.0 14.00 12 0 11 0.9570974 1.208488 #> 2 2.895922 8 18 11.5 15.25 12 0 11 0.8359806 1.450186 #> #> -- es_mean_difference -- #> type comparison_measure_name reference_measure_name effect #> 1 Comparison Posttest Pretest Posttest #> 2 Reference Posttest Pretest Pretest #> 11 Difference Posttest Pretest Posttest ‒ Pretest #> effect_size LL UL SE df ta_LL ta_UL #> 1 13.250000 11.410019 15.089981 0.8359806 11 11.748675 14.751325 #> 2 11.583333 9.476776 13.689890 0.9570974 11 9.864497 13.302170 #> 11 1.666667 0.715218 2.618115 0.4322831 11 0.890336 2.442997 #> #> -- es_smd -- #> comparison_measure_name reference_measure_name effect effect_size #> 1 Posttest Pretest Posttest - Pretest 0.5105098 #> LL UL numerator denominator SE d_biased df #> 1 0.1806393 0.8902152 1.666667 3.112779 0.1810176 0.5105098 11 #> This standardized mean difference is called d_average because the standardizer used was s_average. d_average has been corrected for bias. Correction for bias can be important when df < 50. #> #> -- es_r -- #> x_variable_name y_variable_name effect effect_size LL #> 1 Pretest Posttest Pretest and Posttest 0.8923908 0.62894 #> UL SE n df ta_LL ta_UL #> 1 0.967157 0.06139936 12 10 0.6882891 0.9596343 #> #> -- es_median_difference -- #> type comparison_measure_name reference_measure_name effect #> Comparison Posttest Pretest Posttest #> 1 Reference Posttest Pretest Pretest #> 2 Difference Posttest Pretest Posttest ‒ Pretest #> effect_size LL UL SE ta_LL ta_UL #> 13.5 10.000000 16.000000 1.450186 10.000000 16.000000 #> 1 12.0 9.000000 14.000000 1.208488 9.000000 14.000000 #> 2 1.5 -1.589665 4.589665 1.576389 -1.589665 4.589665 #> #> -- es_mean_ratio -- #> comparison_measure_name reference_measure_name effect effect_size #> 1 Posttest Pretest Posttest / Pretest 1.143885 #> LL UL comparison_mean reference_mean #> 1 1.05031 1.245797 13.25 11.58333 #> [1] \"\\n For more information on this effect size, see Bonett & Price (2020) doi: 10.3102/1076998620934125.\" #> #> -- es_median_ratio -- #> comparison_measure_name reference_measure_name effect effect_size #> 1 Posttest Pretest Posttest / Pretest 1.125 #> LL UL comparison_median reference_median #> 1 0.8723319 1.450853 13.5 12 #> [1] \"\\n For more information on this effect size, see Bonett & Price (2020) doi: 10.3102/1076998620934125.\" #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimated median difference (raw data only) plot_mdiff(estimate, effect_size = \"median\") } sd1 <- 4.28 sd2 <- 3.4 sdiff <- 2.13 cor <- (sd1^2 + sd2^2 - sdiff^2) / (2*sd1*sd2) estimate <- estimate_mdiff_paired( comparison_mean = 14.25, comparison_sd = 4.28, reference_mean = 12.88, reference_sd = 3.4, n = 16, correlation = 0.87072223749, comparison_measure_name = \"After\", reference_measure_name = \"Before\" ) estimate #> Analysis of raw data: #> #> ---Overview--- #> outcome_variable_name mean mean_LL mean_UL sd n df mean_SE #> 1 Before 12.88 11.06827 14.69173 3.40 16 15 0.85 #> 2 After 14.25 11.96935 16.53065 4.28 16 15 1.07 #> #> -- es_mean_difference -- #> type comparison_measure_name reference_measure_name effect #> 1 Comparison After Before After #> 2 Reference After Before Before #> 11 Difference After Before After ‒ Before #> effect_size LL UL SE df ta_LL ta_UL #> 1 14.25 11.9693490 16.530651 1.0700 15 12.3742361 16.125764 #> 2 12.88 11.0682679 14.691732 0.8500 15 11.3899072 14.370093 #> 11 1.37 0.2350031 2.504997 0.5325 15 0.4365007 2.303499 #> #> -- es_smd -- #> comparison_measure_name reference_measure_name effect effect_size #> 1 After Before After - Before 0.3424327 #> LL UL numerator denominator SE d_biased df #> 1 0.05110995 0.6577932 1.37 3.865126 0.154769 0.3424327 15 #> This standardized mean difference is called d_average because the standardizer used was s_average. d_average has been corrected for bias. Correction for bias can be important when df < 50. #> #> -- es_r -- #> x_variable_name y_variable_name effect effect_size LL #> 1 Before After Before and After 0.8707222 0.6430976 #> UL SE n df ta_LL ta_UL #> 1 0.9518053 0.06244354 16 14 0.6915049 0.9428632 #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimated mean difference # plot_mdiff(estimate, effect_size = \"mean\") }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_two.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a two-group study with a continuous outcome variable — estimate_mdiff_two","title":"Estimates for a two-group study with a continuous outcome variable — estimate_mdiff_two","text":"estimate_mdiff_two suitable simple two-group design continuous outcome variable. provides estimates CIs population mean difference repeated measures, standardized mean difference (SMD; Cohen's d) repeated measures, median difference repeated measures (raw data ). can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_two.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a two-group study with a continuous outcome variable — estimate_mdiff_two","text":"","code":"estimate_mdiff_two( data = NULL, outcome_variable = NULL, grouping_variable = NULL, comparison_mean = NULL, comparison_sd = NULL, comparison_n = NULL, reference_mean = NULL, reference_sd = NULL, reference_n = NULL, grouping_variable_levels = NULL, outcome_variable_name = \"My outcome variable\", grouping_variable_name = \"My grouping variable\", conf_level = 0.95, assume_equal_variance = FALSE, save_raw_data = TRUE, switch_comparison_order = FALSE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_two.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a two-group study with a continuous outcome variable — estimate_mdiff_two","text":"data raw data - datavframe tibble outcome_variable raw data - column name outcome variable, vector numeric data grouping_variable raw data - column name grouping variable, vector group names comparison_mean summary data, numeric comparison_sd summary data, numeric > 0 comparison_n summary data, numeric integer > 0 reference_mean summary data, numeric reference_sd summary data, numeric > 0 reference_n summary data, numeric integer > 0 grouping_variable_levels summary data - optional vector 2 group labels outcome_variable_name Optional friendly name outcome variable. Defaults 'outcome variable' outcome variable column name data frame passed. grouping_variable_name Optional friendly name grouping variable. Defaults 'grouping variable' grouping variable column name data.frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. assume_equal_variance Defaults FALSE save_raw_data raw data; defaults TRUE; set FALSE save memory returning raw data estimate object switch_comparison_order Defaults FALSE","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_two.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a two-group study with a continuous outcome variable — estimate_mdiff_two","text":"Returnsobject class esci_estimate es_mean_difference type - outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - SE - df - ta_LL - ta_UL - es_median_difference type - outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - SE - ta_LL - ta_UL - es_smd outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - numerator - denominator - SE - df - d_biased - es_mean_ratio outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - comparison_mean - reference_mean - es_median_ratio outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - comparison_median - reference_median - overview outcome_variable_name - grouping_variable_name - grouping_variable_level - mean - mean_LL - mean_UL - median - median_LL - median_UL - sd - min - max - q1 - q3 - n - missing - df - mean_SE - median_SE - raw_data grouping_variable - outcome_variable -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_two.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a two-group study with a continuous outcome variable — estimate_mdiff_two","text":"Reach function place independent-samples t-test. generate estimate function, can visualize plot_mdiff() can test hypotheses test_mdiff(). estimated mean differences statpsych::ci.mean2(). estimated SMDs CI_smd_ind_contrast(). estimated median differences statpsych::ci.median2().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_two.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates for a two-group study with a continuous outcome variable — estimate_mdiff_two","text":"","code":"# From summary data estimate <- estimate_mdiff_two( comparison_mean = 12.09, comparison_sd = 5.52, comparison_n = 103, reference_mean = 6.88, reference_sd = 4.22, reference_n = 48, grouping_variable_levels = c(\"Ref-Laptop\", \"Comp-Pen\"), outcome_variable_name = \"% Transcription\", grouping_variable_name = \"Note-taking type\", assume_equal_variance = TRUE ) if (FALSE) { # To visualize the estimated median difference (raw data only) plot_mdiff(estimate, effect_size = \"median\") } # From raw data data(\"data_penlaptop1\") estimate <- esci::estimate_mdiff_two( data = data_penlaptop1, outcome_variable = transcription, grouping_variable = condition, switch_comparison_order = TRUE, assume_equal_variance = TRUE ) if (FALSE) { # To visualize the estimated mean difference plot_mdiff(estimate, effect_size = \"mean\") }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_ind_contrast.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a multi-group study with a categorical outcome\r\nvariable — estimate_pdiff_ind_contrast","title":"Estimates for a multi-group study with a categorical outcome\r\nvariable — estimate_pdiff_ind_contrast","text":"estimate_pdiff_ind_contrast suitable multi-group design (subjects) categorical outcome variable. accepts user-defined set contrast weights allows estimation 1-df contrast. can express estimates difference proportions odds ratio (2-group designs ). can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_ind_contrast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a multi-group study with a categorical outcome\r\nvariable — estimate_pdiff_ind_contrast","text":"","code":"estimate_pdiff_ind_contrast( data = NULL, outcome_variable = NULL, grouping_variable = NULL, cases = NULL, ns = NULL, contrast = NULL, case_label = 1, grouping_variable_levels = NULL, outcome_variable_name = \"My outcome variable\", grouping_variable_name = \"My grouping variable\", conf_level = 0.95, count_NA = FALSE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_ind_contrast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a multi-group study with a categorical outcome\r\nvariable — estimate_pdiff_ind_contrast","text":"data raw data - data frame tibble outcome_variable raw data - column name outcome variable factor, vector factor grouping_variable raw data - column name grouping variable factor, vector factor cases summary data - numeric vector 2 event counts, integer >= 0 ns summary data - numeric vector sample sizes, length counts, integer >= corresponding event count contrast vector group weights, length number groups. case_label optional numeric character label summary data, used label defaults 'Affected'. raw data, used specify level used proportion. grouping_variable_levels summary data - optional vector group labels, length cases outcome_variable_name Optional friendly name outcome variable. Defaults 'outcome variable' outcome variable column name data frame passed. grouping_variable_name Optional friendly name grouping variable. Defaults 'grouping variable' grouping variable column name data.frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. count_NA Logical count NAs (TRUE) total N (FALSE)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_ind_contrast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a multi-group study with a categorical outcome\r\nvariable — estimate_pdiff_ind_contrast","text":"Returns object class esci_estimate es_proportion_difference type - outcome_variable_name - case_label - grouping_variable_name - effect - effect_size - LL - UL - SE - effect_size_adjusted - ta_LL - ta_UL - es_odds_ratio outcome_variable_name - case_label - grouping_variable_name - effect - effect_size - SE - LL - UL - ta_LL - ta_UL - overview grouping_variable_name - grouping_variable_level - outcome_variable_name - outcome_variable_level - cases - n - P - P_LL - P_UL - P_SE - P_adjusted - ta_LL - ta_UL - es_phi grouping_variable_name - outcome_variable_name - effect - effect_size - SE - LL - UL -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_ind_contrast.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a multi-group study with a categorical outcome\r\nvariable — estimate_pdiff_ind_contrast","text":"generate estimate function, can visualize plot_mdiff() can test hypotheses test_mdiff(). estimated proportion differences statpsych::ci.lc.prop.bs(). estimated odds ratios (returned) statpsych::ci.oddsratio().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_ind_contrast.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates for a multi-group study with a categorical outcome\r\nvariable — estimate_pdiff_ind_contrast","text":"","code":"estimate <- estimate_pdiff_ind_contrast( cases = c(78, 10), ns = c(252, 20), case_label = \"egocentric\", grouping_variable_levels = c(\"Original\", \"Replication\"), contrast = c(-1, 1), conf_level = 0.95 ) if (FALSE) { # To visualize the estimate plot_proportion(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_one.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a single-group design with a categorical outcome\r\nvariable compared to a reference or population value. — estimate_pdiff_one","title":"Estimates for a single-group design with a categorical outcome\r\nvariable compared to a reference or population value. — estimate_pdiff_one","text":"estimate_pdiff_one suitable single-group design (subjects) categorical outcome variable. calculates effect sizes respect reference population proportion (default value 0). returns estimated difference proportion reference/population value. can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_one.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a single-group design with a categorical outcome\r\nvariable compared to a reference or population value. — estimate_pdiff_one","text":"","code":"estimate_pdiff_one( data = NULL, outcome_variable = NULL, comparison_cases = NULL, comparison_n = NULL, reference_p = 0, case_label = 1, outcome_variable_name = \"My outcome variable\", conf_level = 0.95, count_NA = FALSE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_one.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a single-group design with a categorical outcome\r\nvariable compared to a reference or population value. — estimate_pdiff_one","text":"data raw data - dataframe tibble outcome_variable raw data - column name outcome variable, must factor, vector factor comparison_cases summary data, numeric integer > 0 comparison_n summary data, numeric integer >= count reference_p Reference proportion, numeric >=0 <=1 case_label optional numeric character label count level. outcome_variable_name Optional friendly name outcome variable. Defaults 'outcome variable' outcome variable column name data frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. count_NA Logical count NAs (TRUE) total N (FALSE)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_one.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a single-group design with a categorical outcome\r\nvariable compared to a reference or population value. — estimate_pdiff_one","text":"Returns object class esci_estimate overview outcome_variable_name - outcome_variable_level - cases - n - P - P_LL - P_UL - P_SE - P_adjusted - ta_LL - ta_UL - es_proportion_difference outcome_variable_name - case_label - effect - effect_size - LL - UL - SE - effect_size_adjusted - ta_LL - ta_UL - cases - n - type -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_one.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a single-group design with a categorical outcome\r\nvariable compared to a reference or population value. — estimate_pdiff_one","text":"generate estimate function, can visualize plot_pdiff() can test hypotheses test_pdiff(). estimated proportion differences statpsych::ci.prop1().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_one.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates for a single-group design with a categorical outcome\r\nvariable compared to a reference or population value. — estimate_pdiff_one","text":"","code":"estimate <- estimate_pdiff_one( comparison_cases = 8, comparison_n = 22, reference_p = 0.5 ) if (FALSE) { # To visualize the estimate plot_proportion(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_paired.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a repeated-measures study with two measures of a categorical\r\nvariable — estimate_pdiff_paired","title":"Estimates for a repeated-measures study with two measures of a categorical\r\nvariable — estimate_pdiff_paired","text":"estimate_pdiff_paired suitable simple paired design categorical outcome variable. provides estimates CIs population proportion difference repeated measures. can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_paired.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a repeated-measures study with two measures of a categorical\r\nvariable — estimate_pdiff_paired","text":"","code":"estimate_pdiff_paired( data = NULL, comparison_measure = NULL, reference_measure = NULL, cases_consistent = NULL, cases_inconsistent = NULL, not_cases_consistent = NULL, not_cases_inconsistent = NULL, case_label = 1, not_case_label = NULL, comparison_measure_name = \"Comparison measure\", reference_measure_name = \"Reference measure\", conf_level = 0.95, count_NA = FALSE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_paired.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a repeated-measures study with two measures of a categorical\r\nvariable — estimate_pdiff_paired","text":"data raw data - datavframe tibble comparison_measure raw data - comparison measure, factor. Can column name data frame vector. reference_measure raw data - reference measure, factor. Can column name data frame vector. cases_consistent Count cases measure 1 also cases measure 2; measure 1 = 0, measure 2 = 0; cell 0_0 cases_inconsistent Count cases measure 1 cases measure 2; measure 1 = 0, measure 2 = 1; cell 0_1 not_cases_consistent Count cases measure 1 also cases measure 2; measure 1 = 1, measure 2 = 1, cell 1_1 not_cases_inconsistent Count cases measure 1 cases measure 2; measure 1 = 1, measure 2 = 0, cell 1_0 case_label optional numeric character label case level. not_case_label optional numeric character label case level. comparison_measure_name summary data - optional character label comparison measure. Defaults 'Comparison measure' reference_measure_name summary data - optional character label reference measure. Defaults 'Reference measure' conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. count_NA Logical count NAs (TRUE) total N (FALSE)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_paired.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a repeated-measures study with two measures of a categorical\r\nvariable — estimate_pdiff_paired","text":"Returns object class esci_estimate","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_paired.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a repeated-measures study with two measures of a categorical\r\nvariable — estimate_pdiff_paired","text":"generate estimate function, can visualize plot_pdiff() can test hypotheses test_pdiff(). estimated proportion differences statpsych::ci.prop.ps().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_two.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a two-group study with a categorical outcome variable — estimate_pdiff_two","title":"Estimates for a two-group study with a categorical outcome variable — estimate_pdiff_two","text":"estimate_pdiff_two suitable simple two-group design categorical outcome variable. provides estimates CIs difference proportions two groups, odds ratio, phi. can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_two.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a two-group study with a categorical outcome variable — estimate_pdiff_two","text":"","code":"estimate_pdiff_two( data = NULL, outcome_variable = NULL, grouping_variable = NULL, comparison_cases = NULL, comparison_n = NULL, reference_cases = NULL, reference_n = NULL, case_label = 1, not_case_label = NULL, grouping_variable_levels = NULL, outcome_variable_name = \"My outcome variable\", grouping_variable_name = \"My grouping variable\", conf_level = 0.95, count_NA = FALSE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_two.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a two-group study with a categorical outcome variable — estimate_pdiff_two","text":"data raw data - data frame tibble outcome_variable raw data - column name outcome variable factor, vector factor grouping_variable raw data - column name grouping variable factor, vector factor comparison_cases summary data, numeric integer >= 0 comparison_n summary data, numeric integer >= comparison_events reference_cases summary data, numeric integer >= 0 reference_n summary data, numeric integer >= reference_events case_label optional numeric character label case level. not_case_label optional numeric character label case level. grouping_variable_levels summary data - optional vector 2 group labels outcome_variable_name Optional friendly name outcome variable. Defaults 'outcome variable' outcome variable column name data frame passed. grouping_variable_name Optional friendly name grouping variable. Defaults 'grouping variable' grouping variable column name data.frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. count_NA Logical count NAs (TRUE) total N (FALSE)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_two.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a two-group study with a categorical outcome variable — estimate_pdiff_two","text":"Returnsobject class esci_estimate es_proportion_difference type - outcome_variable_name - case_label - grouping_variable_name - effect - effect_size - LL - UL - SE - effect_size_adjusted - ta_LL - ta_UL - es_odds_ratio outcome_variable_name - case_label - grouping_variable_name - effect - effect_size - SE - LL - UL - ta_LL - ta_UL - overview grouping_variable_name - grouping_variable_level - outcome_variable_name - outcome_variable_level - cases - n - P - P_LL - P_UL - P_SE - P_adjusted - ta_LL - ta_UL - es_phi grouping_variable_name - outcome_variable_name - effect - effect_size - SE - LL - UL -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_two.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a two-group study with a categorical outcome variable — estimate_pdiff_two","text":"generate estimate function, can visualize plot_mdiff() can test hypotheses test_mdiff(). estimated mean differences statpsych::ci.prop2(). estimated odds ratio statpsych::ci.oddsratio(). estimated correlation (phi) statpsych::ci.phi().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_two.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates for a two-group study with a categorical outcome variable — estimate_pdiff_two","text":"","code":"estimate <- estimate_pdiff_two( comparison_cases = 10, comparison_n = 20, reference_cases = 78, reference_n = 252, grouping_variable_levels = c(\"Original\", \"Replication\"), conf_level = 0.95 ) if (FALSE) { # To visualize the estimate plot_pdiff(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a categorical variable with no grouping (single-group design) — estimate_proportion","title":"Estimates for a categorical variable with no grouping (single-group design) — estimate_proportion","text":"estimate_proportion suitable single group design categorical outcome variable. estimates population proportion frequency level outcome variable, confidence intervals. can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a categorical variable with no grouping (single-group design) — estimate_proportion","text":"","code":"estimate_proportion( data = NULL, outcome_variable = NULL, cases = NULL, case_label = 1, outcome_variable_levels = NULL, outcome_variable_name = \"My outcome variable\", conf_level = 0.95, count_NA = FALSE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_proportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a categorical variable with no grouping (single-group design) — estimate_proportion","text":"data raw data - data frame tibble outcome_variable raw data - column name outcome variable, must factor, vector factor cases summary data - vector cases case_label numeric string indicating level factor estimate. Defaults 1, meaning first level analyzed outcome_variable_levels summary data - optional vector 2 characters indicating name count level name count level. Defaults \"Affected\" \"Affected\" outcome_variable_name Optional friendly name outcome variable. Defaults 'outcome variable' outcome variable column name data frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. count_NA Logical count NAs (TRUE) total N (FALSE)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a categorical variable with no grouping (single-group design) — estimate_proportion","text":"Returns object class esci_estimate overview outcome_variable_name - outcome_variable_level - cases - n - P - P_LL - P_UL - P_SE - P_adjusted - ta_LL - ta_UL - es_proportion outcome_variable_name - case_label - effect - effect_size - LL - UL - SE - effect_size_adjusted - ta_LL - ta_UL - cases - n -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_proportion.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a categorical variable with no grouping (single-group design) — estimate_proportion","text":"generate estimate function, can visualize plot_proportion(). want compare estimate known value reference, use estimate_pdiff_one(). estimated proportions statpsych::ci.prop1().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_proportion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates for a categorical variable with no grouping (single-group design) — estimate_proportion","text":"","code":"estimate <- esci::estimate_proportion( cases = c(8, 22-8), outcome_variable_levels = c(\"Affected\", \"Not Affected\") ) if (FALSE) { # To visualize the estimate plot_proportion(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_r.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_r","title":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_r","text":"estimate_r suitable design two continuous variables. estimates linear correlation two variables (Pearson's r) confidence interval. can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_r.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_r","text":"","code":"estimate_r( data = NULL, x = NULL, y = NULL, r = NULL, n = NULL, x_variable_name = \"My x variable\", y_variable_name = \"My y variable\", conf_level = 0.95, save_raw_data = TRUE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_r.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_r","text":"data raw data - data frame tibble x raw data - column name outcome variable, vector numeric data y raw data - column name outcome variable, vector numeric data r summary data - pearson's r correlation coefficient n summary data - Sample size, integer > 0 x_variable_name Optional friendly name x variable. Defaults 'x variable' outcome variable column name data frame passed. y_variable_name Optional friendly name y variable. Defaults 'y variable' outcome variable column name data frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. save_raw_data raw data; defaults TRUE; set FALSE save memory returning raw data estimate object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_r.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_r","text":"Returns object class esci_estimate overview outcome_variable_name - mean - mean_LL - mean_UL - median - median_LL - median_UL - sd - min - max - q1 - q3 - n - missing - df - mean_SE - median_SE - es_r x_variable_name - y_variable_name - effect - effect_size - LL - UL - SE - n - df - ta_LL - ta_UL - regression component - values - LL - UL - raw_data x - y - fit - lwr - upr -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_r.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_r","text":"Reach function conduct simple linear correlation simple linear regression. generate estimate function, can visualize plot_correlation() can test hypotheses test_correlation(). addition, can use plot_scatter() visualize raw data conduct regression analysis r returns predicted Y' values given X value. estimated correlation statpsych::ci.cor(), uses Fisher r--z approach.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_r.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_r","text":"","code":"# From raw data thomason1 <- data.frame( ls_pre = c( 13, 12, 12, 9, 14, 17, 14, 9, 6, 7, 11, 15 ), ls_post = c( 14, 13, 16, 12, 15, 18, 13, 10, 10, 8, 14, 16 ) ) estimate <- esci::estimate_r( thomason1, ls_pre, ls_post ) estimate #> Analysis of raw data: #> Data frame = data #> #> ---Overview--- #> outcome_variable_name mean mean_LL mean_UL median median_LL median_UL #> 1 ls_pre 11.58333 9.476776 13.68989 12.0 9 14 #> 2 ls_post 13.25000 11.410019 15.08998 13.5 10 16 #> sd min max q1 q3 n missing df mean_SE median_SE #> 1 3.315483 6 17 9.0 14.00 12 0 11 0.9570974 1.208488 #> 2 2.895922 8 18 11.5 15.25 12 0 11 0.8359806 1.450186 #> #> -- es_r -- #> x_variable_name y_variable_name effect effect_size LL #> 1 ls_pre ls_post ls_pre and ls_post 0.8923908 0.62894 #> UL SE n df ta_LL ta_UL #> 1 0.967157 0.06139936 12 10 0.6882891 0.9596343 #> #> -- regression -- #> component values LL UL #> 1 Intercept (a) 4.2212267 0.8856544 7.556799 #> 2 Slope (b) 0.7794624 0.5017391 1.057186 #> [1] \"Ŷ = 4.221 + 0.7795*X\" #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. # To evaluate a hypothesis (interval null from -0.1 to 0.1): test_correlation(estimate, rope = c(-0.1, 0.1)) #> $properties #> $properties$effect_size_name #> [1] \"r\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] TRUE #> #> $properties$rope #> [1] -0.1 0.1 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name effect null_words #> 1 Nil Hypothesis Test ls_pre and ls_post ls_pre and ls_post 0.00 #> confidence LL UL CI #> 1 95 0.62894 0.967157 95% CI [0.62894, 0.967157] #> CI_compare t df p p_result #> 1 The 95% CI does not contain H_0 4.300635 10 1.703091e-05 p < 0.05 #> null_decision conclusion significant #> 1 Reject H_0 At α = 0.05, 0.00 is not a plausible value of Ͱ TRUE #> #> $interval_null #> test_type outcome_variable_name effect #> 1 Practical significance test ls_pre and ls_post ls_pre and ls_post #> rope confidence #> 1 (-0.10, 0.10) 95 #> CI #> 1 95% CI [0.62894, 0.967157]\\n90% CI [0.6882891, 0.9596343] #> rope_compare p_result conclusion #> 1 95% CI fully outside H_0 p < 0.05 At α = 0.05, conclude Ͱ is substantive #> significant #> 1 TRUE #> if (FALSE) { # To visualize the value of r plot_correlation(estimate) # To visualize the data (scatterplot) plot_scatter(estimate) # To visualize the data (scatterplot) and use regression to obtain Y' from X plot_scatter(estimate, predict_from_x = 10) } # From summary data estimate <- esci::estimate_r(r = 0.536, n = 50) estimate #> Analysis of summary data: #> #> -- es_r -- #> x_variable_name y_variable_name effect effect_size #> 1 My x variable My y variable My x variable and My y variable 0.536 #> LL UL SE n df ta_LL ta_UL #> 1 0.2978573 0.7058914 0.1018149 50 48 0.3391487 0.6820747 #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimate plot_correlation(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_rdiff_two.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates the difference in correlation for a design with two groups and\r\ntwo continuous outcome variables — estimate_rdiff_two","title":"Estimates the difference in correlation for a design with two groups and\r\ntwo continuous outcome variables — estimate_rdiff_two","text":"estimate_rdiff_two suitable simple two-group design two continuous outcome variables want estimate difference strength relationship two groups. estimate linear correlation (Pearson's r) group difference r, along confidence intervals. can pass raw data summary data. Returns effect sizes appropriate estimating linear relationship two quantitative variables","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_rdiff_two.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates the difference in correlation for a design with two groups and\r\ntwo continuous outcome variables — estimate_rdiff_two","text":"","code":"estimate_rdiff_two( data = NULL, x = NULL, y = NULL, grouping_variable = NULL, comparison_r = NULL, comparison_n = NULL, reference_r = NULL, reference_n = NULL, grouping_variable_levels = NULL, x_variable_name = \"My x variable\", y_variable_name = \"My y variable\", grouping_variable_name = \"My grouping variable\", conf_level = 0.95, save_raw_data = TRUE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_rdiff_two.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates the difference in correlation for a design with two groups and\r\ntwo continuous outcome variables — estimate_rdiff_two","text":"data raw data - dataframe tibble x raw data - column name outcome variable, vector numeric data y raw data - column name outcome variable, vector numeric data grouping_variable raw data, vector factor name factor column data comparison_r summary data, pearson's r correlation coefficient comparison_n summary data - integer > 0 reference_r summary data, pearson's r correlation coefficient reference_n summary data - integer > 0 grouping_variable_levels summary data - optional vector 2 group labels x_variable_name Optional friendly name x variable. Defaults 'x variable' outcome variable column name data frame passed. y_variable_name Optional friendly name y variable. Defaults 'y variable' outcome variable column name data frame passed. grouping_variable_name Optional friendly name grouping variable. Defaults 'grouping variable' grouping variable column name data.frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. save_raw_data raw data; defaults TRUE; set FALSE save memory returning raw data estimate object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_rdiff_two.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates the difference in correlation for a design with two groups and\r\ntwo continuous outcome variables — estimate_rdiff_two","text":"Returnsobject class esci_estimate overview outcome_variable_name - grouping_variable_name - grouping_variable_level - mean - mean_LL - mean_UL - median - median_LL - median_UL - sd - min - max - q1 - q3 - n - missing - df - mean_SE - median_SE - es_r_difference type - grouping_variable_name - grouping_variable_level - x_variable_name - y_variable_name - effect - effect_size - LL - UL - SE - n - df - ta_LL - ta_UL - rz - sem - z - p - es_r grouping_variable_name - grouping_variable_level - x_variable_name - y_variable_name - effect - effect_size - LL - UL - SE - n - df - ta_LL - ta_UL - raw_data x - y - grouping_variable -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_rdiff_two.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates the difference in correlation for a design with two groups and\r\ntwo continuous outcome variables — estimate_rdiff_two","text":"generate estimate function, can visualize plot_rdiff() can test hypotheses test_rdiff(). addition, can use plot_scatter() visualize raw data. estimated single-group r values statpsych::ci.cor(). difference r values statpsych::ci.cor2().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_rdiff_two.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates the difference in correlation for a design with two groups and\r\ntwo continuous outcome variables — estimate_rdiff_two","text":"","code":"# From summary data estimate <- esci::estimate_rdiff_two( comparison_r = .53, comparison_n = 45, reference_r = .41, reference_n = 59, grouping_variable_levels = c(\"Females\", \"Males\"), x_variable_name = \"Satisfaction with life\", y_variable_name = \"Body satisfaction\", grouping_variable_name = \"Gender\", conf_level = .95 ) estimate #> Analysis of summary data: #> #> -- es_r -- #> grouping_variable_name grouping_variable_level x_variable_name #> 1 Gender Females Satisfaction with life #> 2 Gender Males Satisfaction with life #> y_variable_name effect effect_size LL UL SE n df #> 1 Body satisfaction Females 0.41 0.1685419 0.6005378 0.1092338 59 57 #> 2 Body satisfaction Males 0.53 0.2744717 0.7096862 0.1084084 45 43 #> ta_LL ta_UL #> 1 0.2091420 0.5729339 #> 2 0.3188047 0.6847105 #> #> -- es_r_difference -- #> type grouping_variable_name grouping_variable_level #> 1 Comparison Gender Males #> 2 Reference Gender Females #> 3 Difference Gender Males - Females #> x_variable_name y_variable_name effect effect_size #> 1 Satisfaction with life Body satisfaction Males 0.53 #> 2 Satisfaction with life Body satisfaction Females 0.41 #> 3 Satisfaction with life Body satisfaction Males - Females 0.12 #> LL UL SE n df ta_LL ta_UL rz sem #> 1 0.2744717 0.7096862 0.1084084 45 43 0.3188047 0.6847105 0.5901452 0.1543033 #> 2 0.1685419 0.6005378 0.1092338 59 57 0.2091420 0.5729339 0.4356112 0.1336306 #> 3 -0.1987466 0.4209803 NA NA NA -0.1467413 0.3735336 0.1545339 0.2041241 #> z p #> 1 3.8245778 0.0001309964 #> 2 3.2598159 0.0011148455 #> 3 0.7570586 0.4490147644 #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. # To evaluate a hypothesis (interval null from -0.1 to 0.1): esci::test_rdiff(estimate) #> $properties #> $properties$effect_size_name #> [1] \"r\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] FALSE #> #> $properties$rope #> [1] 0 0 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name #> 1 Nil Hypothesis Test Satisfaction with life and Body satisfaction #> effect null_words confidence LL UL #> 1 Males - Females 0.00 95 -0.1987466 0.4209803 #> CI CI_compare t df p #> 1 95% CI [-0.1987466, 0.4209803] The 95% CI contains H_0 0.7570586 NA 0.4490148 #> p_result null_decision #> 1 p ≥ 0.05 Fail to reject H_0 #> conclusion significant #> 1 At α = 0.05, 0.00 remains a plausible value of Ͱ_diff FALSE #> if (FALSE) { # To visualize the values of r and their difference esci::plot_rdiff(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/geom_meta_diamond_h.html","id":null,"dir":"Reference","previous_headings":"","what":"Meta-analysis diamond — geom_meta_diamond_h","title":"Meta-analysis diamond — geom_meta_diamond_h","text":"geom_meta_diamond_h creates horizontal meta-analytic diamond defined x value (horizontal center diamond), xmin xmax values (horizontal ends diamond), y value (vertical placement diamond) height (vertical height diamond). Note use xmin xmax allows representation asymmetric confidence intervals geom.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/geom_meta_diamond_h.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Meta-analysis diamond — geom_meta_diamond_h","text":"","code":"geom_meta_diamond_h( mapping = NULL, data = NULL, stat = \"identity\", position = \"identity\", ..., na.rm = FALSE, show.legend = NA, inherit.aes = TRUE )"},{"path":"https://rcalinjageman.github.io/esci/reference/geom_meta_diamond_h.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Meta-analysis diamond — geom_meta_diamond_h","text":"mapping Set aesthetic mappings created aes(). specified inherit.aes = TRUE (default), combined default mapping top level plot. must supply mapping plot mapping. data data displayed layer. three options: NULL, default, data inherited plot data specified call ggplot(). data.frame, object, override plot data. objects fortified produce data frame. See fortify() variables created. function called single argument, plot data. return value must data.frame, used layer data. function can created formula (e.g. ~ head(.x, 10)). stat statistical transformation use data layer, either ggproto Geom subclass string naming stat stripped stat_ prefix (e.g. \"count\" rather \"stat_count\") position Position adjustment, either string naming adjustment (e.g. \"jitter\" use position_jitter), result call position adjustment function. Use latter need change settings adjustment. ... arguments passed geom. often aesthetics, used set aesthetic fixed value, like colour = \"red\" linewidth = 3 (see Aesthetics, ). ##Aesthetics ## geom_meta_diamond_h understands following aesthetics (required bold): x - horizontal center diamond y - vertical placement diamond xmin - left-side start diamond xmax - right-side start diamond height - vertical span diamond na.rm FALSE, default, missing values removed warning. TRUE, missing values silently removed. show.legend logical. layer included legends? NA, default, includes aesthetics mapped. FALSE never includes, TRUE always includes. can also named logical vector finely select aesthetics display. inherit.aes FALSE, overrides default aesthetics, rather combining . useful helper functions define data aesthetics inherit behaviour default plot specification, e.g. borders().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovicorrelation.html","id":null,"dir":"Reference","previous_headings":"","what":"Correlations: Single Group — jamovicorrelation","title":"Correlations: Single Group — jamovicorrelation","text":"Correlations: Single Group","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovicorrelation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Correlations: Single Group — jamovicorrelation","text":"","code":"jamovicorrelation( switch = \"from_raw\", data, x, y, r = \" \", n = \" \", x_variable_name = \"X variable\", y_variable_name = \"Y variable\", conf_level = 95, show_details = FALSE, do_regression = FALSE, show_line = FALSE, show_line_CI = FALSE, show_residuals = FALSE, show_PI = FALSE, show_mean_lines = FALSE, show_r = FALSE, plot_as_z = FALSE, predict_from_x = \" \", evaluate_hypotheses = FALSE, null_value = \"0\", null_boundary = \"0\", alpha = 0.05, null_color = \"#A40122\", es_plot_width = \"300\", es_plot_height = \"400\", sp_plot_width = \"650\", sp_plot_height = \"650\", ymin = \"-1\", ymax = \"1\", ybreaks = \"auto\", ylab = \"auto\", xlab = \"auto\", axis.text.y = \"14\", axis.title.y = \"15\", axis.text.x = \"14\", axis.title.x = \"15\", error_layout = \"none\", sp_ymin = \"auto\", sp_ymax = \"auto\", sp_ybreaks = \"auto\", sp_xmin = \"auto\", sp_xmax = \"auto\", sp_xbreaks = \"auto\", sp_ylab = \"auto\", sp_xlab = \"auto\", sp_axis.text.y = \"14\", sp_axis.title.y = \"15\", sp_axis.text.x = \"14\", sp_axis.title.x = \"15\", shape_summary = \"circle filled\", color_summary = \"#008DF9\", fill_summary = \"#008DF9\", size_summary = \"4\", alpha_summary = \"1\", linetype_summary = \"solid\", color_interval = \"black\", size_interval = \"3\", alpha_interval = \"1\", alpha_error = \"1\", fill_error = \"gray75\", sp_shape_raw_reference = \"circle filled\", sp_color_raw_reference = \"black\", sp_fill_raw_reference = \"#008DF9\", sp_size_raw_reference = \"3\", sp_alpha_raw_reference = \".25\", sp_linetype_summary_reference = \"solid\", sp_color_summary_reference = \"#008DF9\", sp_size_summary_reference = \"3\", sp_alpha_summary_reference = \".25\", sp_linetype_PI_reference = \"dotted\", sp_color_PI_reference = \"#E20134\", sp_size_PI_reference = \"2\", sp_alpha_PI_reference = \"1\", sp_linetype_residual_reference = \"solid\", sp_color_residual_reference = \"#E20134\", sp_size_residual_reference = \"1\", sp_alpha_residual_reference = \"1\", sp_prediction_label = \"5\", sp_prediction_color = \"#E20134\", sp_linetype_CI = \"solid\", sp_color_CI = \"#008DF9\", sp_size_CI = \"4\", sp_alpha_CI = \"1\", sp_linetype_ref = \"dotted\", sp_color_ref = \"gray60\", sp_size_ref = \"1\", sp_alpha_ref = \"1\", sp_linetype_PI = \"solid\", sp_color_PI = \"#E20134\", sp_size_PI = \"2\", sp_alpha_PI = \"1\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovicorrelation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Correlations: Single Group — jamovicorrelation","text":"switch . data . x . y . r . n . x_variable_name . y_variable_name . conf_level . show_details . do_regression . show_line . show_line_CI . show_residuals . show_PI . show_mean_lines . show_r . plot_as_z . predict_from_x . evaluate_hypotheses . null_value . null_boundary . alpha . null_color . es_plot_width . es_plot_height . sp_plot_width . sp_plot_height . ymin . ymax . ybreaks . ylab . xlab . axis.text.y . axis.title.y . axis.text.x . axis.title.x . error_layout . sp_ymin . sp_ymax . sp_ybreaks . sp_xmin . sp_xmax . sp_xbreaks . sp_ylab . sp_xlab . sp_axis.text.y . sp_axis.title.y . sp_axis.text.x . sp_axis.title.x . shape_summary . color_summary . fill_summary . size_summary . alpha_summary . linetype_summary . color_interval . size_interval . alpha_interval . alpha_error . fill_error . sp_shape_raw_reference . sp_color_raw_reference . sp_fill_raw_reference . sp_size_raw_reference . sp_alpha_raw_reference . sp_linetype_summary_reference . sp_color_summary_reference . sp_size_summary_reference . sp_alpha_summary_reference . sp_linetype_PI_reference . sp_color_PI_reference . sp_size_PI_reference . sp_alpha_PI_reference . sp_linetype_residual_reference . sp_color_residual_reference . sp_size_residual_reference . sp_alpha_residual_reference . sp_prediction_label . sp_prediction_color . sp_linetype_CI . sp_color_CI . sp_size_CI . sp_alpha_CI . sp_linetype_ref . sp_color_ref . sp_size_ref . sp_alpha_ref . sp_linetype_PI . sp_color_PI . sp_size_PI . sp_alpha_PI .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovicorrelation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Correlations: Single Group — jamovicorrelation","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$overview$asDF .data.frame(results$overview)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovidescribe.html","id":null,"dir":"Reference","previous_headings":"","what":"Describe — jamovidescribe","title":"Describe — jamovidescribe","text":"Describe","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovidescribe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Describe — jamovidescribe","text":"","code":"jamovidescribe( data, outcome_variable, show_details = FALSE, mark_mean = FALSE, mark_median = FALSE, mark_sd = FALSE, mark_quartiles = FALSE, mark_z_lines = FALSE, mark_percentile = \"0\", histogram_bins = \"12\", es_plot_width = \"500\", es_plot_height = \"400\", ymin = \"auto\", ymax = \"auto\", breaks = \"auto\", xmin = \"auto\", xmax = \"auto\", xbreaks = \"auto\", ylab = \"auto\", xlab = \"auto\", axis.text.y = \"14\", axis.title.y = \"15\", axis.text.x = \"14\", axis.title.x = \"15\", fill_regular = \"#008DF9\", fill_highlighted = \"#E20134\", color = \"black\", marker_size = \"4\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovidescribe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Describe — jamovidescribe","text":"data . outcome_variable . show_details . mark_mean . mark_median . mark_sd . mark_quartiles . mark_z_lines . mark_percentile . histogram_bins . es_plot_width . es_plot_height . ymin . ymax . breaks . xmin . xmax . xbreaks . ylab . xlab . axis.text.y . axis.title.y . axis.text.x . axis.title.x . fill_regular . fill_highlighted . color . marker_size .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovidescribe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Describe — jamovidescribe","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$overview$asDF .data.frame(results$overview)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimagnitude.html","id":null,"dir":"Reference","previous_headings":"","what":"Means and Medians: Single Group — jamovimagnitude","title":"Means and Medians: Single Group — jamovimagnitude","text":"Means Medians: Single Group","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimagnitude.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Means and Medians: Single Group — jamovimagnitude","text":"","code":"jamovimagnitude( switch = \"from_raw\", data, outcome_variable, mean = \"\", sd = \"\", n = \"\", outcome_variable_name = \"Outcome variable\", conf_level = 95, effect_size = \"mean\", show_details = FALSE, show_calculations = FALSE, evaluate_hypotheses = FALSE, null_value = \"0\", null_boundary = \"0\", alpha = 0.05, null_color = \"#A40122\", es_plot_width = \"300\", es_plot_height = \"400\", ymin = \"auto\", ymax = \"auto\", breaks = \"auto\", ylab = \"auto\", xlab = \"auto\", axis.text.y = \"14\", axis.title.y = \"15\", axis.text.x = \"14\", axis.title.x = \"15\", error_layout = \"halfeye\", error_scale = \"0.20\", error_nudge = \"0.3\", data_layout = \"random\", data_spread = \"0.25\", shape_raw = \"circle filled\", shape_summary = \"circle filled\", color_raw = \"#008DF9\", color_summary = \"#008DF9\", fill_raw = \"NA\", fill_summary = \"#008DF9\", size_raw = \"2\", size_summary = \"4\", alpha_raw = \"1\", alpha_summary = \"1\", linetype_summary = \"solid\", color_interval = \"black\", size_interval = \"3\", alpha_interval = \"1\", alpha_error = \"1\", fill_error = \"gray75\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimagnitude.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Means and Medians: Single Group — jamovimagnitude","text":"switch . data . outcome_variable . mean . sd . n . outcome_variable_name . conf_level . effect_size . show_details . show_calculations . evaluate_hypotheses . null_value . null_boundary . alpha . null_color . es_plot_width . es_plot_height . ymin . ymax . breaks . ylab . xlab . axis.text.y . axis.title.y . axis.text.x . axis.title.x . error_layout . error_scale . error_nudge . data_layout . data_spread . shape_raw . shape_summary . color_raw . color_summary . fill_raw . fill_summary . size_raw . size_summary . alpha_raw . alpha_summary . linetype_summary . color_interval . size_interval . alpha_interval . alpha_error . fill_error .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimagnitude.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Means and Medians: Single Group — jamovimagnitude","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$overview$asDF .data.frame(results$overview)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiff2x2.html","id":null,"dir":"Reference","previous_headings":"","what":"Means and Medians: 2x2 Factorial — jamovimdiff2x2","title":"Means and Medians: 2x2 Factorial — jamovimdiff2x2","text":"Means Medians: 2x2 Factorial","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiff2x2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Means and Medians: 2x2 Factorial — jamovimdiff2x2","text":"","code":"jamovimdiff2x2( design = \"fully_between\", switch = \"from_raw\", data, outcome_variable, grouping_variable_A, grouping_variable_B, outcome_variable_level1, outcome_variable_level2, outcome_variable_name_bs = \"My outcome variable\", grouping_variable, repeated_measures_name = \"Time\", outcome_variable_name = \"My outcome variable\", A1_label = \"A1 level\", A2_label = \"A2 level\", B1_label = \"B1 level\", B2_label = \"B2 level\", A_label = \"Variable A\", B_label = \"Variable B\", A1B1_mean = \" \", A1B1_sd = \" \", A1B1_n = \" \", A1B2_mean = \" \", A1B2_sd = \" \", A1B2_n = \" \", A2B1_mean = \" \", A2B1_sd = \" \", A2B1_n = \" \", A2B2_mean = \" \", A2B2_sd = \" \", A2B2_n = \" \", conf_level = 95, effect_size = \"mean_difference\", assume_equal_variance = TRUE, show_details = FALSE, show_interaction_plot = FALSE, show_CI = FALSE, evaluate_hypotheses = FALSE, null_value = \"0\", null_boundary = \"0\", rope_units = \"raw\", alpha = 0.05, null_color = \"#A40122\", es_plot_width = \"700\", es_plot_height = \"400\", ymin = \"auto\", ymax = \"auto\", ybreaks = \"auto\", ylab = \"auto\", xlab = \"auto\", axis.text.y = \"14\", axis.title.y = \"15\", axis.text.x = \"14\", axis.title.x = \"15\", simple_contrast_labels = FALSE, error_layout = \"halfeye\", error_scale = \"0.25\", error_nudge = \"0.5\", data_layout = \"random\", data_spread = \"0.20\", difference_axis_units = \"raw\", difference_axis_breaks = \"auto\", shape_raw_reference = \"circle filled\", shape_raw_comparison = \"circle filled\", shape_raw_unused = \"circle filled\", shape_summary_reference = \"circle filled\", shape_summary_comparison = \"circle filled\", shape_summary_unused = \"circle filled\", shape_summary_difference = \"triangle filled\", color_raw_reference = \"#008DF9\", color_raw_comparison = \"#009F81\", color_raw_unused = \"gray65\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#009F81\", color_summary_unused = \"gray65\", color_summary_difference = \"black\", fill_raw_reference = \"NA\", fill_raw_comparison = \"NA\", fill_raw_unused = \"NA\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#009F81\", fill_summary_unused = \"gray65\", fill_summary_difference = \"black\", size_raw_reference = \"2\", size_raw_comparison = \"2\", size_raw_unused = \"1\", size_summary_reference = \"4\", size_summary_comparison = \"4\", size_summary_unused = \"4\", size_summary_difference = \"4\", alpha_raw_reference = \"1\", alpha_raw_comparison = \"1\", alpha_raw_unused = \"1\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_unused = \"1\", alpha_summary_difference = \"1\", linetype_summary_reference = \"solid\", linetype_summary_comparison = \"solid\", linetype_summary_unused = \"solid\", linetype_summary_difference = \"solid\", color_interval_reference = \"black\", color_interval_comparison = \"black\", color_interval_unused = \"gray65\", color_interval_difference = \"black\", size_interval_reference = \"3\", size_interval_comparison = \"3\", size_interval_unused = \"3\", size_interval_difference = \"3\", alpha_interval_reference = \"1\", alpha_interval_comparison = \"1\", alpha_interval_unused = \"1\", alpha_interval_difference = \"1\", alpha_error_reference = \"1\", alpha_error_comparison = \"1\", alpha_error_unused = \"1\", alpha_error_difference = \"1\", fill_error_reference = \"gray75\", fill_error_comparison = \"gray75\", fill_error_unused = \"gray75\", fill_error_difference = \"gray75\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiff2x2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Means and Medians: 2x2 Factorial — jamovimdiff2x2","text":"design . switch . data . outcome_variable . grouping_variable_A . grouping_variable_B . outcome_variable_level1 . outcome_variable_level2 . outcome_variable_name_bs . grouping_variable . repeated_measures_name . outcome_variable_name . A1_label . A2_label . B1_label . B2_label . A_label . B_label . A1B1_mean . A1B1_sd . A1B1_n . A1B2_mean . A1B2_sd . A1B2_n . A2B1_mean . A2B1_sd . A2B1_n . A2B2_mean . A2B2_sd . A2B2_n . conf_level . effect_size . assume_equal_variance . show_details . show_interaction_plot . show_CI . evaluate_hypotheses . null_value . null_boundary . rope_units . alpha . null_color . es_plot_width . es_plot_height . ymin . ymax . ybreaks . ylab . xlab . axis.text.y . axis.title.y . axis.text.x . axis.title.x . simple_contrast_labels . error_layout . error_scale . error_nudge . data_layout . data_spread . difference_axis_units . difference_axis_breaks . shape_raw_reference . shape_raw_comparison . shape_raw_unused . shape_summary_reference . shape_summary_comparison . shape_summary_unused . shape_summary_difference . color_raw_reference . color_raw_comparison . color_raw_unused . color_summary_reference . color_summary_comparison . color_summary_unused . color_summary_difference . fill_raw_reference . fill_raw_comparison . fill_raw_unused . fill_summary_reference . fill_summary_comparison . fill_summary_unused . fill_summary_difference . size_raw_reference . size_raw_comparison . size_raw_unused . size_summary_reference . size_summary_comparison . size_summary_unused . size_summary_difference . alpha_raw_reference . alpha_raw_comparison . alpha_raw_unused . alpha_summary_reference . alpha_summary_comparison . alpha_summary_unused . alpha_summary_difference . linetype_summary_reference . linetype_summary_comparison . linetype_summary_unused . linetype_summary_difference . color_interval_reference . color_interval_comparison . color_interval_unused . color_interval_difference . size_interval_reference . size_interval_comparison . size_interval_unused . size_interval_difference . alpha_interval_reference . alpha_interval_comparison . alpha_interval_unused . alpha_interval_difference . alpha_error_reference . alpha_error_comparison . alpha_error_unused . alpha_error_difference . fill_error_reference . fill_error_comparison . fill_error_unused . fill_error_difference .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiff2x2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Means and Medians: 2x2 Factorial — jamovimdiff2x2","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$overview$asDF .data.frame(results$overview)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiffindcontrast.html","id":null,"dir":"Reference","previous_headings":"","what":"Means and Medians: Independent Groups Contrast — jamovimdiffindcontrast","title":"Means and Medians: Independent Groups Contrast — jamovimdiffindcontrast","text":"Means Medians: Independent Groups Contrast","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiffindcontrast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Means and Medians: Independent Groups Contrast — jamovimdiffindcontrast","text":"","code":"jamovimdiffindcontrast( switch = \"from_raw\", data, outcome_variable, grouping_variable, means, sds, ns, grouping_variable_levels, outcome_variable_name = \"Outcome variable\", grouping_variable_name = \"Grouping variable\", comparison_labels = \" \", reference_labels = \" \", conf_level = 95, effect_size = \"mean_difference\", assume_equal_variance = TRUE, show_details = FALSE, evaluate_hypotheses = FALSE, null_value = \"0\", null_boundary = \"0\", rope_units = \"raw\", alpha = 0.05, null_color = \"#A40122\", es_plot_width = \"550\", es_plot_height = \"450\", ymin = \"auto\", ymax = \"auto\", ybreaks = \"auto\", ylab = \"auto\", xlab = \"auto\", axis.text.y = \"14\", axis.title.y = \"15\", axis.text.x = \"14\", axis.title.x = \"15\", simple_contrast_labels = TRUE, error_layout = \"halfeye\", error_scale = \"0.25\", error_nudge = \"0.5\", data_layout = \"random\", data_spread = \"0.20\", difference_axis_units = \"raw\", difference_axis_breaks = \"auto\", shape_raw_reference = \"circle filled\", shape_raw_comparison = \"circle filled\", shape_raw_unused = \"circle filled\", shape_summary_reference = \"circle filled\", shape_summary_comparison = \"circle filled\", shape_summary_unused = \"circle filled\", shape_summary_difference = \"triangle filled\", color_raw_reference = \"#008DF9\", color_raw_comparison = \"#009F81\", color_raw_unused = \"gray65\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#009F81\", color_summary_unused = \"gray65\", color_summary_difference = \"black\", fill_raw_reference = \"NA\", fill_raw_comparison = \"NA\", fill_raw_unused = \"NA\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#009F81\", fill_summary_unused = \"gray65\", fill_summary_difference = \"black\", size_raw_reference = \"2\", size_raw_comparison = \"2\", size_raw_unused = \"1\", size_summary_reference = \"4\", size_summary_comparison = \"4\", size_summary_unused = \"4\", size_summary_difference = \"4\", alpha_raw_reference = \"1\", alpha_raw_comparison = \"1\", alpha_raw_unused = \"1\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_unused = \"1\", alpha_summary_difference = \"1\", linetype_summary_reference = \"solid\", linetype_summary_comparison = \"solid\", linetype_summary_unused = \"solid\", linetype_summary_difference = \"solid\", color_interval_reference = \"black\", color_interval_comparison = \"black\", color_interval_unused = \"gray65\", color_interval_difference = \"black\", size_interval_reference = \"3\", size_interval_comparison = \"3\", size_interval_unused = \"3\", size_interval_difference = \"3\", alpha_interval_reference = \"1\", alpha_interval_comparison = \"1\", alpha_interval_unused = \"1\", alpha_interval_difference = \"1\", alpha_error_reference = \"1\", alpha_error_comparison = \"1\", alpha_error_unused = \"1\", alpha_error_difference = \"1\", fill_error_reference = \"gray75\", fill_error_comparison = \"gray75\", fill_error_unused = \"gray75\", fill_error_difference = \"gray75\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiffindcontrast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Means and Medians: Independent Groups Contrast — jamovimdiffindcontrast","text":"switch . data . outcome_variable . grouping_variable . means . sds . ns . grouping_variable_levels . outcome_variable_name . grouping_variable_name . comparison_labels . reference_labels . conf_level . effect_size . assume_equal_variance . show_details . evaluate_hypotheses . null_value . null_boundary . rope_units . alpha . null_color . es_plot_width . es_plot_height . ymin . ymax . ybreaks . ylab . xlab . axis.text.y . axis.title.y . axis.text.x . axis.title.x . simple_contrast_labels . error_layout . error_scale . error_nudge . data_layout . data_spread . difference_axis_units . difference_axis_breaks . shape_raw_reference . shape_raw_comparison . shape_raw_unused . shape_summary_reference . shape_summary_comparison . shape_summary_unused . shape_summary_difference . color_raw_reference . color_raw_comparison . color_raw_unused . color_summary_reference . color_summary_comparison . color_summary_unused . color_summary_difference . fill_raw_reference . fill_raw_comparison . fill_raw_unused . fill_summary_reference . fill_summary_comparison . fill_summary_unused . fill_summary_difference . size_raw_reference . size_raw_comparison . size_raw_unused . size_summary_reference . size_summary_comparison . size_summary_unused . size_summary_difference . alpha_raw_reference . alpha_raw_comparison . alpha_raw_unused . alpha_summary_reference . alpha_summary_comparison . alpha_summary_unused . alpha_summary_difference . linetype_summary_reference . linetype_summary_comparison . linetype_summary_unused . linetype_summary_difference . color_interval_reference . color_interval_comparison . color_interval_unused . color_interval_difference . size_interval_reference . size_interval_comparison . size_interval_unused . size_interval_difference . alpha_interval_reference . alpha_interval_comparison . alpha_interval_unused . alpha_interval_difference . alpha_error_reference . alpha_error_comparison . alpha_error_unused . alpha_error_difference . fill_error_reference . fill_error_comparison . fill_error_unused . fill_error_difference .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiffindcontrast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Means and Medians: Independent Groups Contrast — jamovimdiffindcontrast","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$overview$asDF .data.frame(results$overview)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiffpaired.html","id":null,"dir":"Reference","previous_headings":"","what":"Means and Medians: Paired — jamovimdiffpaired","title":"Means and Medians: Paired — jamovimdiffpaired","text":"Means Medians: Paired","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiffpaired.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Means and Medians: Paired — jamovimdiffpaired","text":"","code":"jamovimdiffpaired( switch = \"from_raw\", data, reference_measure, comparison_measure, comparison_mean = \" \", comparison_sd = \" \", reference_mean = \" \", reference_sd = \" \", n = \" \", enter_r_or_sdiff = \"enter_r\", correlation = \" \", sdiff = \" \", comparison_measure_name = \"Comparison measure\", reference_measure_name = \"Reference measure\", conf_level = 95, effect_size = \"mean_difference\", show_ratio = FALSE, show_details = FALSE, show_calculations = FALSE, evaluate_hypotheses = FALSE, null_value = \"0\", null_boundary = \"0\", rope_units = \"raw\", alpha = 0.05, null_color = \"#A40122\", es_plot_width = \"600\", es_plot_height = \"400\", ymin = \"auto\", ymax = \"auto\", ybreaks = \"auto\", ylab = \"auto\", xlab = \"auto\", axis.text.y = \"14\", axis.title.y = \"15\", axis.text.x = \"14\", axis.title.x = \"15\", simple_contrast_labels = TRUE, error_layout = \"halfeye\", error_scale = \"0.25\", error_nudge = \"0.5\", data_layout = \"random\", data_spread = \"0.20\", difference_axis_units = \"raw\", difference_axis_breaks = \"auto\", shape_raw_reference = \"circle filled\", shape_raw_comparison = \"circle filled\", shape_raw_difference = \"triangle filled\", shape_summary_reference = \"circle filled\", shape_summary_comparison = \"circle filled\", shape_summary_difference = \"triangle filled\", color_raw_reference = \"#008DF9\", color_raw_comparison = \"#008DF9\", color_raw_difference = \"#E20134\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#008DF9\", color_summary_difference = \"black\", fill_raw_reference = \"NA\", fill_raw_comparison = \"NA\", fill_raw_difference = \"NA\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#008DF9\", fill_summary_difference = \"black\", size_raw_reference = \"2\", size_raw_comparison = \"2\", size_raw_difference = \"2\", size_summary_reference = \"4\", size_summary_comparison = \"4\", size_summary_difference = \"4\", alpha_raw_reference = \"1\", alpha_raw_comparison = \"1\", alpha_raw_difference = \"1\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_difference = \"1\", linetype_summary_reference = \"solid\", linetype_summary_comparison = \"solid\", linetype_summary_difference = \"solid\", color_interval_reference = \"black\", color_interval_comparison = \"black\", color_interval_difference = \"black\", size_interval_reference = \"3\", size_interval_comparison = \"3\", size_interval_difference = \"3\", alpha_interval_reference = \"1\", alpha_interval_comparison = \"1\", alpha_interval_difference = \"1\", alpha_error_reference = \"1\", alpha_error_comparison = \"1\", alpha_error_difference = \"1\", fill_error_reference = \"gray75\", fill_error_comparison = \"gray75\", fill_error_difference = \"gray75\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiffpaired.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Means and Medians: Paired — jamovimdiffpaired","text":"switch . data . reference_measure . comparison_measure . comparison_mean . comparison_sd . reference_mean . reference_sd . n . enter_r_or_sdiff . correlation . sdiff . comparison_measure_name . reference_measure_name . conf_level . effect_size . show_ratio . show_details . show_calculations . evaluate_hypotheses . null_value . null_boundary . rope_units . alpha . null_color . es_plot_width . es_plot_height . ymin . ymax . ybreaks . ylab . xlab . axis.text.y . axis.title.y . axis.text.x . axis.title.x . simple_contrast_labels . error_layout . error_scale . error_nudge . data_layout . data_spread . difference_axis_units . difference_axis_breaks . shape_raw_reference . shape_raw_comparison . shape_raw_difference . shape_summary_reference . shape_summary_comparison . shape_summary_difference . color_raw_reference . color_raw_comparison . color_raw_difference . color_summary_reference . color_summary_comparison . color_summary_difference . fill_raw_reference . fill_raw_comparison . fill_raw_difference . fill_summary_reference . fill_summary_comparison . fill_summary_difference . size_raw_reference . size_raw_comparison . size_raw_difference . size_summary_reference . size_summary_comparison . size_summary_difference . alpha_raw_reference . alpha_raw_comparison . alpha_raw_difference . alpha_summary_reference . alpha_summary_comparison . alpha_summary_difference . linetype_summary_reference . linetype_summary_comparison . linetype_summary_difference . color_interval_reference . color_interval_comparison . color_interval_difference . size_interval_reference . size_interval_comparison . size_interval_difference . alpha_interval_reference . alpha_interval_comparison . alpha_interval_difference . alpha_error_reference . alpha_error_comparison . alpha_error_difference . fill_error_reference . fill_error_comparison . fill_error_difference .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiffpaired.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Means and Medians: Paired — jamovimdiffpaired","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$overview$asDF .data.frame(results$overview)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdifftwo.html","id":null,"dir":"Reference","previous_headings":"","what":"Means and Medians: Two Groups — jamovimdifftwo","title":"Means and Medians: Two Groups — jamovimdifftwo","text":"Means Medians: Two Groups","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdifftwo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Means and Medians: Two Groups — jamovimdifftwo","text":"","code":"jamovimdifftwo( switch = \"from_raw\", data, outcome_variable, grouping_variable, reference_level_name = \"Reference group\", reference_mean = \" \", reference_sd = \" \", reference_n = \" \", comparison_level_name = \"Comparison group\", comparison_mean = \" \", comparison_sd = \" \", comparison_n = \" \", outcome_variable_name = \"Outcome variable\", grouping_variable_name = \"Grouping variable\", conf_level = 95, assume_equal_variance = TRUE, effect_size = \"mean_difference\", show_ratio = FALSE, switch_comparison_order = FALSE, show_details = FALSE, show_calculations = FALSE, evaluate_hypotheses = FALSE, null_value = \"0\", null_boundary = \"0\", rope_units = \"raw\", alpha = 0.05, null_color = \"#A40122\", es_plot_width = \"600\", es_plot_height = \"400\", ymin = \"auto\", ymax = \"auto\", ybreaks = \"auto\", ylab = \"auto\", xlab = \"auto\", axis.text.y = \"14\", axis.title.y = \"15\", axis.text.x = \"14\", axis.title.x = \"15\", simple_contrast_labels = TRUE, error_layout = \"halfeye\", error_scale = \"0.25\", error_nudge = \"0.5\", data_layout = \"random\", data_spread = \"0.20\", difference_axis_units = \"raw\", difference_axis_breaks = \"auto\", shape_raw_reference = \"circle filled\", shape_raw_comparison = \"circle filled\", shape_summary_reference = \"circle filled\", shape_summary_comparison = \"circle filled\", shape_summary_difference = \"triangle filled\", color_raw_reference = \"#008DF9\", color_raw_comparison = \"#009F81\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#009F81\", color_summary_difference = \"black\", fill_raw_reference = \"NA\", fill_raw_comparison = \"NA\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#009F81\", fill_summary_difference = \"black\", size_raw_reference = \"2\", size_raw_comparison = \"2\", size_summary_reference = \"4\", size_summary_comparison = \"4\", size_summary_difference = \"4\", alpha_raw_reference = \"1\", alpha_raw_comparison = \"1\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_difference = \"1\", linetype_summary_reference = \"solid\", linetype_summary_comparison = \"solid\", linetype_summary_difference = \"solid\", color_interval_reference = \"black\", color_interval_comparison = \"black\", color_interval_difference = \"black\", size_interval_reference = \"3\", size_interval_comparison = \"3\", size_interval_difference = \"3\", alpha_interval_reference = \"1\", alpha_interval_comparison = \"1\", alpha_interval_difference = \"1\", alpha_error_reference = \"1\", alpha_error_comparison = \"1\", alpha_error_difference = \"1\", fill_error_reference = \"gray75\", fill_error_comparison = \"gray75\", fill_error_difference = \"gray75\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdifftwo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Means and Medians: Two Groups — jamovimdifftwo","text":"switch . data . outcome_variable . grouping_variable . reference_level_name . reference_mean . reference_sd . reference_n . comparison_level_name . comparison_mean . comparison_sd . comparison_n . outcome_variable_name . grouping_variable_name . conf_level . assume_equal_variance . effect_size . show_ratio . switch_comparison_order . show_details . show_calculations . evaluate_hypotheses . null_value . null_boundary . rope_units . alpha . null_color . es_plot_width . es_plot_height . ymin . ymax . ybreaks . ylab . xlab . axis.text.y . axis.title.y . axis.text.x . axis.title.x . simple_contrast_labels . error_layout . error_scale . error_nudge . data_layout . data_spread . difference_axis_units . difference_axis_breaks . shape_raw_reference . shape_raw_comparison . shape_summary_reference . shape_summary_comparison . shape_summary_difference . color_raw_reference . color_raw_comparison . color_summary_reference . color_summary_comparison . color_summary_difference . fill_raw_reference . fill_raw_comparison . fill_summary_reference . fill_summary_comparison . fill_summary_difference . size_raw_reference . size_raw_comparison . size_summary_reference . size_summary_comparison . size_summary_difference . alpha_raw_reference . alpha_raw_comparison . alpha_summary_reference . alpha_summary_comparison . alpha_summary_difference . linetype_summary_reference . linetype_summary_comparison . linetype_summary_difference . color_interval_reference . color_interval_comparison . color_interval_difference . size_interval_reference . size_interval_comparison . size_interval_difference . alpha_interval_reference . alpha_interval_comparison . alpha_interval_difference . alpha_error_reference . alpha_error_comparison . alpha_error_difference . fill_error_reference . fill_error_comparison . fill_error_difference .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdifftwo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Means and Medians: Two Groups — jamovimdifftwo","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$overview$asDF .data.frame(results$overview)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetamdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Meta-Analysis: Difference in Means — jamovimetamdiff","title":"Meta-Analysis: Difference in Means — jamovimetamdiff","text":"Meta-Analysis: Difference Means","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetamdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Meta-Analysis: Difference in Means — jamovimetamdiff","text":"","code":"jamovimetamdiff( switch = \"from_raw\", data, comparison_means, comparison_sds, comparison_ns, reference_means, reference_sds, reference_ns, r, labels, moderator, d, dcomparison_ns, dreference_ns, dr, dlabels, dmoderator, conf_level = 95, effect_label = \"My effect\", reported_effect_size = \"mean_difference\", assume_equal_variance = TRUE, random_effects = \"random_effects\", include_PIs = FALSE, show_details = FALSE, es_plot_width = \"600\", es_plot_height = \"750\", size_base = \"2\", size_multiplier = \"3\", axis.text.y = \"14\", report_CIs = FALSE, meta_diamond_height = \".25\", xlab = \"auto\", xmin = \"auto\", xmax = \"auto\", xbreaks = \"auto\", mark_zero = TRUE, axis.text.x = \"14\", axis.title.x = \"15\", dlab = \"auto\", dmin = \"auto\", dmax = \"auto\", dbreaks = \"auto\", shape_raw_reference = \"square filled\", shape_raw_comparison = \"square filled\", shape_summary_difference = \"triangle filled\", shape_raw_unused = \"square filled\", color_raw_reference = \"#008DF9\", color_raw_comparison = \"#009F81\", color_raw_unused = \"gray65\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#009F81\", color_summary_unused = \"gray75\", color_summary_difference = \"black\", color_summary_overall = \"black\", fill_raw_reference = \"#008DF9\", fill_raw_comparison = \"#009F81\", fill_raw_unused = \"gray65\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#009F81\", fill_summary_unused = \"gray75\", fill_summary_difference = \"black\", fill_summary_overall = \"black\", alpha_raw_reference = \"1\", alpha_raw_comparison = \"1\", alpha_raw_unused = \"1\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_unused = \"1\", alpha_summary_difference = \"1\", alpha_summary_overall = \"1\", linetype_raw_reference = \"solid\", linetype_raw_comparison = \"solid\", linetype_summary_difference = \"solid\", linetype_raw_unused = \"solid\", color_interval_reference = \"black\", color_interval_comparison = \"black\", color_interval_difference = \"black\", color_interval_unused = \"black\", size_interval_reference = \".50\", size_interval_comparison = \".50\", size_interval_difference = \".50\", size_interval_unused = \".50\", alpha_interval_reference = \"1\", alpha_interval_comparison = \"1\", alpha_interval_difference = \"1\", alpha_interval_unused = \"1\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetamdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Meta-Analysis: Difference in Means — jamovimetamdiff","text":"switch . data . comparison_means . comparison_sds . comparison_ns . reference_means . reference_sds . reference_ns . r . labels . moderator . d . dcomparison_ns . dreference_ns . dr . dlabels . dmoderator . conf_level . effect_label . reported_effect_size . assume_equal_variance . random_effects . include_PIs . show_details . es_plot_width . es_plot_height . size_base . size_multiplier . axis.text.y . report_CIs . meta_diamond_height . xlab . xmin . xmax . xbreaks . mark_zero . axis.text.x . axis.title.x . dlab . dmin . dmax . dbreaks . shape_raw_reference . shape_raw_comparison . shape_summary_difference . shape_raw_unused . color_raw_reference . color_raw_comparison . color_raw_unused . color_summary_reference . color_summary_comparison . color_summary_unused . color_summary_difference . color_summary_overall . fill_raw_reference . fill_raw_comparison . fill_raw_unused . fill_summary_reference . fill_summary_comparison . fill_summary_unused . fill_summary_difference . fill_summary_overall . alpha_raw_reference . alpha_raw_comparison . alpha_raw_unused . alpha_summary_reference . alpha_summary_comparison . alpha_summary_unused . alpha_summary_difference . alpha_summary_overall . linetype_raw_reference . linetype_raw_comparison . linetype_summary_difference . linetype_raw_unused . color_interval_reference . color_interval_comparison . color_interval_difference . color_interval_unused . size_interval_reference . size_interval_comparison . size_interval_difference . size_interval_unused . alpha_interval_reference . alpha_interval_comparison . alpha_interval_difference . alpha_interval_unused .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetamdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Meta-Analysis: Difference in Means — jamovimetamdiff","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$raw_data$asDF .data.frame(results$raw_data)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetamean.html","id":null,"dir":"Reference","previous_headings":"","what":"Meta-Analysis: Means — jamovimetamean","title":"Meta-Analysis: Means — jamovimetamean","text":"Meta-Analysis: Means","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetamean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Meta-Analysis: Means — jamovimetamean","text":"","code":"jamovimetamean( switch = \"from_raw\", data, means, sds, ns, labels, moderator, ds, dns, dlabels, dmoderator, conf_level = 95, effect_label = \"My effect\", reference_mean = \"\", reported_effect_size = \"mean_difference\", random_effects = \"random_effects\", include_PIs = FALSE, show_details = FALSE, es_plot_width = \"600\", es_plot_height = \"750\", size_base = \"2\", size_multiplier = \"3\", axis.text.y = \"14\", report_CIs = FALSE, meta_diamond_height = \".25\", xlab = \"auto\", xmin = \"auto\", xmax = \"auto\", xbreaks = \"auto\", mark_zero = TRUE, axis.text.x = \"14\", axis.title.x = \"15\", dlab = \"auto\", dmin = \"auto\", dmax = \"auto\", dbreaks = \"auto\", shape_raw_reference = \"square filled\", shape_raw_comparison = \"square filled\", shape_summary_difference = \"triangle filled\", shape_raw_unused = \"square filled\", color_raw_reference = \"#008DF9\", color_raw_comparison = \"#009F81\", color_raw_unused = \"gray65\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#009F81\", color_summary_unused = \"gray75\", color_summary_difference = \"black\", color_summary_overall = \"black\", fill_raw_reference = \"#008DF9\", fill_raw_comparison = \"#009F81\", fill_raw_unused = \"gray65\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#009F81\", fill_summary_unused = \"gray75\", fill_summary_difference = \"black\", fill_summary_overall = \"black\", alpha_raw_reference = \"1\", alpha_raw_comparison = \"1\", alpha_raw_unused = \"1\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_unused = \"1\", alpha_summary_difference = \"1\", alpha_summary_overall = \"1\", linetype_raw_reference = \"solid\", linetype_raw_comparison = \"solid\", linetype_summary_difference = \"solid\", linetype_raw_unused = \"solid\", color_interval_reference = \"black\", color_interval_comparison = \"black\", color_interval_difference = \"black\", color_interval_unused = \"black\", size_interval_reference = \".50\", size_interval_comparison = \".50\", size_interval_difference = \".50\", size_interval_unused = \".50\", alpha_interval_reference = \"1\", alpha_interval_comparison = \"1\", alpha_interval_difference = \"1\", alpha_interval_unused = \"1\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetamean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Meta-Analysis: Means — jamovimetamean","text":"switch . data . means . sds . ns . labels . moderator . ds . dns . dlabels . dmoderator . conf_level . effect_label . reference_mean . reported_effect_size . random_effects . include_PIs . show_details . es_plot_width . es_plot_height . size_base . size_multiplier . axis.text.y . report_CIs . meta_diamond_height . xlab . xmin . xmax . xbreaks . mark_zero . axis.text.x . axis.title.x . dlab . dmin . dmax . dbreaks . shape_raw_reference . shape_raw_comparison . shape_summary_difference . shape_raw_unused . color_raw_reference . color_raw_comparison . color_raw_unused . color_summary_reference . color_summary_comparison . color_summary_unused . color_summary_difference . color_summary_overall . fill_raw_reference . fill_raw_comparison . fill_raw_unused . fill_summary_reference . fill_summary_comparison . fill_summary_unused . fill_summary_difference . fill_summary_overall . alpha_raw_reference . alpha_raw_comparison . alpha_raw_unused . alpha_summary_reference . alpha_summary_comparison . alpha_summary_unused . alpha_summary_difference . alpha_summary_overall . linetype_raw_reference . linetype_raw_comparison . linetype_summary_difference . linetype_raw_unused . color_interval_reference . color_interval_comparison . color_interval_difference . color_interval_unused . size_interval_reference . size_interval_comparison . size_interval_difference . size_interval_unused . alpha_interval_reference . alpha_interval_comparison . alpha_interval_difference . alpha_interval_unused .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetamean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Meta-Analysis: Means — jamovimetamean","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$raw_data$asDF .data.frame(results$raw_data)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetapdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Meta-Analysis: Difference in Proportions — jamovimetapdiff","title":"Meta-Analysis: Difference in Proportions — jamovimetapdiff","text":"Meta-Analysis: Difference Proportions","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetapdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Meta-Analysis: Difference in Proportions — jamovimetapdiff","text":"","code":"jamovimetapdiff( data, reference_cases, reference_ns, comparison_cases, comparison_ns, labels, moderator, effect_label = \"My effect\", reported_effect_size = \"RD\", conf_level = 95, random_effects = \"random_effects\", include_PIs = FALSE, show_details = FALSE, es_plot_width = \"600\", es_plot_height = \"750\", size_base = \"2\", size_multiplier = \"3\", axis.text.y = \"14\", report_CIs = FALSE, meta_diamond_height = \".25\", xlab = \"auto\", xmin = \"auto\", xmax = \"auto\", xbreaks = \"auto\", mark_zero = FALSE, axis.text.x = \"14\", axis.title.x = \"15\", dlab = \"auto\", dmin = \"auto\", dmax = \"auto\", dbreaks = \"auto\", shape_raw_reference = \"square filled\", shape_raw_comparison = \"square filled\", shape_summary_difference = \"triangle filled\", shape_raw_unused = \"square filled\", color_raw_reference = \"#008DF9\", color_raw_comparison = \"#009F81\", color_raw_unused = \"gray65\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#009F81\", color_summary_unused = \"gray75\", color_summary_difference = \"black\", color_summary_overall = \"black\", fill_raw_reference = \"#008DF9\", fill_raw_comparison = \"#009F81\", fill_raw_unused = \"gray65\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#009F81\", fill_summary_unused = \"gray75\", fill_summary_difference = \"black\", fill_summary_overall = \"black\", alpha_raw_reference = \"1\", alpha_raw_comparison = \"1\", alpha_raw_unused = \"1\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_unused = \"1\", alpha_summary_difference = \"1\", alpha_summary_overall = \"1\", linetype_raw_reference = \"solid\", linetype_raw_comparison = \"solid\", linetype_summary_difference = \"solid\", linetype_raw_unused = \"solid\", color_interval_reference = \"black\", color_interval_comparison = \"black\", color_interval_difference = \"black\", color_interval_unused = \"black\", size_interval_reference = \".50\", size_interval_comparison = \".50\", size_interval_difference = \".50\", size_interval_unused = \".50\", alpha_interval_reference = \"1\", alpha_interval_comparison = \"1\", alpha_interval_difference = \"1\", alpha_interval_unused = \"1\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetapdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Meta-Analysis: Difference in Proportions — jamovimetapdiff","text":"data . reference_cases . reference_ns . comparison_cases . comparison_ns . labels . moderator . effect_label . reported_effect_size . conf_level . random_effects . include_PIs . show_details . es_plot_width . es_plot_height . size_base . size_multiplier . axis.text.y . report_CIs . meta_diamond_height . xlab . xmin . xmax . xbreaks . mark_zero . axis.text.x . axis.title.x . dlab . dmin . dmax . dbreaks . shape_raw_reference . shape_raw_comparison . shape_summary_difference . shape_raw_unused . color_raw_reference . color_raw_comparison . color_raw_unused . color_summary_reference . color_summary_comparison . color_summary_unused . color_summary_difference . color_summary_overall . fill_raw_reference . fill_raw_comparison . fill_raw_unused . fill_summary_reference . fill_summary_comparison . fill_summary_unused . fill_summary_difference . fill_summary_overall . alpha_raw_reference . alpha_raw_comparison . alpha_raw_unused . alpha_summary_reference . alpha_summary_comparison . alpha_summary_unused . alpha_summary_difference . alpha_summary_overall . linetype_raw_reference . linetype_raw_comparison . linetype_summary_difference . linetype_raw_unused . color_interval_reference . color_interval_comparison . color_interval_difference . color_interval_unused . size_interval_reference . size_interval_comparison . size_interval_difference . size_interval_unused . alpha_interval_reference . alpha_interval_comparison . alpha_interval_difference . alpha_interval_unused .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetapdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Meta-Analysis: Difference in Proportions — jamovimetapdiff","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$raw_data$asDF .data.frame(results$raw_data)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetaproportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Meta-Analysis: Proportions — jamovimetaproportion","title":"Meta-Analysis: Proportions — jamovimetaproportion","text":"Meta-Analysis: Proportions","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetaproportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Meta-Analysis: Proportions — jamovimetaproportion","text":"","code":"jamovimetaproportion( data, cases, ns, labels, moderator, effect_label = \"My effect\", conf_level = 95, random_effects = \"random_effects\", include_PIs = FALSE, show_details = FALSE, es_plot_width = \"600\", es_plot_height = \"750\", size_base = \"2\", size_multiplier = \"3\", axis.text.y = \"14\", report_CIs = FALSE, meta_diamond_height = \".25\", xlab = \"auto\", xmin = \"auto\", xmax = \"auto\", xbreaks = \"auto\", mark_zero = FALSE, axis.text.x = \"14\", axis.title.x = \"15\", dlab = \"auto\", dmin = \"auto\", dmax = \"auto\", dbreaks = \"auto\", shape_raw_reference = \"square filled\", shape_raw_comparison = \"square filled\", shape_summary_difference = \"triangle filled\", shape_raw_unused = \"square filled\", color_raw_reference = \"#008DF9\", color_raw_comparison = \"#009F81\", color_raw_unused = \"gray65\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#009F81\", color_summary_unused = \"gray75\", color_summary_difference = \"black\", color_summary_overall = \"black\", fill_raw_reference = \"#008DF9\", fill_raw_comparison = \"#009F81\", fill_raw_unused = \"gray65\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#009F81\", fill_summary_unused = \"gray75\", fill_summary_difference = \"black\", fill_summary_overall = \"black\", alpha_raw_reference = \"1\", alpha_raw_comparison = \"1\", alpha_raw_unused = \"1\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_unused = \"1\", alpha_summary_difference = \"1\", alpha_summary_overall = \"1\", linetype_raw_reference = \"solid\", linetype_raw_comparison = \"solid\", linetype_summary_difference = \"solid\", linetype_raw_unused = \"solid\", color_interval_reference = \"black\", color_interval_comparison = \"black\", color_interval_difference = \"black\", color_interval_unused = \"black\", size_interval_reference = \".50\", size_interval_comparison = \".50\", size_interval_difference = \".50\", size_interval_unused = \".50\", alpha_interval_reference = \"1\", alpha_interval_comparison = \"1\", alpha_interval_difference = \"1\", alpha_interval_unused = \"1\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetaproportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Meta-Analysis: Proportions — jamovimetaproportion","text":"data . cases . ns . labels . moderator . effect_label . conf_level . random_effects . include_PIs . show_details . es_plot_width . es_plot_height . size_base . size_multiplier . axis.text.y . report_CIs . meta_diamond_height . xlab . xmin . xmax . xbreaks . mark_zero . axis.text.x . axis.title.x . dlab . dmin . dmax . dbreaks . shape_raw_reference . shape_raw_comparison . shape_summary_difference . shape_raw_unused . color_raw_reference . color_raw_comparison . color_raw_unused . color_summary_reference . color_summary_comparison . color_summary_unused . color_summary_difference . color_summary_overall . fill_raw_reference . fill_raw_comparison . fill_raw_unused . fill_summary_reference . fill_summary_comparison . fill_summary_unused . fill_summary_difference . fill_summary_overall . alpha_raw_reference . alpha_raw_comparison . alpha_raw_unused . alpha_summary_reference . alpha_summary_comparison . alpha_summary_unused . alpha_summary_difference . alpha_summary_overall . linetype_raw_reference . linetype_raw_comparison . linetype_summary_difference . linetype_raw_unused . color_interval_reference . color_interval_comparison . color_interval_difference . color_interval_unused . size_interval_reference . size_interval_comparison . size_interval_difference . size_interval_unused . alpha_interval_reference . alpha_interval_comparison . alpha_interval_difference . alpha_interval_unused .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetaproportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Meta-Analysis: Proportions — jamovimetaproportion","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$raw_data$asDF .data.frame(results$raw_data)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetar.html","id":null,"dir":"Reference","previous_headings":"","what":"Meta-Analysis: Correlations — jamovimetar","title":"Meta-Analysis: Correlations — jamovimetar","text":"Meta-Analysis: Correlations","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Meta-Analysis: Correlations — jamovimetar","text":"","code":"jamovimetar( data, rs, ns, labels, moderator, effect_label = \"My effect\", conf_level = 95, random_effects = \"random_effects\", include_PIs = FALSE, show_details = FALSE, es_plot_width = \"600\", es_plot_height = \"750\", size_base = \"2\", size_multiplier = \"3\", axis.text.y = \"14\", report_CIs = FALSE, meta_diamond_height = \".25\", xlab = \"auto\", xmin = \"-1\", xmax = \"1\", xbreaks = \"auto\", mark_zero = TRUE, axis.text.x = \"14\", axis.title.x = \"15\", dlab = \"auto\", dmin = \"auto\", dmax = \"auto\", dbreaks = \"auto\", shape_raw_reference = \"square filled\", shape_raw_comparison = \"square filled\", shape_summary_difference = \"triangle filled\", shape_raw_unused = \"square filled\", color_raw_reference = \"#008DF9\", color_raw_comparison = \"#009F81\", color_raw_unused = \"gray65\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#009F81\", color_summary_unused = \"gray75\", color_summary_difference = \"black\", color_summary_overall = \"black\", fill_raw_reference = \"#008DF9\", fill_raw_comparison = \"#009F81\", fill_raw_unused = \"gray65\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#009F81\", fill_summary_unused = \"gray75\", fill_summary_difference = \"black\", fill_summary_overall = \"black\", alpha_raw_reference = \"1\", alpha_raw_comparison = \"1\", alpha_raw_unused = \"1\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_unused = \"1\", alpha_summary_difference = \"1\", alpha_summary_overall = \"1\", linetype_raw_reference = \"solid\", linetype_raw_comparison = \"solid\", linetype_summary_difference = \"solid\", linetype_raw_unused = \"solid\", color_interval_reference = \"black\", color_interval_comparison = \"black\", color_interval_difference = \"black\", color_interval_unused = \"black\", size_interval_reference = \".50\", size_interval_comparison = \".50\", size_interval_difference = \".50\", size_interval_unused = \".50\", alpha_interval_reference = \"1\", alpha_interval_comparison = \"1\", alpha_interval_difference = \"1\", alpha_interval_unused = \"1\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Meta-Analysis: Correlations — jamovimetar","text":"data . rs . ns . labels . moderator . effect_label . conf_level . random_effects . include_PIs . show_details . es_plot_width . es_plot_height . size_base . size_multiplier . axis.text.y . report_CIs . meta_diamond_height . xlab . xmin . xmax . xbreaks . mark_zero . axis.text.x . axis.title.x . dlab . dmin . dmax . dbreaks . shape_raw_reference . shape_raw_comparison . shape_summary_difference . shape_raw_unused . color_raw_reference . color_raw_comparison . color_raw_unused . color_summary_reference . color_summary_comparison . color_summary_unused . color_summary_difference . color_summary_overall . fill_raw_reference . fill_raw_comparison . fill_raw_unused . fill_summary_reference . fill_summary_comparison . fill_summary_unused . fill_summary_difference . fill_summary_overall . alpha_raw_reference . alpha_raw_comparison . alpha_raw_unused . alpha_summary_reference . alpha_summary_comparison . alpha_summary_unused . alpha_summary_difference . alpha_summary_overall . linetype_raw_reference . linetype_raw_comparison . linetype_summary_difference . linetype_raw_unused . color_interval_reference . color_interval_comparison . color_interval_difference . color_interval_unused . size_interval_reference . size_interval_comparison . size_interval_difference . size_interval_unused . alpha_interval_reference . alpha_interval_comparison . alpha_interval_difference . alpha_interval_unused .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Meta-Analysis: Correlations — jamovimetar","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$raw_data$asDF .data.frame(results$raw_data)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovipdiffpaired.html","id":null,"dir":"Reference","previous_headings":"","what":"Proportions: Paired — jamovipdiffpaired","title":"Proportions: Paired — jamovipdiffpaired","text":"Proportions: Paired","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovipdiffpaired.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Proportions: Paired — jamovipdiffpaired","text":"","code":"jamovipdiffpaired( switch = \"from_raw\", data, reference_measure, comparison_measure, cases_consistent = \" \", cases_inconsistent = \" \", not_cases_consistent = \" \", not_cases_inconsistent = \" \", case_label = \"Sick\", not_case_label = \"Well\", comparison_measure_name = \"Post-test\", reference_measure_name = \"Pre-test\", conf_level = 95, show_details = FALSE, evaluate_hypotheses = FALSE, null_value = \"0\", null_boundary = \"0\", alpha = 0.05, null_color = \"#A40122\", es_plot_width = \"400\", es_plot_height = \"450\", ymin = \"auto\", ymax = \"auto\", ybreaks = \"auto\", ylab = \"auto\", xlab = \"auto\", axis.text.y = \"14\", axis.title.y = \"15\", axis.text.x = \"14\", axis.title.x = \"15\", simple_contrast_labels = TRUE, error_layout = \"none\", difference_axis_breaks = \"auto\", shape_summary_reference = \"circle filled\", shape_summary_comparison = \"circle filled\", shape_summary_difference = \"triangle filled\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#009F81\", color_summary_difference = \"black\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#009F81\", fill_summary_difference = \"black\", size_summary_reference = \"4\", size_summary_comparison = \"4\", size_summary_difference = \"4\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_difference = \"1\", linetype_summary_reference = \"solid\", linetype_summary_comparison = \"solid\", linetype_summary_difference = \"solid\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovipdiffpaired.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Proportions: Paired — jamovipdiffpaired","text":"switch . data . reference_measure . comparison_measure . cases_consistent . cases_inconsistent . not_cases_consistent . not_cases_inconsistent . case_label . not_case_label . comparison_measure_name . reference_measure_name . conf_level . show_details . evaluate_hypotheses . null_value . null_boundary . alpha . null_color . es_plot_width . es_plot_height . ymin . ymax . ybreaks . ylab . xlab . axis.text.y . axis.title.y . axis.text.x . axis.title.x . simple_contrast_labels . error_layout . difference_axis_breaks . shape_summary_reference . shape_summary_comparison . shape_summary_difference . color_summary_reference . color_summary_comparison . color_summary_difference . fill_summary_reference . fill_summary_comparison . fill_summary_difference . size_summary_reference . size_summary_comparison . size_summary_difference . alpha_summary_reference . alpha_summary_comparison . alpha_summary_difference . linetype_summary_reference . linetype_summary_comparison . linetype_summary_difference .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovipdiffpaired.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Proportions: Paired — jamovipdiffpaired","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$overview$asDF .data.frame(results$overview)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovipdifftwo.html","id":null,"dir":"Reference","previous_headings":"","what":"Proportions: Two Groups — jamovipdifftwo","title":"Proportions: Two Groups — jamovipdifftwo","text":"Proportions: Two Groups","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovipdifftwo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Proportions: Two Groups — jamovipdifftwo","text":"","code":"jamovipdifftwo( switch = \"from_raw\", data, outcome_variable, grouping_variable, comparison_cases = \" \", comparison_not_cases = \" \", reference_cases = \" \", reference_not_cases = \" \", case_label = \"Sick\", not_case_label = \"Well\", grouping_variable_level1 = \"Treated\", grouping_variable_level2 = \"Control\", outcome_variable_name = \"Outcome variable\", grouping_variable_name = \"Grouping variable\", count_NA = FALSE, show_ratio = FALSE, show_chi_square = FALSE, chi_table_option = \"both\", show_phi = FALSE, conf_level = 95, show_details = FALSE, evaluate_hypotheses = FALSE, null_value = \"0\", null_boundary = \"0\", alpha = 0.05, null_color = \"#A40122\", es_plot_width = \"400\", es_plot_height = \"450\", ymin = \"auto\", ymax = \"auto\", ybreaks = \"auto\", ylab = \"auto\", xlab = \"auto\", axis.text.y = \"14\", axis.title.y = \"15\", axis.text.x = \"14\", axis.title.x = \"15\", simple_contrast_labels = TRUE, error_layout = \"none\", difference_axis_breaks = \"auto\", shape_summary_reference = \"circle filled\", shape_summary_comparison = \"circle filled\", shape_summary_difference = \"triangle filled\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#009F81\", color_summary_difference = \"black\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#009F81\", fill_summary_difference = \"black\", size_summary_reference = \"4\", size_summary_comparison = \"4\", size_summary_difference = \"4\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_difference = \"1\", linetype_summary_reference = \"solid\", linetype_summary_comparison = \"solid\", linetype_summary_difference = \"solid\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovipdifftwo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Proportions: Two Groups — jamovipdifftwo","text":"switch . data . outcome_variable . grouping_variable . comparison_cases . comparison_not_cases . reference_cases . reference_not_cases . case_label . not_case_label . grouping_variable_level1 . grouping_variable_level2 . outcome_variable_name . grouping_variable_name . count_NA . show_ratio . show_chi_square . chi_table_option . show_phi . conf_level . show_details . evaluate_hypotheses . null_value . null_boundary . alpha . null_color . es_plot_width . es_plot_height . ymin . ymax . ybreaks . ylab . xlab . axis.text.y . axis.title.y . axis.text.x . axis.title.x . simple_contrast_labels . error_layout . difference_axis_breaks . shape_summary_reference . shape_summary_comparison . shape_summary_difference . color_summary_reference . color_summary_comparison . color_summary_difference . fill_summary_reference . fill_summary_comparison . fill_summary_difference . size_summary_reference . size_summary_comparison . size_summary_difference . alpha_summary_reference . alpha_summary_comparison . alpha_summary_difference . linetype_summary_reference . linetype_summary_comparison . linetype_summary_difference .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovipdifftwo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Proportions: Two Groups — jamovipdifftwo","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$overview$asDF .data.frame(results$overview)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamoviproportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Proportions: Single Group — jamoviproportion","title":"Proportions: Single Group — jamoviproportion","text":"Proportions: Single Group","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamoviproportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Proportions: Single Group — jamoviproportion","text":"","code":"jamoviproportion( switch = \"from_raw\", data, outcome_variable, cases = \" \", not_cases = \" \", case_label = \"Affected\", not_case_label = \"Not Affected\", outcome_variable_name = \"My outcome variable\", count_NA = FALSE, conf_level = 95, show_details = FALSE, plot_possible = FALSE, evaluate_hypotheses = FALSE, null_value = \"0\", null_boundary = \"0\", alpha = 0.05, null_color = \"#A40122\", es_plot_width = \"300\", es_plot_height = \"400\", ymin = \"0\", ymax = \"1\", breaks = \"auto\", ylab = \"auto\", xlab = \"auto\", axis.text.y = \"14\", axis.title.y = \"15\", axis.text.x = \"14\", axis.title.x = \"15\", error_layout = \"none\", shape_summary = \"circle filled\", color_summary = \"#008DF9\", fill_summary = \"#008DF9\", size_summary = \"4\", alpha_summary = \"1\", linetype_summary = \"solid\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamoviproportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Proportions: Single Group — jamoviproportion","text":"switch . data . outcome_variable . cases . not_cases . case_label . not_case_label . outcome_variable_name . count_NA . conf_level . show_details . plot_possible . evaluate_hypotheses . null_value . null_boundary . alpha . null_color . es_plot_width . es_plot_height . ymin . ymax . breaks . ylab . xlab . axis.text.y . axis.title.y . axis.text.x . axis.title.x . error_layout . shape_summary . color_summary . fill_summary . size_summary . alpha_summary . linetype_summary .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamoviproportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Proportions: Single Group — jamoviproportion","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$overview$asDF .data.frame(results$overview)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovirdifftwo.html","id":null,"dir":"Reference","previous_headings":"","what":"Correlations: Two Groups — jamovirdifftwo","title":"Correlations: Two Groups — jamovirdifftwo","text":"Correlations: Two Groups","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovirdifftwo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Correlations: Two Groups — jamovirdifftwo","text":"","code":"jamovirdifftwo( switch = \"from_raw\", data, x, y, grouping_variable, comparison_r = \" \", comparison_n = \" \", reference_r = \" \", reference_n = \" \", x_variable_name = \"X variable\", y_variable_name = \"Y variable\", comparison_level_name = \"Comparison level\", reference_level_name = \"Reference level\", grouping_variable_name = \"Grouping variable\", conf_level = 95, show_details = FALSE, show_line = TRUE, show_line_CI = TRUE, evaluate_hypotheses = FALSE, null_value = \"0\", null_boundary = \"0\", alpha = 0.05, null_color = \"#A40122\", es_plot_width = \"600\", es_plot_height = \"400\", sp_plot_width = \"650\", sp_plot_height = \"650\", ymin = \"auto\", ymax = \"auto\", ybreaks = \"auto\", ylab = \"auto\", xlab = \"auto\", axis.text.y = \"14\", axis.title.y = \"15\", axis.text.x = \"14\", axis.title.x = \"15\", sp_ymin = \"auto\", sp_ymax = \"auto\", sp_ybreaks = \"auto\", sp_xmin = \"auto\", sp_xmax = \"auto\", sp_xbreaks = \"auto\", sp_ylab = \"auto\", sp_xlab = \"auto\", sp_axis.text.y = \"14\", sp_axis.title.y = \"15\", sp_axis.text.x = \"14\", sp_axis.title.x = \"15\", difference_axis_breaks = \"auto\", shape_summary_reference = \"circle filled\", shape_summary_comparison = \"circle filled\", shape_summary_difference = \"triangle filled\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#009F81\", color_summary_difference = \"black\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#009F81\", fill_summary_difference = \"black\", size_summary_reference = \"4\", size_summary_comparison = \"4\", size_summary_difference = \"4\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_difference = \"1\", linetype_summary_reference = \"solid\", linetype_summary_comparison = \"solid\", linetype_summary_difference = \"solid\", sp_shape_raw_reference = \"circle filled\", sp_shape_raw_comparison = \"circle filled\", sp_shape_raw_unused = \"circle filled\", sp_color_raw_reference = \"black\", sp_color_raw_comparison = \"black\", sp_color_raw_unused = \"black\", sp_fill_raw_reference = \"#008DF9\", sp_fill_raw_comparison = \"#009F81\", sp_fill_raw_unused = \"NA\", sp_size_raw_reference = \"3\", sp_size_raw_comparison = \"3\", sp_size_raw_unused = \"2\", sp_alpha_raw_reference = \".25\", sp_alpha_raw_comparison = \".25\", sp_alpha_raw_unused = \".25\", sp_linetype_summary_reference = \"solid\", sp_linetype_summary_comparison = \"solid\", sp_color_summary_reference = \"#008DF9\", sp_color_summary_comparison = \"#009F81\", sp_size_summary_reference = \"2\", sp_size_summary_comparison = \"2\", sp_alpha_summary_reference = \".25\", sp_alpha_summary_comparison = \".25\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovirdifftwo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Correlations: Two Groups — jamovirdifftwo","text":"switch . data . x . y . grouping_variable . comparison_r . comparison_n . reference_r . reference_n . x_variable_name . y_variable_name . comparison_level_name . reference_level_name . grouping_variable_name . conf_level . show_details . show_line . show_line_CI . evaluate_hypotheses . null_value . null_boundary . alpha . null_color . es_plot_width . es_plot_height . sp_plot_width . sp_plot_height . ymin . ymax . ybreaks . ylab . xlab . axis.text.y . axis.title.y . axis.text.x . axis.title.x . sp_ymin . sp_ymax . sp_ybreaks . sp_xmin . sp_xmax . sp_xbreaks . sp_ylab . sp_xlab . sp_axis.text.y . sp_axis.title.y . sp_axis.text.x . sp_axis.title.x . difference_axis_breaks . shape_summary_reference . shape_summary_comparison . shape_summary_difference . color_summary_reference . color_summary_comparison . color_summary_difference . fill_summary_reference . fill_summary_comparison . fill_summary_difference . size_summary_reference . size_summary_comparison . size_summary_difference . alpha_summary_reference . alpha_summary_comparison . alpha_summary_difference . linetype_summary_reference . linetype_summary_comparison . linetype_summary_difference . sp_shape_raw_reference . sp_shape_raw_comparison . sp_shape_raw_unused . sp_color_raw_reference . sp_color_raw_comparison . sp_color_raw_unused . sp_fill_raw_reference . sp_fill_raw_comparison . sp_fill_raw_unused . sp_size_raw_reference . sp_size_raw_comparison . sp_size_raw_unused . sp_alpha_raw_reference . sp_alpha_raw_comparison . sp_alpha_raw_unused . sp_linetype_summary_reference . sp_linetype_summary_comparison . sp_color_summary_reference . sp_color_summary_comparison . sp_size_summary_reference . sp_size_summary_comparison . sp_alpha_summary_reference . sp_alpha_summary_comparison .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovirdifftwo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Correlations: Two Groups — jamovirdifftwo","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$overview$asDF .data.frame(results$overview)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jesci_document_data.html","id":null,"dir":"Reference","previous_headings":"","what":"also promote kind, altruistic behavior. Brethel-Haurwitz and Marsh (2014)\r\nexamined this idea by collecting data on U.S. states. A Gallup poll in 2010\r\nwas used to measure each state's well-being index, a measure of mean\r\nhappiness for the state's residents on a scale from 0 to 100. Next, a kidney\r\ndonation database for 1999-2010 was used to figure out each state's rate\r\n(number of donations per 1 million people) of non-directed kidney\r\ndonations-that's giving one kidney to a stranger, an extremely generous and\r\naltruistic thing to do!\r\ntheir classic study, Jacowitz and Kahneman (1995) asked participants to\r\nestimate how many babies are born each day in the United States. Participants\r\nwere given either a low anchor (more than 100 babies/day) or a high anchor\r\n(less than 50,000 babies/day). Those who saw the low anchor estimated many\r\nfewer births/day than those who saw the high anchor, which suggests that the\r\nwording can have a profound influence. The correct answer, as it happens, is\r\n~11,000 births/day in 2014. To investigate the extent that these results are\r\nreplicable, the Many Labs project repeated this classic study at many\r\ndifferent labs around the world. You can find the summary data for 30 of\r\nthese labs in the Anchor Estimate ma data file\r\nnumerous other issues can be highly damaging, but are thriving in this social\r\nmedia age. Trying to debunk a conspiracy theory by presenting facts and\r\nevidence often doesn't work, alas. Psychological inoculation, also similar to\r\nprebunking, presents a mild form of misinformation, preferably with\r\nexplanation, in the hope of building resistance to real-life fake news-a sort\r\nof vaccine for fake news. The Bad News game is a spin-off from research on\r\npsychological inoculation. Basol et al. (2020) assessed the possible\r\neffectiveness of this game as a fake news vaccine. At getbadnews.com you can\r\nclick 'About' for information, or just start playing the game- it's easy and\r\nmaybe even fun. You encounter mock Twitter (now X) fake news messages that\r\nillustrate common strategies for making fake news memorable or believable.\r\nYou make choices between messages and decide which ones to 'forward' as you\r\ntry to spread fake news while building your credibility score and number of\r\n'followers'-rather like real life for a conspiracy theorist wanting to spread\r\nthe word. Compete with your friends for credibility and number of followers.\r\nBasol's online participants first saw 18 fictitious fake news tweets and\r\nrated each for reliability (accuracy, believability), and also rated their\r\nconfidence in that reliability rating. Both ratings were on a 1 to 7 scale.\r\nThose in the BadNews group then played the game for 15 minutes, whereas those\r\nin the Control group played Tetris. Then all once again gave reliability and\r\nconfidence ratings for the 18 tweets.\r\ndecade earlier, had been one of several outside experts invited to scrutinize\r\nthe laboratory and experimental procedures of parapsychology researcher\r\nCharles Honorton. Bem not only judged them adequate, but joined the research\r\neffort and became a coauthor with Honorton. Bem and Honorton (1994) first\r\nreviewed early ganzfeld studies and described how the experimental procedure\r\nhad been improved to reduce the chance that results could be influenced by\r\nvarious possible biases, or leakages of information from sender to receiver.\r\nFor example, the randomization procedure was carried out automatically by\r\ncomputer, and all stimuli were presented under computer control. Bem and\r\nHonorton then presented data from studies conducted with the improved\r\nprocedure. Table 13.1 presents basic data from 10 studies reported by Bem and\r\nHonorton (1994). Participants each made a single judgment, so in Pilot 1, for\r\nexample, 22 participants responded, with 8 of them giving a correct response.\r\nThree pilot studies helped refine the procedures, then four studies used\r\nnovice receivers. Study 5 used 20 students of music, drama, or dance as\r\nreceivers, in response to suggestions that creative people might be more\r\nlikely to show telepathy. Studies 6 and 7 used receivers who had participated\r\nin an earlier study. The proportion of hits expected by chance is .25, and\r\nTable 13.1 shows that all but Study 1 found proportions higher than .25.\r\nwho identified as female. Data is Subjective Wellbeing abd Body Satisfaction.\r\nwho identified as male. Data is Subjective Wellbeing abd Body Satisfaction.\r\nReported are measures of Subjective Wellbeing abd Body Satisfaction.\r\nFictituous data from an unrealistically small HEAT\r\nstudy comparing scores for a single group of students before and after a\r\nworkshop on climate change.\r\nSome researchers claim that moral judgments are based not only on rational\r\nconsiderations but also on one's current emotional state. To what extent can\r\nrecent emotional experiences influence moral judgments? Schnall et al. (2008)\r\nexamined this question by manipulating feelings of cleanliness and purity and\r\nthen observing the extent that this changes how harshly participants judge\r\nthe morality of others. Inscho Study 1, Schnall et al. asked participants to\r\ncomplete a word scramble task with either neutral words (neutral prime) or\r\nwords related to cleanliness (cleanliness prime). All students then completed\r\na set of moral judgments about controversial scenarios: Moral judgment is the\r\naverage of six items, each rated on a scale from 0 to 9, with high meaning\r\nharsh. The data from this study are in the Clean moral file, which also\r\ncontains data from a replication by Johnson et al. (2014) — jesci_document_data","title":"also promote kind, altruistic behavior. Brethel-Haurwitz and Marsh (2014)\r\nexamined this idea by collecting data on U.S. states. A Gallup poll in 2010\r\nwas used to measure each state's well-being index, a measure of mean\r\nhappiness for the state's residents on a scale from 0 to 100. Next, a kidney\r\ndonation database for 1999-2010 was used to figure out each state's rate\r\n(number of donations per 1 million people) of non-directed kidney\r\ndonations-that's giving one kidney to a stranger, an extremely generous and\r\naltruistic thing to do!\r\ntheir classic study, Jacowitz and Kahneman (1995) asked participants to\r\nestimate how many babies are born each day in the United States. Participants\r\nwere given either a low anchor (more than 100 babies/day) or a high anchor\r\n(less than 50,000 babies/day). Those who saw the low anchor estimated many\r\nfewer births/day than those who saw the high anchor, which suggests that the\r\nwording can have a profound influence. The correct answer, as it happens, is\r\n~11,000 births/day in 2014. To investigate the extent that these results are\r\nreplicable, the Many Labs project repeated this classic study at many\r\ndifferent labs around the world. You can find the summary data for 30 of\r\nthese labs in the Anchor Estimate ma data file\r\nnumerous other issues can be highly damaging, but are thriving in this social\r\nmedia age. Trying to debunk a conspiracy theory by presenting facts and\r\nevidence often doesn't work, alas. Psychological inoculation, also similar to\r\nprebunking, presents a mild form of misinformation, preferably with\r\nexplanation, in the hope of building resistance to real-life fake news-a sort\r\nof vaccine for fake news. The Bad News game is a spin-off from research on\r\npsychological inoculation. Basol et al. (2020) assessed the possible\r\neffectiveness of this game as a fake news vaccine. At getbadnews.com you can\r\nclick 'About' for information, or just start playing the game- it's easy and\r\nmaybe even fun. You encounter mock Twitter (now X) fake news messages that\r\nillustrate common strategies for making fake news memorable or believable.\r\nYou make choices between messages and decide which ones to 'forward' as you\r\ntry to spread fake news while building your credibility score and number of\r\n'followers'-rather like real life for a conspiracy theorist wanting to spread\r\nthe word. Compete with your friends for credibility and number of followers.\r\nBasol's online participants first saw 18 fictitious fake news tweets and\r\nrated each for reliability (accuracy, believability), and also rated their\r\nconfidence in that reliability rating. Both ratings were on a 1 to 7 scale.\r\nThose in the BadNews group then played the game for 15 minutes, whereas those\r\nin the Control group played Tetris. Then all once again gave reliability and\r\nconfidence ratings for the 18 tweets.\r\ndecade earlier, had been one of several outside experts invited to scrutinize\r\nthe laboratory and experimental procedures of parapsychology researcher\r\nCharles Honorton. Bem not only judged them adequate, but joined the research\r\neffort and became a coauthor with Honorton. Bem and Honorton (1994) first\r\nreviewed early ganzfeld studies and described how the experimental procedure\r\nhad been improved to reduce the chance that results could be influenced by\r\nvarious possible biases, or leakages of information from sender to receiver.\r\nFor example, the randomization procedure was carried out automatically by\r\ncomputer, and all stimuli were presented under computer control. Bem and\r\nHonorton then presented data from studies conducted with the improved\r\nprocedure. Table 13.1 presents basic data from 10 studies reported by Bem and\r\nHonorton (1994). Participants each made a single judgment, so in Pilot 1, for\r\nexample, 22 participants responded, with 8 of them giving a correct response.\r\nThree pilot studies helped refine the procedures, then four studies used\r\nnovice receivers. Study 5 used 20 students of music, drama, or dance as\r\nreceivers, in response to suggestions that creative people might be more\r\nlikely to show telepathy. Studies 6 and 7 used receivers who had participated\r\nin an earlier study. The proportion of hits expected by chance is .25, and\r\nTable 13.1 shows that all but Study 1 found proportions higher than .25.\r\nwho identified as female. Data is Subjective Wellbeing abd Body Satisfaction.\r\nwho identified as male. Data is Subjective Wellbeing abd Body Satisfaction.\r\nReported are measures of Subjective Wellbeing abd Body Satisfaction.\r\nFictituous data from an unrealistically small HEAT\r\nstudy comparing scores for a single group of students before and after a\r\nworkshop on climate change.\r\nSome researchers claim that moral judgments are based not only on rational\r\nconsiderations but also on one's current emotional state. To what extent can\r\nrecent emotional experiences influence moral judgments? Schnall et al. (2008)\r\nexamined this question by manipulating feelings of cleanliness and purity and\r\nthen observing the extent that this changes how harshly participants judge\r\nthe morality of others. Inscho Study 1, Schnall et al. asked participants to\r\ncomplete a word scramble task with either neutral words (neutral prime) or\r\nwords related to cleanliness (cleanliness prime). All students then completed\r\na set of moral judgments about controversial scenarios: Moral judgment is the\r\naverage of six items, each rated on a scale from 0 to 9, with high meaning\r\nharsh. The data from this study are in the Clean moral file, which also\r\ncontains data from a replication by Johnson et al. (2014) — jesci_document_data","text":"psychological behavioral measures. various psychological behavioral measures. performance. first study (Damisch 1) used golf putting task. Students experimental group told using lucky ball (Lucky group). Calin-Jageman Caldwell (2014) reported two studies designed replicate Damisch 1. first (RCJ 1) followed Damisch 1 closely practical, although participants American rather German college students. second (RCJ 2) made several changes designed increase effect superstition putting performance. 138 U.S. adults, recruited August 2018 Prolific Academic, worked online. First, saw six fake headlines four times, time asked rate interesting/engaging/ funny/well-written headline . rating task simply ensured participants paid attention headline. stimuli 12 actual fake-news headlines American politics, accompanying photographs. Half appealed Republicans half Democrats. Later, 12 fake headlines presented one time, random mix six Old headlines-seen -six New headlines seen previously. stated clearly independent, non-partisan fact-checking established headlines true. Participants first rated, 0 () 100 (extremely) scale, degree judged unethical publish headline. Unethicality DV. also rated likely share headline saw posted acquaintance social media; three similar ratings. Finally, rated accurate believed headline . heart rate? investigate, Lakens (2013) asked students record heart rate (beats per minute) rest (baseline) recalling time intense anger. conceptual replication classic study Ekman et al. (1983). Load Emotion heartrate data set book website. extent initial performance class relate performance final exam? First exam final exam scores nine students enrolled introductory psychology course. Exam scores percentages, 0 = answers correct 100 = answers correct. Data synthetic represent patterns found previous psych stats course. extent exposed American flag influence political attitudes? One seminal study (Carter et al., 2011) explored issue subtly exposing participants either images American flag control images. Next, participants asked political attitudes, using 1-7 rating scale high scores indicate conservative attitudes. Participants exposed flag found express substantially conservative attitudes. Many Labs project replicated finding 25 different locations United States. mathematics? investigate, Nosek et al. (2002) asked male female students complete Implicit Association Test (IAT)-task designed measure participant's implicit (non-conscious) feelings towards topic. (never heard IAT, try : tiny.cc/harvardiat) IAT, students tested negative feelings towards mathematics art. Scores reflect degree student negative implicit attitudes mathematics art (positive score: negative feelings mathematics; 0: level negativity ; negative score: negative feelings art). data_gender_math_iat data two labs participated large-scale replication original study (Klein et al., 2014a, 2014b) al. (2002), male female participants completed Implicit Association Test (IAT) measured extent negative attitudes towards mathematics, compared art. study found women, compared men, tended negative implicit attitudes towards mathematics. Many Labs project repeated study locations around world (Klein et al., 2014a, 2014b). Summary data 30 labs available Gender math IAT ma. Higher scores indicate implicit bias mathematics. See also data_gender_math_iat raw data two specific sites replication effort. eating much less delay Alzheimer's? , great news. Halagappa et al. (2007) investigated possibility using mouse model, meaning used Alzheimer-prone mice, genetically predisposed develop neural degeneration typical Alzheimer's. researchers used six independent groups mice, three tested mouse middle age 10 months old, three mouse old age 17 months. age control group normal mice ate freely (NFree10 NFree17 groups), group Alzheimer-prone mice also ate freely (AFree10 AFree17 groups), another Alzheimer-prone group restricted 40% less food normal (ADiet10 ADiet17 groups). Table 14.2 lists factors define groups, group labels. discuss one measure mouse cognition: percent time spent near target water maze, higher values indicating better learning memory. Table 14.2 reports means standard deviations measure, group sizes. Maybe thinking buying house college? Regression can help hunt bargain. Download Home Prices data set. file contains real estate listings 1997 2003 city California. explore extent size home (square meters) predicts sale price. Suppose want change battery phone, cook perfect souffle, perform three-ball juggle. Just numerous people every day, might search online find video shows . Suppose watch video just . First question: well predict perform task? Second question: well actually perform task, first time tried? Now suppose watch video many times: consider two questions. questions investigated series studies Kardas O'Brien (2018). first quick analyses Kardas Experiments 3 4-call Expt 3 Expt 4-focusing effect watching video many times rather . Expt 3, participants first watched brief video person performing moonwalk. Low Exposure group watched video , High Exposure group 20 times. participants predicted, 1 10 scale, well felt able perform moonwalk . Finally, attempted single performance moonwalk, videoed. videos rated, 1 10 scale, independent raters. souffle, perform three-ball juggle. Just numerous people every day, might search online find video shows . Suppose watch video just . First question: well predict perform task? Second question: well actually perform task, first time tried? Now suppose watch video many times: consider two questions. questions investigated series studies Kardas O'Brien (2018). first quick analyses Kardas Experiments 3 4-call Expt 3 Expt 4-focusing effect watching video many times rather . Expt 4 conducted online participants recruited Amazon's Mechanical Turk, typically diverse students. online task based mirror-drawing game developed Bob students (Cusack et al., 2015, tiny.cc/bobmirrortrace). Participants first read description game scoring procedure. play, use computer trackpad trace target line, accurately quickly can. task tricky can see mirror image path tracing finger trackpad. running score displayed. final score percentage match target line path traced, scores can range 0 100 investigate, participants asked participate taste test. participants actually given grape juice, one glass poured bottle labeled 'Organic' glass bottle labeled 'Generic'. tasting (counterbalanced order), participants asked rate much enjoyed juice scale 1 () 10 (much). Participants also asked say much willing pay large container juice scale $1 $10. Load Labels flavor data set book website. data collected part class project Floretta-Schiller et al. (2015), whose work inspired clever study looking effects fast-food wrappers children's enjoyment food (Robinson et al., 2007). researchers interested different study approaches might impact learning. Working France, created three independent groups, comprising 95 adults. Participants worked online seven learning modules DNA. Reread group worked module, worked second time going next module. Quiz group worked module, complete quiz going next module. Prequiz group work quiz seeing presentation module, went quiz presentation next module. Participants received feedback brief explanation answering question quiz, take long wished work module quiz. Seven days later, participants completed final test. data_latimier_3groups full data set. facilitate different student exercises, also seperate data entities group (data_latimier_prequiz, data_latimier_reread, etc.), every pair groups (data_latimier_quiz_prequiz, etc.). Just Prequiz group Latimier et al., 2019 See full details data_latimier_3_groups Just Quiz (RQ) Prequiz (QR) groups Latimier et al., 2019 See full details data_latimier_3_groups Just Quiz group Latimier et al., 2019 See full details data_latimier_3_groups Just Reread (RR) Prequiz (QR) groups Latimier et al., 2019 See full details data_latimier_3_groups Just Reread Quiz groups Latimier et al., 2019 See full details data_latimier_3_groups Just Reread group Latimier et al., 2019 See full details data_latimier_3_groups worked sufficiently hard develop requisite skills? Meta-analysis correlations can help answer questions. issue extent practice effort may sufficient achieving highest levels expertise. Ericsson et al. (1993) argued years effort matters : 'Many characteristics believed reflect innate talent actually result intense practice extended minimum 10 years' (p. 363). view enormously popularized Malcolm Gladwell (2008), argued book Outliers 10,000 hours focused practice key achieving expertise. However, view now challenged, one important contribution large meta-analysis correlations amount intense practice level achievement: Macnamara et al. (2014) combined 157 correlations reported wide range fields, sports music education, found correlation r = .35 .30, .39. Table 11.1 shows 16 main correlations music, Macnamara et al. (2014). brightly colored areas indicating brain regions active particular type cognition emotion. Search online fMRI (functional magnetic resonance imaging) brain scans see pictures learn made. can fascinating-last able see thinking works? 2008, McCabe Castel published studies investigated adding brain picture might alter judgments credibility scientific article. one group participants, article accompanied brain image irrelevant article. second, independent group, image. Participants read article, gave rating statement 'scientific reasoning article made sense'. response options 1 (strongly disagree), 2 (disagree), 3 (agree), 4 (strongly agree). researchers reported mean ratings higher brain picture without, difference small. seemed irrelevant brain picture may , small influence. authors drew appropriately cautious conclusions, result quickly attracted attention many media reports greatly overstated . least according popular media, seemed adding brain picture made story convincing. Search 'McCabe seeing believing', similar, find media reports blog posts. warned readers watch brain pictures, , said, can trick believing things true. result intrigued New Zealander colleagues mine discovered , despite wide recognition, finding replicated. ran replication studies using materials used original researchers, found generally small ESs. joined team data analysis stage research published (Michael et al., 2013). discuss meta-analysis two original studies eight replications team. studies sufficiently similar meta-analysis, especially considering Michael studies designed many features matched original studies. data set include two additional critique studies run Michael team. See also data_mccabemichael_brain2 run Michael team. (2011). People wanted reduce stress, experienced meditators, assigned Meditation (n = 16) Control (n = 17) group. Meditation group participated 8 weeks intensive training practice mindfulness meditation. researchers used questionnaire assess range emotional cognitive variables (Pretest) (Posttest) 8-week period. assessment conducted participants meditating. study notable including brain imaging assess possible changes participants' brains Pretest Posttest. researchers measured gray matter concentration, increases brain regions experience higher frequent activation. researchers expected hippocampus may especially responsive meditation implicated regulation emotion, arousal, general responsiveness. therefore included planned analysis assessment changes gray matter concentration hippocampus. extent might choosing organic foods make us morally smug? investigate, Eskine (2013) asked participants rate images organic food, neutral (control) food, comfort food. Next, guise different study, participants completed moral judgment scale read different controversial scenarios rated morally wrong judged (scale 1-7, high judgments mean wrong). Table 14.7 shows summary data, also available first four variables OrganicMoral file. file can see two variables, report full data-come shortly. use summary data. results Eskine (2013) published, Moery Calin-Jageman (2016) conducted series close replications. obtained original materials Eskine, piloted procedure, preregistered sampling analysis plan. OSF page, osf.io/atkn7, details. data one close replications last two variables OrganicMoral file. replication study, group names variable ReplicationGroup moral judgments MoralJudgment. (may need scroll right see variables.) skills? investigate, Burgmer Englich (2012) assigned German participants either power control conditions asked play golf (Experiment 1) darts (Experiment 2). found participants manipulated feel powerful performed substantially better control condition. study finding , Cusack et al. (2015) conducted five replications United States. Across replications tried different ways manipulating power, different types tasks (golf, mirror tracing, cognitive task), different levels difficulty, different types participant pools (undergraduates online). Summary data seven studies available PowerPerformance ma. reassurance, , instead, encouragement challenge? Carol Dweck colleagues investigated many questions people respond different types feedback. next example comes Dweck's research group illustrates data analysis starts full data, rather summary statistics. Rattan et al. (2012) asked college student participants imagine undertaking mathematics course just received low score (65%) first test year. Participants assigned randomly three groups, received different feedback along low score. Comfort group received positive encouragement also reassurance, Challenge group received positive encouragement also challenge, Control group received just positive encouragement. Participants responded range questions felt course professor. discuss data ratings motivation toward mathematics, made received feedback. childhood? investigate, large international sample children asked play game given 10 stickers asked give stickers away another child able tested day. number stickers donated considered measure altruistic sharing. addition, parents child reported family's religion. Summary data provided. data large online survey participants asked report, scale 0 100, belief existence God. Age also reported. explanations material studying. Self-explaining generally found effective standard studying, may also take time. raises question whether study strategy extra time benefits learning. explore issue, grade school children took pretest mathematics conceptual knowledge, studied mathematics problems, took similar posttest (McEldoon et al., 2013). Participants randomly assigned one two study conditions: normal study + practice (Practice group), self-explaining (Self-Explain group). first condition intended make time spent learning similar two groups. can find part data study SelfExplain, scores percent correct. program, now job develop ad campaign. choose BeforeAfter pair pictures, Figure 14.1, top panel? might Progressive sequence pictures person, bottom panel, effective? Pause, think, discuss. choose, ? might think BeforeAfter simpler dramatic. hand, Progressive highlights steady improvement claim program deliver. probably surprised learn BeforeAfter used often long favorite advertising industry, whereas Progressive used rarely. Luca Cian colleagues (Cian et al., 2020) curious know extent BeforeAfter actually effective, appealing, credible Progressive, , indeed, whether Progressive might score highly. reported seven studies various aspects question. focus Study 2, used three independent groups compare three conditions illustrated Figure 14.1. BeforeAfterInfo condition, middle panel, comprises three BeforeAfter pairs, thus providing extra information endpoints. researchers included condition case advantage Progressive might stem simply images, rather illustrates clear progressive sequence. randomly assigned 213 participants MTurk one three groups. Participants asked 'imagine decided lose weight', saw one three ads weight loss program called MRMDiets. answered question 'evaluate MRMDiets?' choosing 1-7 response several scales, including Unlikeable-Likable, Ineffective-Effective, credible-Credible. researchers averaged six scores give overall Credibility score, 1-7 scale, 7 credible. Simmons Nelson (2020) sufficiently intrigued carry two substantial close replications. cooperation original researchers, used materials procedure. used much larger groups preregistered research plan, including data analysis plan. focus first replication, 761 participants MTurk randomized three groups. decided examine extent sleep relates attractiveness. 70 college students self-reported amount sleep night . addition, photograph taken participant rated attractiveness scale 1 10 two judges opposite gender. average rating score used. can download data set (Sleep Beauty) book website. Stickgold et al. (2000) found , remarkably, performance visual discrimination task actually improved 48-96 hours initial training, even without practice time. However, participants sleep deprived period? trained 11 participants new skill, sleep deprived. data (-14.7, -10.7, -10.7, 2.2, 2.4, 4.5, 7.2, 9.6, 10, 21.3, 21.8)-download Stickgold data set book website. data changes performance scores immediately training night without sleep: 0 represents change, positive scores represent improvement, negative scores represent decline. Data set courtesy DataCrunch (tiny.cc/Stickgold) extent study strategy influence learning? investigate, psychology students randomly assigned three groups asked learn biology facts using one three different strategies: ) Self-Explain (explaining fact new knowledge gained relates already known), b) Elab Interrogation (elaborative interrogation: stating fact makes sense), c) Repetition Control (stating fact ). studying, students took 25-point fill--blank test (O'Reilly et al., 1998) interested ways enhance students' critical thinking. investigating argument mapping, promising way use diagrams represent structure arguments. Students study completed established test critical thinking (Pretest), critical thinking course based argument mapping, second version test (Posttest). might factors cause aggressive behavior? explore, Hilgard (2015) asked male participants play one four versions video game 15 minutes. game customized vary violence (shooting zombies helping aliens) difficulty (targets controlled tough AI dumb AI). game, players provoked given insulting evaluation confederate. Participants got decide long confederate hold hand painfully cold ice water (0 80 seconds), taken measure aggressive behavior. can find materials analysis plan study Open Science Framework: osf. io/cwenz. simplified version full data set.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jesci_document_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"also promote kind, altruistic behavior. Brethel-Haurwitz and Marsh (2014)\r\nexamined this idea by collecting data on U.S. states. A Gallup poll in 2010\r\nwas used to measure each state's well-being index, a measure of mean\r\nhappiness for the state's residents on a scale from 0 to 100. Next, a kidney\r\ndonation database for 1999-2010 was used to figure out each state's rate\r\n(number of donations per 1 million people) of non-directed kidney\r\ndonations-that's giving one kidney to a stranger, an extremely generous and\r\naltruistic thing to do!\r\ntheir classic study, Jacowitz and Kahneman (1995) asked participants to\r\nestimate how many babies are born each day in the United States. Participants\r\nwere given either a low anchor (more than 100 babies/day) or a high anchor\r\n(less than 50,000 babies/day). Those who saw the low anchor estimated many\r\nfewer births/day than those who saw the high anchor, which suggests that the\r\nwording can have a profound influence. The correct answer, as it happens, is\r\n~11,000 births/day in 2014. To investigate the extent that these results are\r\nreplicable, the Many Labs project repeated this classic study at many\r\ndifferent labs around the world. You can find the summary data for 30 of\r\nthese labs in the Anchor Estimate ma data file\r\nnumerous other issues can be highly damaging, but are thriving in this social\r\nmedia age. Trying to debunk a conspiracy theory by presenting facts and\r\nevidence often doesn't work, alas. Psychological inoculation, also similar to\r\nprebunking, presents a mild form of misinformation, preferably with\r\nexplanation, in the hope of building resistance to real-life fake news-a sort\r\nof vaccine for fake news. The Bad News game is a spin-off from research on\r\npsychological inoculation. Basol et al. (2020) assessed the possible\r\neffectiveness of this game as a fake news vaccine. At getbadnews.com you can\r\nclick 'About' for information, or just start playing the game- it's easy and\r\nmaybe even fun. You encounter mock Twitter (now X) fake news messages that\r\nillustrate common strategies for making fake news memorable or believable.\r\nYou make choices between messages and decide which ones to 'forward' as you\r\ntry to spread fake news while building your credibility score and number of\r\n'followers'-rather like real life for a conspiracy theorist wanting to spread\r\nthe word. Compete with your friends for credibility and number of followers.\r\nBasol's online participants first saw 18 fictitious fake news tweets and\r\nrated each for reliability (accuracy, believability), and also rated their\r\nconfidence in that reliability rating. Both ratings were on a 1 to 7 scale.\r\nThose in the BadNews group then played the game for 15 minutes, whereas those\r\nin the Control group played Tetris. Then all once again gave reliability and\r\nconfidence ratings for the 18 tweets.\r\ndecade earlier, had been one of several outside experts invited to scrutinize\r\nthe laboratory and experimental procedures of parapsychology researcher\r\nCharles Honorton. Bem not only judged them adequate, but joined the research\r\neffort and became a coauthor with Honorton. Bem and Honorton (1994) first\r\nreviewed early ganzfeld studies and described how the experimental procedure\r\nhad been improved to reduce the chance that results could be influenced by\r\nvarious possible biases, or leakages of information from sender to receiver.\r\nFor example, the randomization procedure was carried out automatically by\r\ncomputer, and all stimuli were presented under computer control. Bem and\r\nHonorton then presented data from studies conducted with the improved\r\nprocedure. Table 13.1 presents basic data from 10 studies reported by Bem and\r\nHonorton (1994). Participants each made a single judgment, so in Pilot 1, for\r\nexample, 22 participants responded, with 8 of them giving a correct response.\r\nThree pilot studies helped refine the procedures, then four studies used\r\nnovice receivers. Study 5 used 20 students of music, drama, or dance as\r\nreceivers, in response to suggestions that creative people might be more\r\nlikely to show telepathy. Studies 6 and 7 used receivers who had participated\r\nin an earlier study. The proportion of hits expected by chance is .25, and\r\nTable 13.1 shows that all but Study 1 found proportions higher than .25.\r\nwho identified as female. Data is Subjective Wellbeing abd Body Satisfaction.\r\nwho identified as male. Data is Subjective Wellbeing abd Body Satisfaction.\r\nReported are measures of Subjective Wellbeing abd Body Satisfaction.\r\nFictituous data from an unrealistically small HEAT\r\nstudy comparing scores for a single group of students before and after a\r\nworkshop on climate change.\r\nSome researchers claim that moral judgments are based not only on rational\r\nconsiderations but also on one's current emotional state. To what extent can\r\nrecent emotional experiences influence moral judgments? Schnall et al. (2008)\r\nexamined this question by manipulating feelings of cleanliness and purity and\r\nthen observing the extent that this changes how harshly participants judge\r\nthe morality of others. Inscho Study 1, Schnall et al. asked participants to\r\ncomplete a word scramble task with either neutral words (neutral prime) or\r\nwords related to cleanliness (cleanliness prime). All students then completed\r\na set of moral judgments about controversial scenarios: Moral judgment is the\r\naverage of six items, each rated on a scale from 0 to 9, with high meaning\r\nharsh. The data from this study are in the Clean moral file, which also\r\ncontains data from a replication by Johnson et al. (2014) — jesci_document_data","text":"","code":"jesci_document_data(save_files = FALSE)"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_any.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate any meta effect. — meta_any","title":"Estimate any meta effect. — meta_any","text":"meta_any suitable synthesizing effect size across multiple studies. must provide effect size study predicted sampling variance study.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_any.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate any meta effect. — meta_any","text":"","code":"meta_any( data, yi, vi, labels = NULL, moderator = NULL, contrast = NULL, effect_label = \"My effect\", effect_size_name = \"Effect size\", moderator_variable_name = \"My moderator\", random_effects = TRUE, conf_level = 0.95 )"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_any.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate any meta effect. — meta_any","text":"data data frame tibble columns yi Name column data containing effect size study vi Name column data containing expected sampling variance study labels Name column data containing label study moderator Optional name column data containing factor categorical moderator contrast Optional vector specifying contrast analysis categorical moderator. define moderator defined; vector length match number levels moderator effect_label Optional human-friendly name effect synthesized; defaults 'effect' effect_size_name Optional human-friendly name effect size synthesized; defaults 'Effect size' moderator_variable_name Optional human-friendly name moderator, defined; passed moderator defined, set quoted name moderator column 'moderator' random_effects Use TRUE obtain random effect meta-anlaysis (usually reccomended); FALSE fixed effect. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_any.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate any meta effect. — meta_any","text":"esci-estimate object; list data frames properties. Returned tables include: es_meta - data frame meta-analytic effect sizes. moderator defined, additional row level moderator. effect_label - Study label effect_size - Effect size LL - Lower bound conf_level% confidence interval UL - Upper bound conf_level% confidence interval SE - Expected standard error k - Number studies diamond_ratio - ratio random fixed effects meta-analtics effect sizes diamond_ratio_LL - lower bound conf_level% confidence interval diamond ratio diamond_ratio_UL - upper bound conf_level% confidence interval diamond ratio I2 - I2 measure heterogeneity I2_LL - Lower bound conf_level% confidence interval I2 I2_UL - upper bound conf_level% confidence interval I2 PI_LL - lower bound conf_level% prediction interval PI_UL - upper bound conf_level% prediction interval p - p value meta-analytic effect size, based null exactly 0 *width - width effect-size confidence interval FE_effect_size - effect size fixed-effects model (regardless fixed effects selected RE_effect_size - effect size random-effects model (regardless random effects selected FE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio RE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio es_heterogeneity - data frame heterogeneity values conf_level% CIs meta-analytic effect size. moderator defined also reports heterogeneity estimates level moderator. effect_label - study label moderator_variable_name - moderator passed, gives name moderator moderator_level - 'Overall' level moderator, passed measure - Name measure heterogeneity estimate - Value heterogeneity estimate LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval raw_data - data one row study passed label - study label effect_size - effect size weight - study weight meta analysis sample_variance - expected level sampling variation SE - expected standard error LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval mean - used calculate study p value; d value entered study sd - use calculate study p value; set 1 study n - study sample size p - p value study, based null exactly 0","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_any.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate any meta effect. — meta_any","text":"#' generate estimate function, can visualize plot_meta(). meta-analytic effect size, confidence interval heterogeneity estimates come metafor::rma(). diamond ratio confidence interval come CI_diamond_ratio().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d1.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate ameta-analytic Cohen's d1 across multiple studies — meta_d1","title":"Estimate ameta-analytic Cohen's d1 across multiple studies — meta_d1","text":"meta_d1 suitable synthesizing across multiple single-group studies continuous outcome variable, outcome measured scale studies","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate ameta-analytic Cohen's d1 across multiple studies — meta_d1","text":"","code":"meta_d1( data, ds, ns, labels = NULL, moderator = NULL, contrast = NULL, effect_label = \"My effect\", random_effects = TRUE, conf_level = 0.95 )"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate ameta-analytic Cohen's d1 across multiple studies — meta_d1","text":"data data frame tibble ds Set bias-adjusted cohen's d1 values, 1 study ns Set sample sizes, positive integers, 1 study labels Optional set labels, 1 study moderator Optional factor categorical moderator; k > 2 per group contrast Optional vector specifying contrast moderator levels effect_label Optional character providing human-friendly label effect random_effects Boolean; TRUE random effects model; otherwise fixed effects conf_level confidence level confidence interval. Given decimal form. Defaults 0.95.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate ameta-analytic Cohen's d1 across multiple studies — meta_d1","text":"esci-estimate object; list data frames properties. Returned tables include: es_meta - data frame meta-analytic effect sizes. moderator defined, additional row level moderator. effect_label - Study label effect_size - Effect size LL - Lower bound conf_level% confidence interval UL - Upper bound conf_level% confidence interval SE - Expected standard error k - Number studies diamond_ratio - ratio random fixed effects meta-analtics effect sizes diamond_ratio_LL - lower bound conf_level% confidence interval diamond ratio diamond_ratio_UL - upper bound conf_level% confidence interval diamond ratio I2 - I2 measure heterogeneity I2_LL - Lower bound conf_level% confidence interval I2 I2_UL - upper bound conf_level% confidence interval I2 PI_LL - lower bound conf_level% prediction interval PI_UL - upper bound conf_level% prediction interval p - p value meta-analytic effect size, based null exactly 0 *width - width effect-size confidence interval FE_effect_size - effect size fixed-effects model (regardless fixed effects selected RE_effect_size - effect size random-effects model (regardless random effects selected FE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio RE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio es_heterogeneity - data frame heterogeneity values conf_level% CIs meta-analytic effect size. moderator defined also reports heterogeneity estimates level moderator. effect_label - study label moderator_variable_name - moderator passed, gives name moderator moderator_level - 'Overall' level moderator, passed measure - Name measure heterogeneity estimate - Value heterogeneity estimate LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval raw_data - data one row study passed label - study label effect_size - effect size weight - study weight meta analysis sample_variance - expected level sampling variation SE - expected standard error LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval mean - used calculate study p value; d value entered study sd - use calculate study p value; set 1 study n - study sample size p - p value study, based null exactly 0","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d1.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate ameta-analytic Cohen's d1 across multiple studies — meta_d1","text":"generate estimate function, can visualize plot_meta(). study's effect size expressed Cohen's d1: (mean - reference) / sd. d1 values corrected bias. function CI_smd_one() can assist converting raw data study d1_unbiased. meta-analytic effect size, confidence interval heterogeneity estimates come metafor::rma(). diamond ratio confidence interval come CI_diamond_ratio().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate ameta-analytic Cohen's d1 across multiple studies — meta_d1","text":"","code":"# example code original_7 <- data.frame( study_name = c( \"Aden (1993)\" , \"Buggs (1995)\" , \"Crazed (1999)\" , \"Dudley (2003)\" , \"Evers (2005)\" , \"Fox (2009)\", \"Mine (2011)\" ), rt_mean = c( 454 , 317 , 430 , 525 , 479 , 387, 531 ), rt_sd = c( 142 , 158 , 137 , 260 , 144 , 165, 233 ), rt_n = c( 24 , 7 , 20 , 8 , 14 , 13, 18 ), subset = as.factor( c( \"90s\", \"90s\", \"90s\", \"00s\", \"00s\", \"00s\", \"00s\" ) ), d1_unbiased = c( 3.091587, 1.742751, 3.012857, 1.793487, 3.130074, 2.195209, 2.17667 ) ) # Fixed effect, 95% CI estimate <- esci::meta_d1( original_7, d1_unbiased, rt_n, study_name, random_effects = FALSE ) estimate <- esci::meta_d1( data = original_7, ds = d1_unbiased, ns = rt_n, moderator = subset, labels = study_name, random_effects = FALSE ) if (FALSE) { # Forest plot esci::plot_meta(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d2.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate meta-analytic standardized mean difference across multiple\r\ntwo group studies (all paired, all independent, or a mix). — meta_d2","title":"Estimate meta-analytic standardized mean difference across multiple\r\ntwo group studies (all paired, all independent, or a mix). — meta_d2","text":"meta_d2 suitable synthesizing across multiple two-group studies (paired independent) continuous outcome measure studies measured scale, instead magnitude difference study expressed d_s d_avg.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate meta-analytic standardized mean difference across multiple\r\ntwo group studies (all paired, all independent, or a mix). — meta_d2","text":"","code":"meta_d2( data, ds, comparison_ns, reference_ns, r = NULL, labels = NULL, moderator = NULL, contrast = NULL, effect_label = \"My effect\", assume_equal_variance = FALSE, random_effects = TRUE, conf_level = 0.95 )"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate meta-analytic standardized mean difference across multiple\r\ntwo group studies (all paired, all independent, or a mix). — meta_d2","text":"data data frame tibble ds Set bias-adjusted cohen's d_s d_avg values, 1 study comparison_ns Set comparison_group sample sizes, positive integers, 1 study reference_ns Set reference_groups sample sizes, positive integers, 1 study r optional correlation measures w-s studies, NA otherwise labels Optional set labels, 1 study moderator Optional factor categorical moderator; k > 2 per group contrast Optional vector specifying contrast moderator levels effect_label Optional character providing human-friendly label effect assume_equal_variance Defaults FALSE random_effects Boolean; TRUE random effects model; otherwise fixed effects conf_level confidence level confidence interval. Given decimal form. Defaults 0.95.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate meta-analytic standardized mean difference across multiple\r\ntwo group studies (all paired, all independent, or a mix). — meta_d2","text":"esci-estimate object; list data frames properties. Returned tables include: es_meta - data frame meta-analytic effect sizes. moderator defined, additional row level moderator. effect_label - Study label effect_size - Effect size LL - Lower bound conf_level% confidence interval UL - Upper bound conf_level% confidence interval SE - Expected standard error k - Number studies diamond_ratio - ratio random fixed effects meta-analtics effect sizes diamond_ratio_LL - lower bound conf_level% confidence interval diamond ratio diamond_ratio_UL - upper bound conf_level% confidence interval diamond ratio I2 - I2 measure heterogeneity I2_LL - Lower bound conf_level% confidence interval I2 I2_UL - upper bound conf_level% confidence interval I2 PI_LL - lower bound conf_level% prediction interval PI_UL - upper bound conf_level% prediction interval p - p value meta-analytic effect size, based null exactly 0 *width - width effect-size confidence interval FE_effect_size - effect size fixed-effects model (regardless fixed effects selected RE_effect_size - effect size random-effects model (regardless random effects selected FE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio RE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio es_heterogeneity - data frame heterogeneity values conf_level% CIs meta-analytic effect size. moderator defined also reports heterogeneity estimates level moderator. effect_label - study label moderator_variable_name - moderator passed, gives name moderator moderator_level - 'Overall' level moderator, passed measure - Name measure heterogeneity estimate - Value heterogeneity estimate LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval raw_data - data one row study passed label - study label effect_size - effect size weight - study weight meta analysis sample_variance - expected level sampling variation SE - expected standard error LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval mean - used calculate study p value; d value entered study sd - use calculate study p value; set 1 study n - study sample size p - p value study, based null exactly 0","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate meta-analytic standardized mean difference across multiple\r\ntwo group studies (all paired, all independent, or a mix). — meta_d2","text":"generate estimate function, can visualize plot_meta(). study's effect size expressed : Cohen's d_s: (comparison_mean - reference_mean) / sd_pooled Cohen_'s d_avg: (comparison_mean - reference_mean) / sd_avg enter d_s, set assume_equal_variance TRUE enter d_avg, set assume_equal_variance FALSE d values corrected bias. function CI_smd_ind_contrast() can assist converting raw data study d_s d_avg bias correction. als details calculation forms d CIs. meta-analytic effect size, confidence interval heterogeneity estimates come metafor::rma(). diamond ratio confidence interval come CI_diamond_ratio().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate meta-analytic standardized mean difference across multiple\r\ntwo group studies (all paired, all independent, or a mix). — meta_d2","text":"","code":"# Data set -- see Introduction to the New Statistics, 1st edition lucky_golf <- data.frame( study = c(paste(\"Damisch\", seq(1:6)), \"Calin 1\", \"Calin 2\"), my_smd = c(0.83, 0.986, 0.66, 0.78, 0.979, 0.86, 0.05, 0.047), smd_corrected = c(0.806, 0.963, 0.647, 0.758, 0.950, 0.835, 0.050, 0.047), n1 = c(14, 17, 20, 15, 14, 14, 58, 54), n2 = c(14, 17, 21, 14, 14, 14, 66, 57), subset = as.factor(c(rep(\"Germany\", times = 6), rep(\"USA\", times = 2))) ) # Meta-analysis, random effects, assuming equal variance, no moderator estimate <- esci::meta_d2( data = lucky_golf, ds = smd_corrected, comparison_ns = n1, reference_ns = n2, labels = study, assume_equal_variance = TRUE, random_effects = TRUE ) if (FALSE) { # Forest plot esci::plot_meta(estimate) } # Meta-analysis, random effects, assuming equal variance estimate <- esci::meta_d2( data = lucky_golf, ds = smd_corrected, comparison_ns = n1, reference_ns = n2, moderator = subset, labels = study, assume_equal_variance = TRUE, random_effects = TRUE ) if (FALSE) { # Forest plot esci::plot_meta(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mdiff_two.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate meta-analytic difference in means across multiple two-group studies. — meta_mdiff_two","title":"Estimate meta-analytic difference in means across multiple two-group studies. — meta_mdiff_two","text":"meta_mdiff_two suitable synthesizing across multiple two-group studies (paired independent) continuous outcome measure. takes raw data study. studies used measurement scale, meta-analytic raw-score difference can returned. studies used different scales, standardized mean difference can returned. Studies can paired, independent, mix. Equal variance can assumed, . standardized mean difference output, d_s equal variance assumed d_avg equal variance assumed.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mdiff_two.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate meta-analytic difference in means across multiple two-group studies. — meta_mdiff_two","text":"","code":"meta_mdiff_two( data, comparison_means, comparison_sds, comparison_ns, reference_means, reference_sds, reference_ns, r = NULL, labels = NULL, moderator = NULL, contrast = NULL, effect_label = \"My effect\", reported_effect_size = c(\"mean_difference\", \"smd_unbiased\", \"smd\"), assume_equal_variance = FALSE, random_effects = TRUE, conf_level = 0.95 )"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mdiff_two.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate meta-analytic difference in means across multiple two-group studies. — meta_mdiff_two","text":"data data frame tibble comparison_means Set comparison_group means, 1 per study comparison_sds Set comparison_group standard deviations, 1 per study, > 0 comparison_ns Set comparison_group sample sizes, positive integers, 1 study reference_means Set reference_group means, 1 per study reference_sds Set comparison_group standard deviations, 1 per study, > 0 reference_ns Set reference_group sample sizes, positive integers, 1 study r Optional correlation measures w-s studies, NA otherwise labels optional collection study labels moderator optional factor analyze categorical moderator, must k > 2 per groups contrast optional contrast estimate moderator levels; express vector contrast weights 1 weight per moderator level. effect_label Optional character giving human-friendly name effect synthesized reported_effect_size Character specifying effect size return: Must one 'mean_difference', 'smd_unbiased' (return unbiased Cohen's d_s d_avg) 'smd' (return d_s d_avg without correction bias). Defaults mean_difference. assume_equal_variance Defaults FALSE random_effects TRUE random effect model; FALSE fixed effects conf_level confidence level confidence interval. Given decimal form. Defaults 0.95.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mdiff_two.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate meta-analytic difference in means across multiple two-group studies. — meta_mdiff_two","text":"esci-estimate object; list data frames properties. Returned tables include: es_meta - data frame meta-analytic effect sizes. moderator defined, additional row level moderator. effect_label - Study label effect_size - Effect size LL - Lower bound conf_level% confidence interval UL - Upper bound conf_level% confidence interval SE - Expected standard error k - Number studies diamond_ratio - ratio random fixed effects meta-analtics effect sizes diamond_ratio_LL - lower bound conf_level% confidence interval diamond ratio diamond_ratio_UL - upper bound conf_level% confidence interval diamond ratio I2 - I2 measure heterogeneity I2_LL - Lower bound conf_level% confidence interval I2 I2_UL - upper bound conf_level% confidence interval I2 PI_LL - lower bound conf_level% prediction interval PI_UL - upper bound conf_level% prediction interval p - p value meta-analytic effect size, based null exactly 0 *width - width effect-size confidence interval FE_effect_size - effect size fixed-effects model (regardless fixed effects selected RE_effect_size - effect size random-effects model (regardless random effects selected FE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio RE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio es_heterogeneity - data frame heterogeneity values conf_level% CIs meta-analytic effect size. moderator defined also reports heterogeneity estimates level moderator. effect_label - study label moderator_variable_name - moderator passed, gives name moderator moderator_level - 'Overall' level moderator, passed measure - Name measure heterogeneity estimate - Value heterogeneity estimate LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval raw_data - data one row study passed label - study label effect_size - effect size weight - study weight meta analysis sample_variance - expected level sampling variation SE - expected standard error LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval mean - used calculate study p value; d value entered study sd - use calculate study p value; set 1 study n - study sample size p - p value study, based null exactly 0","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mdiff_two.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate meta-analytic difference in means across multiple two-group studies. — meta_mdiff_two","text":"generate estimate function, can visualize plot_meta(). meta-analytic effect size, confidence interval heterogeneity estimates come metafor::rma(). diamond ratio confidence interval come CI_diamond_ratio(). reported_effect_size smd_unbiased smd conversion Cohen's d handled CI_smd_ind_contrast().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mdiff_two.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate meta-analytic difference in means across multiple two-group studies. — meta_mdiff_two","text":"","code":"# Data set -- see Introduction to the New Statistics, 1st edition brain_scan_persuasion <- data.frame( study_name = c(\"McCabe 1\", \"McCabe 2\", paste(\"Michael\", seq(1:10), sep = \" \")), nbM = c(2.89, 2.69, 2.90, 2.62, 2.96, 2.93, 2.86, 2.50, 2.41, 2.54, 2.73, 2.66), nbS = c(0.79, 0.55, 0.58, 0.54, 0.36, 0.60, 0.59, 0.84, 0.78, 0.66, 0.67, 0.65), nbN = c(28, 26, 98, 42, 24, 184, 274, 58, 34, 99, 98, 94), bM = c(3.12, 3.00, 2.86, 2.85, 3.07, 2.89, 2.91, 2.60, 2.74, 2.72, 2.68, 2.64), bS = c(0.65, 0.54, 0.61, 0.57, 0.55, 0.60, 0.52, 0.83, 0.51, 0.68, 0.69, 0.71), bN = c(26, 28, 99, 33, 21, 184, 255, 55, 34, 95, 93, 97), mod = as.factor( c(\"Simple\", \"Critique\", \"Simple\",\"Simple\",\"Simple\", \"Simple\",\"Simple\",\"Critique\",\"Critique\",\"Critique\", \"Critique\",\"Critique\") ), ds = c(0.31217944, 0.56073138, -0.06693802, 0.41136192, 0.23581389, -0.06652995, 0.08958082, 0.11892778, 0.49506069, 0.26765910, -0.07326, -0.02925) ) # Meta-analysis: random effects, no moderator estimate <- esci::meta_mdiff_two( data = brain_scan_persuasion, comparison_means = bM, comparison_sds = bS, comparison_ns = bN, reference_means = nbM, reference_sds = nbS, reference_ns = nbN, labels = study_name, effect_label = \"Brain Photo Rating - No Brain Photo Rating\", assume_equal_variance = TRUE, random_effects = TRUE ) if (FALSE) { # Forest plot esci::plot_meta(estimate) } # Meta-analysis: random effects, moderator if (FALSE) { # Forest plot esci::plot_meta(estimate) } # Meta-analysis: random effects, moderator, output d_s if (FALSE) { # Forest plot esci::plot_meta(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate a meta-analytic mean across multiple single-group studies. — meta_mean","title":"Estimate a meta-analytic mean across multiple single-group studies. — meta_mean","text":"meta_mean suitable synthesizing across multiple single-group studies continuous outcome variable studies measured scale.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate a meta-analytic mean across multiple single-group studies. — meta_mean","text":"","code":"meta_mean( data, means, sds, ns, labels = NULL, moderator = NULL, contrast = NULL, effect_label = \"My effect\", reference_mean = 0, reported_effect_size = c(\"mean_difference\", \"smd_unbiased\", \"smd\"), random_effects = TRUE, conf_level = 0.95 )"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate a meta-analytic mean across multiple single-group studies. — meta_mean","text":"data dataframe tibble means collection study means, 1 per study sds collection study standard deviations, 1 per study, >0 ns collection sample sizes, 1 per study, integers > 2 labels optional collection study labels moderator optional factor analyze categorical moderator, must k > 2 per groups contrast optional contrast estimate moderator levels; express vector contrast weights 1 weight per moderator level. effect_label Optional character giving human-friendly name effect synthesized reference_mean Optional reference mean, defaults 0 reported_effect_size Character specifying effect size return; Must one 'mean_difference', 'smd_unbiased' (return unbiased Cohen's d1) 'smd' (return Cohen's d1 without correction bias) random_effects TRUE random effect model; FALSE fixed effects conf_level confidence level confidence interval. Given decimal form. Defaults 0.95.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate a meta-analytic mean across multiple single-group studies. — meta_mean","text":"esci-estimate object; list data frames properties. Returned tables include: es_meta - data frame meta-analytic effect sizes. moderator defined, additional row level moderator. effect_label - Study label effect_size - Effect size LL - Lower bound conf_level% confidence interval UL - Upper bound conf_level% confidence interval SE - Expected standard error k - Number studies diamond_ratio - ratio random fixed effects meta-analtics effect sizes diamond_ratio_LL - lower bound conf_level% confidence interval diamond ratio diamond_ratio_UL - upper bound conf_level% confidence interval diamond ratio I2 - I2 measure heterogeneity I2_LL - Lower bound conf_level% confidence interval I2 I2_UL - upper bound conf_level% confidence interval I2 PI_LL - lower bound conf_level% prediction interval PI_UL - upper bound conf_level% prediction interval p - p value meta-analytic effect size, based null exactly 0 *width - width effect-size confidence interval FE_effect_size - effect size fixed-effects model (regardless fixed effects selected RE_effect_size - effect size random-effects model (regardless random effects selected FE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio RE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio es_heterogeneity - data frame heterogeneity values conf_level% CIs meta-analytic effect size. moderator defined also reports heterogeneity estimates level moderator. effect_label - study label moderator_variable_name - moderator passed, gives name moderator moderator_level - 'Overall' level moderator, passed measure - Name measure heterogeneity estimate - Value heterogeneity estimate LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval raw_data - data one row study passed label - study label effect_size - effect size weight - study weight meta analysis sample_variance - expected level sampling variation SE - expected standard error LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval mean - used calculate study p value; d value entered study sd - use calculate study p value; set 1 study n - study sample size p - p value study, based null exactly 0","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate a meta-analytic mean across multiple single-group studies. — meta_mean","text":"meta-analytic effect size, confidence interval heterogeneity estimates come metafor::rma(). diamond ratio confidence interval come CI_diamond_ratio(). reported_effect_size smd_unbiased smd conversion d1 handled CI_smd_one().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate a meta-analytic mean across multiple single-group studies. — meta_mean","text":"","code":"# example code original_7 <- data.frame( study_name = c( \"Aden (1993)\" , \"Buggs (1995)\" , \"Crazed (1999)\" , \"Dudley (2003)\" , \"Evers (2005)\" , \"Fox (2009)\", \"Mine (2011)\" ), rt_mean = c( 454 , 317 , 430 , 525 , 479 , 387, 531 ), rt_sd = c( 142 , 158 , 137 , 260 , 144 , 165, 233 ), rt_n = c( 24 , 7 , 20 , 8 , 14 , 13, 18 ), subset = as.factor( c( \"90s\", \"90s\", \"90s\", \"00s\", \"00s\", \"00s\", \"00s\" ) ), d1_unbiased = c( 3.091587, 1.742751, 3.012857, 1.793487, 3.130074, 2.195209, 2.17667 ) ) # Fixed effect, 95% CI estimate <- esci::meta_mean( original_7, rt_mean, rt_sd, rt_n, study_name, random_effects = FALSE ) # Random effects, categorical moderator, report cohen's d1 estimate <- esci::meta_mean( original_7, rt_mean, rt_sd, rt_n, study_name, subset, reported_effect_size = \"smd_unbiased\" ) if (FALSE) { # Forest plot esci::plot_meta(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_pdiff_two.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate meta-analytic difference in proportions over multiple studies\r\nwith two independent groups and a categorical outcome variable. — meta_pdiff_two","title":"Estimate meta-analytic difference in proportions over multiple studies\r\nwith two independent groups and a categorical outcome variable. — meta_pdiff_two","text":"meta_pdiff_two suitable synthesizing across multiple two-group studies categorical outcome variable. takes input number cases/events comparison reference groups well total number samples comparison reference groups.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_pdiff_two.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate meta-analytic difference in proportions over multiple studies\r\nwith two independent groups and a categorical outcome variable. — meta_pdiff_two","text":"","code":"meta_pdiff_two( data, comparison_cases, comparison_ns, reference_cases, reference_ns, labels = NULL, moderator = NULL, contrast = NULL, effect_label = \"My effect\", reported_effect_size = c(\"RD\", \"RR\", \"OR\", \"AS\", \"PETO\"), random_effects = TRUE, conf_level = 0.95 )"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_pdiff_two.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate meta-analytic difference in proportions over multiple studies\r\nwith two independent groups and a categorical outcome variable. — meta_pdiff_two","text":"data dataframe tibble comparison_cases collection case/event counts comparison groups, 1 per study, integers >= 0 comparison_ns collection sample sizes comparison groups, 1 per study, integers > 2 reference_cases collection case/event counts reference groups, 1 per study, integers >= 0 reference_ns collection sample sizes reference groups, 1 per study, integers > 2 labels optional collection study labels moderator optional factor analyze categorical moderator, must k > 2 per groups contrast optional contrast estimate moderator levels; express vector contrast weights 1 weight per moderator level. effect_label Optional character giving human-friendly name effect synthesized reported_effect_size Character specifying effect size return: Must one 'RD' (risk difference, default), 'RR' (log risk ratio), '' (log odds ratio), '' (arcsine square root transformed risk difference), 'PETO' (log odds ratio estimated using Peto's method). See metafor::escalc() details. random_effects TRUE random effect model; FALSE fixed effects conf_level confidence level confidence interval. Given decimal form. Defaults 0.95.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_pdiff_two.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate meta-analytic difference in proportions over multiple studies\r\nwith two independent groups and a categorical outcome variable. — meta_pdiff_two","text":"esci-estimate object; list data frames properties. Returned tables include: es_meta - data frame meta-analytic effect sizes. moderator defined, additional row level moderator. effect_label - Study label effect_size - Effect size LL - Lower bound conf_level% confidence interval UL - Upper bound conf_level% confidence interval SE - Expected standard error k - Number studies diamond_ratio - ratio random fixed effects meta-analtics effect sizes diamond_ratio_LL - lower bound conf_level% confidence interval diamond ratio diamond_ratio_UL - upper bound conf_level% confidence interval diamond ratio I2 - I2 measure heterogeneity I2_LL - Lower bound conf_level% confidence interval I2 I2_UL - upper bound conf_level% confidence interval I2 PI_LL - lower bound conf_level% prediction interval PI_UL - upper bound conf_level% prediction interval p - p value meta-analytic effect size, based null exactly 0 *width - width effect-size confidence interval FE_effect_size - effect size fixed-effects model (regardless fixed effects selected RE_effect_size - effect size random-effects model (regardless random effects selected FE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio RE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio es_heterogeneity - data frame heterogeneity values conf_level% CIs meta-analytic effect size. moderator defined also reports heterogeneity estimates level moderator. effect_label - study label moderator_variable_name - moderator passed, gives name moderator moderator_level - 'Overall' level moderator, passed measure - Name measure heterogeneity estimate - Value heterogeneity estimate LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval raw_data - data one row study passed label - study label effect_size - effect size weight - study weight meta analysis sample_variance - expected level sampling variation SE - expected standard error LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval mean - used calculate study p value; d value entered study sd - use calculate study p value; set 1 study n - study sample size p - p value study, based null exactly 0","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_pdiff_two.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate meta-analytic difference in proportions over multiple studies\r\nwith two independent groups and a categorical outcome variable. — meta_pdiff_two","text":"generate estimate function, can visualize plot_meta(). meta-analytic effect size, confidence interval heterogeneity estimates come metafor::rma(). conversion events suitable effect sizes handled metafor::escalc()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_pdiff_two.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate meta-analytic difference in proportions over multiple studies\r\nwith two independent groups and a categorical outcome variable. — meta_pdiff_two","text":"","code":"# Data set: Replications of power on egocentric behavior esci_meta_pdiff_two <- data.frame( studies = c( \"Online\", \"Original\", \"Online Pilot\", \"Exact replication\" ), control_egocentric = c( 33, 4, 4, 7 ), control_sample_size = c( 101, 33, 10, 53 ), power_egocentric = c( 48, 8, 4, 11 ), power_sample_size = c( 105, 24, 12, 56 ), setting = as.factor( c( \"Online\", \"In-Person\", \"Online\", \"In-Person\" ) ) ) # Meta-analysis, risk difference as effect size estimate <- esci::meta_pdiff_two( esci_meta_pdiff_two, power_egocentric, power_sample_size, control_egocentric, control_sample_size, studies, reported_effect_size = \"RD\" ) if (FALSE) { # Forest plot esci::plot_meta(estimate) } # Meta-analysis, risk difference as effect size, moderator (setting) estimate <- esci::meta_pdiff_two( esci_meta_pdiff_two, power_egocentric, power_sample_size, control_egocentric, control_sample_size, studies, moderator = setting, reported_effect_size = \"RD\" ) if (FALSE) { # Forest plot esci::plot_meta(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate a meta-analytic proportion of outcomes over multiple studies with\r\na categorical outcome variable. — meta_proportion","title":"Estimate a meta-analytic proportion of outcomes over multiple studies with\r\na categorical outcome variable. — meta_proportion","text":"meta_proportion suitable synthesizing across multiple studies categorical outcome variable. takes input number cases/events number samples study.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate a meta-analytic proportion of outcomes over multiple studies with\r\na categorical outcome variable. — meta_proportion","text":"","code":"meta_proportion( data, cases, ns, labels = NULL, moderator = NULL, contrast = NULL, effect_label = \"My effect\", random_effects = TRUE, conf_level = 0.95 )"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_proportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate a meta-analytic proportion of outcomes over multiple studies with\r\na categorical outcome variable. — meta_proportion","text":"data dataframe tibble cases collection cases/event counts, 1 per study, integers, > 0 ns collection sample sizes, 1 per study, integers > 2 labels optional collection study labels moderator optional factor analyze categorical moderator, must k > 2 per groups contrast optional contrast estimate moderator levels; express vector contrast weights 1 weight per moderator level. effect_label Optional character giving human-friendly name effect synthesized random_effects TRUE random effect model; FALSE fixed effects conf_level confidence level confidence interval. Given decimal form. Defaults 0.95.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate a meta-analytic proportion of outcomes over multiple studies with\r\na categorical outcome variable. — meta_proportion","text":"esci-estimate object; list data frames properties. Returned tables include: es_meta - data frame meta-analytic effect sizes. moderator defined, additional row level moderator. effect_label - Study label effect_size - Effect size LL - Lower bound conf_level% confidence interval UL - Upper bound conf_level% confidence interval SE - Expected standard error k - Number studies diamond_ratio - ratio random fixed effects meta-analtics effect sizes diamond_ratio_LL - lower bound conf_level% confidence interval diamond ratio diamond_ratio_UL - upper bound conf_level% confidence interval diamond ratio I2 - I2 measure heterogeneity I2_LL - Lower bound conf_level% confidence interval I2 I2_UL - upper bound conf_level% confidence interval I2 PI_LL - lower bound conf_level% prediction interval PI_UL - upper bound conf_level% prediction interval p - p value meta-analytic effect size, based null exactly 0 *width - width effect-size confidence interval FE_effect_size - effect size fixed-effects model (regardless fixed effects selected RE_effect_size - effect size random-effects model (regardless random effects selected FE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio RE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio es_heterogeneity - data frame heterogeneity values conf_level% CIs meta-analytic effect size. moderator defined also reports heterogeneity estimates level moderator. effect_label - study label moderator_variable_name - moderator passed, gives name moderator moderator_level - 'Overall' level moderator, passed measure - Name measure heterogeneity estimate - Value heterogeneity estimate LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval raw_data - data one row study passed label - study label effect_size - effect size weight - study weight meta analysis sample_variance - expected level sampling variation SE - expected standard error LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval mean - used calculate study p value; d value entered study sd - use calculate study p value; set 1 study n - study sample size p - p value study, based null exactly 0","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_proportion.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate a meta-analytic proportion of outcomes over multiple studies with\r\na categorical outcome variable. — meta_proportion","text":"generate estimate function, can visualize plot_meta(). meta-analytic effect size, confidence interval heterogeneity estimates come metafor::rma().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_proportion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate a meta-analytic proportion of outcomes over multiple studies with\r\na categorical outcome variable. — meta_proportion","text":"","code":"# Data set: Replications of power on egocentric behavior esci_meta_pdiff_two <- data.frame( studies = c( \"Online\", \"Original\", \"Online Pilot\", \"Exact replication\" ), control_egocentric = c( 33, 4, 4, 7 ), control_sample_size = c( 101, 33, 10, 53 ), power_egocentric = c( 48, 8, 4, 11 ), power_sample_size = c( 105, 24, 12, 56 ), setting = as.factor( c( \"Online\", \"In-Person\", \"Online\", \"In-Person\" ) ) ) # Meta-analysis, risk difference as effect size estimate <- esci::meta_proportion( esci_meta_pdiff_two, power_egocentric, power_sample_size, studies ) if (FALSE) { # Forest plot esci::plot_meta(estimate) } # Meta-analysis, risk difference as effect size, moderator (setting) estimate <- esci::meta_proportion( esci_meta_pdiff_two, power_egocentric, power_sample_size, studies, moderator = setting ) if (FALSE) { # Forest plot esci::plot_meta(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_r.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate meta-analytic Pearson's r across multiple studies with two\r\ncontinuous outcome variables. — meta_r","title":"Estimate meta-analytic Pearson's r across multiple studies with two\r\ncontinuous outcome variables. — meta_r","text":"meta_r suitable synthesizing across multiple studies measured linear correlation (Pearson's r) two continuyous variables.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_r.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate meta-analytic Pearson's r across multiple studies with two\r\ncontinuous outcome variables. — meta_r","text":"","code":"meta_r( data, rs, ns, labels = NULL, moderator = NULL, contrast = NULL, effect_label = \"My effect\", random_effects = TRUE, conf_level = 0.95 )"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_r.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate meta-analytic Pearson's r across multiple studies with two\r\ncontinuous outcome variables. — meta_r","text":"data dataframe tibble rs collection Pearson's r values, 1 per study, -1 1, inclusive ns collection study sample sizes, integers > 2 labels optional collection study labels moderator optional factor analyze categorical moderator, must k > 2 per groups contrast optional contrast estimate moderator levels; express vector contrast weights 1 weight per moderator level. effect_label Optional character giving human-friendly name effect synthesized random_effects TRUE random effect model; FALSE fixed effects conf_level confidence level confidence interval. Given decimal form. Defaults 0.95.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_r.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate meta-analytic Pearson's r across multiple studies with two\r\ncontinuous outcome variables. — meta_r","text":"esci-estimate object; list data frames properties. Returned tables include: es_meta - data frame meta-analytic effect sizes. moderator defined, additional row level moderator. effect_label - Study label effect_size - Effect size LL - Lower bound conf_level% confidence interval UL - Upper bound conf_level% confidence interval SE - Expected standard error k - Number studies diamond_ratio - ratio random fixed effects meta-analtics effect sizes diamond_ratio_LL - lower bound conf_level% confidence interval diamond ratio diamond_ratio_UL - upper bound conf_level% confidence interval diamond ratio I2 - I2 measure heterogeneity I2_LL - Lower bound conf_level% confidence interval I2 I2_UL - upper bound conf_level% confidence interval I2 PI_LL - lower bound conf_level% prediction interval PI_UL - upper bound conf_level% prediction interval p - p value meta-analytic effect size, based null exactly 0 *width - width effect-size confidence interval FE_effect_size - effect size fixed-effects model (regardless fixed effects selected RE_effect_size - effect size random-effects model (regardless random effects selected FE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio RE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio es_heterogeneity - data frame heterogeneity values conf_level% CIs meta-analytic effect size. moderator defined also reports heterogeneity estimates level moderator. effect_label - study label moderator_variable_name - moderator passed, gives name moderator moderator_level - 'Overall' level moderator, passed measure - Name measure heterogeneity estimate - Value heterogeneity estimate LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval raw_data - data one row study passed label - study label effect_size - effect size weight - study weight meta analysis sample_variance - expected level sampling variation SE - expected standard error LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval mean - used calculate study p value; d value entered study sd - use calculate study p value; set 1 study n - study sample size p - p value study, based null exactly 0","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_r.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate meta-analytic Pearson's r across multiple studies with two\r\ncontinuous outcome variables. — meta_r","text":"generate estimate function, can visualize plot_meta(). meta-analytic effect size, confidence interval heterogeneity estimates come metafor::rma().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_r.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate meta-analytic Pearson's r across multiple studies with two\r\ncontinuous outcome variables. — meta_r","text":"","code":"# Data: See Introduction to the New Statistics, first edition esci_single_r <- data.frame( studies = c( 'Violin, viola' , 'Strings' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'All' , 'Piano' , 'Piano' , 'Band' , 'Music majors' , 'Music majors' , 'All' ), rvalues = c( .67, .51, .4, .46, .47, .228, -.224, .104, .322, .231, .67, .41, .34, .31, .54, .583 ), sample_size = c( 109, 55, 19, 30, 19, 52, 24, 52, 16, 97, 57, 107, 178, 64, 19, 135 ), subsets = as.factor( c( 'Strings' , 'Strings' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Strings' , 'Strings' , 'Strings' , 'Strings' ) ) ) # Meta-analysis, random effects, no moderator estimate <- esci::meta_r( esci_single_r, rvalues, sample_size, studies, random_effects = TRUE ) if (FALSE) { # Forest plot esci::plot_meta(estimate) } # Meta-analysis, random effects, moderator (subsets) estimate <- esci::meta_r( esci_single_r, rvalues, sample_size, studies, subsets, random_effects = TRUE ) if (FALSE) { # Forest plot esci::plot_meta(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_smd_two.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate meta-analytic d_s or d_avg across multiple two group studies. — meta_smd_two","title":"Estimate meta-analytic d_s or d_avg across multiple two group studies. — meta_smd_two","text":"meta_smd_two suitable synthesizing across multiple two-group studies outcome variable continuous studies measured scale.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_smd_two.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate meta-analytic d_s or d_avg across multiple two group studies. — meta_smd_two","text":"","code":"meta_smd_two( data, smds, comparison_ns, reference_ns, labels = NULL, moderator = NULL, contrast = NULL, effect_label = \"My effect\", assume_equal_variance = FALSE, correct_bias = TRUE, esci_vi = FALSE, random_effects = TRUE, conf_level = 0.95 )"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_smd_two.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate meta-analytic d_s or d_avg across multiple two group studies. — meta_smd_two","text":"data data frame tibble smds Name column data containing Cohen's d study comparison_ns Name column data containing sample size comparison group study; integer > 0. reference_ns Name column data containing sample size reference group study; integer > 0. labels Name column data containing label study moderator Optional name column data containing factor categorical moderator contrast Optional vector specifying contrast analysis categorical moderator. define moderator defined; vector length match number levels moderator effect_label Optional human-friendly name effect synthesized; defaults 'effect' assume_equal_variance TRUE assume equal variance, meaning effect sizes provided d_s; FALSE assume equal variance, meaning effect sizes provided d_avg correct_bias TRUE apply bias correction effect sizes provided conducting meta-analysis esci_vi TRUE report sampling variance based Bonnett rather metafor random_effects Use TRUE obtain random effect meta-anlaysis (usually reccomended); FALSE fixed effect. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_smd_two.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate meta-analytic d_s or d_avg across multiple two group studies. — meta_smd_two","text":"esci-estimate object; list data frames properties. Returned tables include: es_meta - data frame one overall. moderator defined, additional row level moderator. effect_label - Study label effect_size - Effect size LL - Lower bound conf_level% confidence interval UL - Upper bound conf_level% confidence interval SE - Expected standard error k - Number studies diamond_ratio - ratio random fixed effects meta-analtics effect sizes diamond_ratio_LL - lower bound conf_level% confidence interval diamond ratio diamond_ratio_UL - upper bound conf_level% confidence interval diamond ratio I2 - I2 measure heterogeneity I2_LL - Lower bound conf_level% confidence interval I2 I2_UL - upper bound conf_level% confidence interval I2 PI_LL - lower bound conf_level% prediction interval PI_UL - upper bound conf_level% prediction interval p - p value meta-analytic effect size, based null exactly 0 width - width effect-size confidence interval FE_effect_size - effect size fixed-effects model (regardless fixed effects selected RE_effect_size - effect size random-effects model (regardless random effects selected FE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio RE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio es_heterogeneity - data frame heterogeneity values conf_level% CIs meta-analytic effect size. moderator defined also reports heterogeneity estimates level moderator. effect_label - study label moderator_variable_name - moderator passed, gives name moderator moderator_variable_name - 'Overall' level moderator, passed measure - Name measure heterogeneity LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval raw_data - data one row study passed label - study label effect_size - effect size weight - study weight meta analysis sample_variance - expected level sampling variation SE - expected standard error LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval mean - used calculate study p value; d value entered study n - study sample size sd - use calculate study p value; set 1 study p - p value study, based null exactly 0","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_smd_two.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate meta-analytic d_s or d_avg across multiple two group studies. — meta_smd_two","text":"study's effect size expressed d_s d_avg. function estimate_mdiff_two() can provide effect sizes raw data. meta-analytic effect size, confidence interval heterogeneity estimates come metafor::rma(). diamond ratio confidence interval come CI_diamond_ratio().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/overview.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates descriptive statistics for a continuous variable — overview","title":"Calculates descriptive statistics for a continuous variable — overview","text":"function calculates basic descriptive statistics numerical variable. can calculate overall summary, broken levels grouping variable. Inputs can summary data, vectors, data frame.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/overview.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates descriptive statistics for a continuous variable — overview","text":"","code":"overview( data = NULL, outcome_variable = NULL, grouping_variable = NULL, means = NULL, sds = NULL, ns = NULL, grouping_variable_levels = NULL, outcome_variable_name = \"My Outcome Variable\", grouping_variable_name = NULL, conf_level = 0.95, assume_equal_variance = FALSE )"},{"path":"https://rcalinjageman.github.io/esci/reference/overview.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates descriptive statistics for a continuous variable — overview","text":"data raw data, data frame tibble outcome_variable raw data, either vector containing numerical data name data-frame column containing factor grouping_variable optional; raw data either vector containing factor name data frame column containing factor means summary data - vector 1 numerical means sds summary data - vector standard deviations, length means ns summary data - vector sample sizes, length means grouping_variable_levels summary data - optional vector group labels, length means. passed, auto-generated. outcome_variable_name Optional friendly name outcome variable. Defaults 'Outcome Variable'. Ignored data-frame passed, argument ignored. grouping_variable_name Optional friendly name grouping variable. data frame passed, argument ignored. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. assume_equal_variance Defaults FALSE","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/overview.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates descriptive statistics for a continuous variable — overview","text":"Returns table descriptive statistics overview outcome_variable_name - grouping_variable_name - grouping_variable_level - mean - mean_LL - mean_UL - median - median_LL - median_UL - sd - min - max - q1 - q3 - n - missing - df - mean_SE - median_SE -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/overview.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculates descriptive statistics for a continuous variable — overview","text":"equal variance assumed, group treated independently. case, estimated mean, CI, SE statpsych::ci.mean1(), estimated median, CI, SE statpsych::ci.median1(). equal variance assumed, group CI calculated respect group data, using statpsych::ci.lc.mean.bs() statpsych::ci.lc.median.bs()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/overview.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculates descriptive statistics for a continuous variable — overview","text":"","code":"# example code esci::overview(data_latimier_3groups, \"Test%\", \"Group\") #> outcome_variable_name grouping_variable_name grouping_variable_level mean #> 1 Test% Group Reread 37.29323 #> 2 Test% Group Quiz 42.75689 #> 3 Test% Group Prequiz 37.14286 #> mean_LL mean_UL median median_LL median_UL sd min max #> 1 34.17500 40.41146 33.33333 28.79785 37.86881 15.30716 9.52381 76.19048 #> 2 38.96548 46.54830 42.85714 36.05392 49.66036 18.61177 14.28571 95.23810 #> 3 33.88967 40.39604 33.33333 31.06559 35.60107 15.96965 9.52381 90.47619 #> q1 q3 n missing df mean_SE median_SE #> 1 28.57143 47.61905 95 0 94 1.570482 2.314063 #> 2 28.57143 57.14286 95 0 94 1.909527 3.471095 #> 3 23.80952 42.85714 95 0 94 1.638451 1.157033"},{"path":"https://rcalinjageman.github.io/esci/reference/overview_nominal.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates descriptive statistics for a numerical variable — overview_nominal","title":"Calculates descriptive statistics for a numerical variable — overview_nominal","text":"function calculated basic descriptive statistics categorical/ nominal variable. Inputs can summary data, vectors, data frame.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/overview_nominal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates descriptive statistics for a numerical variable — overview_nominal","text":"","code":"overview_nominal( data = NULL, outcome_variable = NULL, grouping_variable = NULL, cases = NULL, outcome_variable_levels = NULL, outcome_variable_name = \"My Outcome Variable\", grouping_variable_name = \"My Grouping Variable\", conf_level = 0.95, count_NA = FALSE )"},{"path":"https://rcalinjageman.github.io/esci/reference/overview_nominal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates descriptive statistics for a numerical variable — overview_nominal","text":"data raw data, data frame tibble outcome_variable raw data, either vector containing factor data name data-frame column containing factor grouping_variable raw data, either NULL (default), vector factor data-frame column containing factor cases summary data - vector 1 counts, integers>0 outcome_variable_levels summary data - optional vector group labels, length cases. passed, auto-generated. outcome_variable_name Optional friendly name outcome variable. Defaults 'Outcome Variable'. Ignored data-frame passed, argument ignored. grouping_variable_name Optional friendly name grouping variable. Defaults 'Grouping Variable'. Ignored summary data data frames -- used vectors data passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. count_NA Logical count NAs (TRUE) total N (FALSE)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/overview_nominal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates descriptive statistics for a numerical variable — overview_nominal","text":"Returns table descriptive statistics overview_nominal outcome_variable_name - outcome_variable_level - cases - n - P - P_LL - P_UL - P_SE - P_adjusted - ta_LL - ta_UL -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/overview_nominal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculates descriptive statistics for a numerical variable — overview_nominal","text":"","code":"# example code esci::overview_nominal(esci::data_latimier_3groups, \"Group\") #> outcome_variable_name outcome_variable_level cases n P P_LL #> 1 Group Reread 95 285 0.3333333 0.2811977 #> 2 Group Quiz 95 285 0.3333333 0.2811977 #> 3 Group Prequiz 95 285 0.3333333 0.2811977 #> P_UL P_SE P_adjusted ta_LL ta_UL #> 1 0.3900826 0.02777728 0.3356401 0.2899506 0.3813297 #> 2 0.3900826 0.02777728 0.3356401 0.2899506 0.3813297 #> 3 0.3900826 0.02777728 0.3356401 0.2899506 0.3813297"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_correlation.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot an estimated Pearson's r value — plot_correlation","title":"Plot an estimated Pearson's r value — plot_correlation","text":"plot_correlation creates ggplot2 plot suitable visualizing estimate correlation two continuous variables (Pearson's r). function can passed esci_estimate object generated estimate_r()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_correlation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot an estimated Pearson's r value — plot_correlation","text":"","code":"plot_correlation( estimate, error_layout = c(\"halfeye\", \"eye\", \"gradient\", \"none\"), error_scale = 0.3, error_normalize = c(\"groups\", \"all\", \"panels\"), rope = c(NA, NA), ggtheme = NULL )"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_correlation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot an estimated Pearson's r value — plot_correlation","text":"estimate esci_estimate object generated estimate_r() error_layout Optional; One 'halfeye', 'eye', 'gradient' 'none' expected sampling error measure central tendency displayed. Caution - displayed error distributions seem correct yet error_scale Optional real number > 0 speciyin width expected sampling error visualization; default 0.3 error_normalize Optional; One 'groups' (default), '', 'panels' specifying width expected sampling error distributions calculated. rope Optional two-item vector specifying region practical equivalence (ROPE) highlighted plot. point null hypothesis, pass value (e.g. c(0, 0) test point null exaclty 0); interval null pass ascending values (e.g. c(-1, 1)) ggtheme Optional ggplot2 theme object control overall styling; defaults ggplot2::theme_classic()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_correlation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot an estimated Pearson's r value — plot_correlation","text":"Returns ggplot object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_correlation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot an estimated Pearson's r value — plot_correlation","text":"function developed primarily student use within jamovi learning along text book Introduction New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024). Expect breaking changes function improved general use. Work still done includes: Revise avoid deprecated ggplot features Revise consistent ability control aesthetics consistent layer names","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_correlation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot an estimated Pearson's r value — plot_correlation","text":"","code":"# From raw data thomason1 <- data.frame( ls_pre = c( 13, 12, 12, 9, 14, 17, 14, 9, 6, 7, 11, 15 ), ls_post = c( 14, 13, 16, 12, 15, 18, 13, 10, 10, 8, 14, 16 ) ) estimate <- esci::estimate_r( thomason1, ls_pre, ls_post ) estimate #> Analysis of raw data: #> Data frame = data #> #> ---Overview--- #> outcome_variable_name mean mean_LL mean_UL median median_LL median_UL #> 1 ls_pre 11.58333 9.476776 13.68989 12.0 9 14 #> 2 ls_post 13.25000 11.410019 15.08998 13.5 10 16 #> sd min max q1 q3 n missing df mean_SE median_SE #> 1 3.315483 6 17 9.0 14.00 12 0 11 0.9570974 1.208488 #> 2 2.895922 8 18 11.5 15.25 12 0 11 0.8359806 1.450186 #> #> -- es_r -- #> x_variable_name y_variable_name effect effect_size LL #> 1 ls_pre ls_post ls_pre and ls_post 0.8923908 0.62894 #> UL SE n df ta_LL ta_UL #> 1 0.967157 0.06139936 12 10 0.6882891 0.9596343 #> #> -- regression -- #> component values LL UL #> 1 Intercept (a) 4.2212267 0.8856544 7.556799 #> 2 Slope (b) 0.7794624 0.5017391 1.057186 #> [1] \"Ŷ = 4.221 + 0.7795*X\" #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. # To evaluate a hypothesis (interval null from -0.1 to 0.1): test_correlation(estimate, rope = c(-0.1, 0.1)) #> $properties #> $properties$effect_size_name #> [1] \"r\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] TRUE #> #> $properties$rope #> [1] -0.1 0.1 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name effect null_words #> 1 Nil Hypothesis Test ls_pre and ls_post ls_pre and ls_post 0.00 #> confidence LL UL CI #> 1 95 0.62894 0.967157 95% CI [0.62894, 0.967157] #> CI_compare t df p p_result #> 1 The 95% CI does not contain H_0 4.300635 10 1.703091e-05 p < 0.05 #> null_decision conclusion significant #> 1 Reject H_0 At α = 0.05, 0.00 is not a plausible value of Ͱ TRUE #> #> $interval_null #> test_type outcome_variable_name effect #> 1 Practical significance test ls_pre and ls_post ls_pre and ls_post #> rope confidence #> 1 (-0.10, 0.10) 95 #> CI #> 1 95% CI [0.62894, 0.967157]\\n90% CI [0.6882891, 0.9596343] #> rope_compare p_result conclusion #> 1 95% CI fully outside H_0 p < 0.05 At α = 0.05, conclude Ͱ is substantive #> significant #> 1 TRUE #> if (FALSE) { # To visualize the value of r plot_correlation(estimate) # To visualize the data (scatterplot) plot_scatter(estimate) # To visualize the data (scatterplot) and use regression to obtain Y' from X plot_scatter(estimate, predict_from_x = 10) } # From summary data estimate <- esci::estimate_r(r = 0.536, n = 50) estimate #> Analysis of summary data: #> #> -- es_r -- #> x_variable_name y_variable_name effect effect_size #> 1 My x variable My y variable My x variable and My y variable 0.536 #> LL UL SE n df ta_LL ta_UL #> 1 0.2978573 0.7058914 0.1018149 50 48 0.3391487 0.6820747 #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimate plot_correlation(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_describe.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a histogram or dotplot of an estimated magnitude with raw data — plot_describe","title":"Plot a histogram or dotplot of an estimated magnitude with raw data — plot_describe","text":"plot_describe Takes estimate produced estimate_magnitude produces dotplot histogram. can mark various descriptive statitics plot, including mean, median, sd, quartiles, z lines. percentile passed, color-codes data based percentile.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_describe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a histogram or dotplot of an estimated magnitude with raw data — plot_describe","text":"","code":"plot_describe( estimate, type = c(\"histogram\", \"dotplot\"), mark_mean = FALSE, mark_median = FALSE, mark_sd = FALSE, mark_quartiles = FALSE, mark_z_lines = FALSE, mark_percentile = NULL, histogram_bins = 12, ylim = c(0, NA), ybreaks = NULL, xlim = c(NA, NA), xbreaks = NULL, fill_regular = \"#008DF9\", fill_highlighted = \"#E20134\", color = \"black\", marker_size = 5, ggtheme = NULL )"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_describe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a histogram or dotplot of an estimated magnitude with raw data — plot_describe","text":"estimate esci_estimate object raw data es_mean type histogram dotplot mark_mean mean marked? mark_median median marked? mark_sd mean marked? mark_quartiles mean marked? mark_z_lines z lines marked? mark_percentile percentile (0 1) marked histogram_bins number bins histogram ylim 2-length numeric vector ybreaks numeric >= 1 xlim 2-length numeric vector xbreaks numeric >= 1 fill_regular color fill_highlighted color color outline color marker_size Size markers ggtheme theme apply, ","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_describe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a histogram or dotplot of an estimated magnitude with raw data — plot_describe","text":"Returns ggplot object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_describe.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot a histogram or dotplot of an estimated magnitude with raw data — plot_describe","text":"function developed primarily student use within jamovi learning along text book Introduction New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024). Expect breaking changes function improved general use. Work still done includes: Revise avoid deprecated ggplot features Revise consistent ability control aesthetics consistent layer names","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_describe.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot a histogram or dotplot of an estimated magnitude with raw data — plot_describe","text":"","code":"# example code # Generate an estimate on a single continuous variable estimate <- esci::estimate_magnitude(esci::data_latimier_3groups, `Test%`) # Now describe the result, with a histogram plot_describe(estimate) # Same, but as a dotplot and mark the mean plot_describe(estimate, type = \"dotplot\", mark_mean = TRUE) #> Warning: Ignoring unknown aesthetics: z #> Warning: `stat(z <= mark_percentile)` was deprecated in ggplot2 3.4.0. #> ℹ Please use `after_stat(z <= mark_percentile)` instead. #> ℹ The deprecated feature was likely used in the esci package. #> Please report the issue to the authors."},{"path":"https://rcalinjageman.github.io/esci/reference/plot_interaction.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the interaction from a 2x2 design — plot_interaction","title":"Plot the interaction from a 2x2 design — plot_interaction","text":"plot_interaction helps visualize interaction 2x2 design. plots 2 simple effects first factor can also help visualize CIs simple effects. comparison simple effects represents interaction (difference difference). can pass esci-estimate objects generated estimate_mdiff_2x2_between() estimate_mdiff_2x2_mixed(). function returns ggplot2 object.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_interaction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the interaction from a 2x2 design — plot_interaction","text":"","code":"plot_interaction( estimate, effect_size = c(\"mean\", \"median\"), show_CI = FALSE, ggtheme = NULL, line_count = 100, line_alpha = 0.02 )"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_interaction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the interaction from a 2x2 design — plot_interaction","text":"estimate esci_estimate object raw data es_mdiff_2x2_ function effect_size Optional; one 'mean' 'median' determine measure central tendency plotted. Note median available estimate generated raw data. Defauls 'mean' show_CI Optional logical; set TRUE visualize confidence intervals simple effect; defaults FALSE ggtheme Optional ggplot2 theme object specify visual style plot. Defaults ggplot2::theme_classic() line_count Optional integer > 0 specify number lines used visualize simple-effect confidence intervals; defaults 100 line_alpha Optional numeric 0 1 specify alpha (transparancy) confidence interval lines; defaults 0.02","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_interaction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the interaction from a 2x2 design — plot_interaction","text":"Returns ggplot object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_interaction.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot the interaction from a 2x2 design — plot_interaction","text":"function developed primarily student use within jamovi learning along text book Introduction New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024). Expect breaking changes function improved general use. Work still done includes: Revise avoid deprecated ggplot features Revise consistent ability control aesthetics consistent layer names","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_interaction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot the interaction from a 2x2 design — plot_interaction","text":"","code":"# From summary data means <- c(1.5, 1.14, 1.38, 2.22) sds <- c(1.38, .96,1.5, 1.68) ns <- c(26, 26, 25, 26) grouping_variable_A_levels <- c(\"Evening\", \"Morning\") grouping_variable_B_levels <- c(\"Sleep\", \"No Sleep\") estimates <- estimate_mdiff_2x2_between( means = means, sds = sds, ns = ns, grouping_variable_A_levels = grouping_variable_A_levels, grouping_variable_B_levels = grouping_variable_B_levels, grouping_variable_A_name = \"Testing Time\", grouping_variable_B_name = \"Rest\", outcome_variable_name = \"False Memory Score\", assume_equal_variance = TRUE ) if (FALSE) { # To visualize the estimated mean difference for the interaction plot_mdiff(estimate$interaction, effect_size = \"mean\") # To visualize the interaction as a line plot plot_interaction(estimates) # Same but with fan effect representing each simple-effect CI plot_interaction(estimates, show_CI = TRUE) }"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_magnitude.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the mean or median for a continuous variable — plot_magnitude","title":"Plot the mean or median for a continuous variable — plot_magnitude","text":"plot_magnitude creates ggplot2 plot suitable visualizing results study one group one continuous outcome variables. can highlight either mean median outcome variable. function can passed esci_estimate object generated estimate_magnitude()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_magnitude.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the mean or median for a continuous variable — plot_magnitude","text":"","code":"plot_magnitude( estimate, effect_size = c(\"mean\", \"median\"), data_layout = c(\"random\", \"swarm\", \"none\"), data_spread = 0.25, error_layout = c(\"halfeye\", \"eye\", \"gradient\", \"none\"), error_scale = 0.3, error_nudge = 0.35, error_normalize = c(\"groups\", \"all\", \"panels\"), rope = c(NA, NA), ggtheme = NULL )"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_magnitude.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the mean or median for a continuous variable — plot_magnitude","text":"estimate esci_estimate object generated estimate_magnitude() effect_size Optional; One 'mean' (default) 'median'; specifies measure central tendency highlight; note medians available esci_estimate object generated raw data data_layout Optional; One 'random' (default), 'swarm', 'none' raw data (available) displayed data_spread Optional real number > 0 specifying width raw data (available) take graph; default 0.25; default spacing two groups graph 1 error_layout Optional; One 'halfeye', 'eye', 'gradient' 'none' expected sampling error measure central tendency displayed. Currently, applies 'mean' selected measure central tendency error_scale Optional real number > 0 speciyin width expected sampling error visualization; default 0.3 error_nudge Optional amount error distribution offset; default 0.35 error_normalize Optional; One 'groups' (default), '', 'panels' specifying width expected sampling error distributions calculated. rope Optional two-item vector specifying region practical equivalence (ROPE) highlighted plot. point null hypothesis, pass value (e.g. c(0, 0) test point null exaclty 0); interval null pass ascending values (e.g. c(-1, 1)) ggtheme Optional ggplot2 theme object control overall styling; defaults ggplot2::theme_classic()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_magnitude.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the mean or median for a continuous variable — plot_magnitude","text":"Returns ggplot object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_magnitude.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot the mean or median for a continuous variable — plot_magnitude","text":"function developed primarily student use within jamovi learning along text book Introduction New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024). Expect breaking changes function improved general use. Work still done includes: Revise avoid deprecated ggplot features Revise consistent ability control aesthetics consistent layer names","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_magnitude.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot the mean or median for a continuous variable — plot_magnitude","text":"","code":"# From raw data data(\"data_penlaptop1\") estimate <- esci::estimate_magnitude( data = data_penlaptop1[data_penlaptop1$condition == \"Pen\", ], outcome_variable = transcription ) estimate #> Analysis of raw data: #> Data frame = data #> Outcome variable(s) = transcription #> #> ---Overview--- #> outcome_variable_name mean mean_LL mean_UL median median_LL median_UL #> 1 transcription 8.811765 7.154642 10.46889 8.6 6.5 11.1 #> sd min max q1 q3 n missing df mean_SE median_SE #> 1 4.749339 1 20.1 5.2 11.275 34 0 33 0.8145049 1.021201 #> #> -- es_mean -- #> outcome_variable_name effect effect_size LL UL SE #> 1 transcription transcription 8.811765 7.154642 10.46889 0.8145049 #> df ta_LL ta_UL #> 1 33 7.746606 9.876923 #> #> -- es_median -- #> outcome_variable_name effect effect_size LL UL SE df ta_LL #> 1 transcription transcription 8.6 6.5 11.1 1.021201 33 7.1 #> ta_UL #> 1 10.8 #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimate plot_magnitude(estimate) } # From summary data mymean <- 24.5 mysd <- 3.65 myn <- 40 estimate <- esci::estimate_magnitude( mean = mymean, sd = mysd, n = myn ) estimate #> Analysis of raw data: #> Outcome variable(s) = My outcome variable #> #> ---Overview--- #> outcome_variable_name mean mean_LL mean_UL sd n df mean_SE #> 1 My outcome variable 24.5 23.33267 25.66733 3.65 40 39 0.5771157 #> #> -- es_mean -- #> outcome_variable_name effect effect_size LL UL #> 1 My outcome variable My outcome variable 24.5 23.33267 25.66733 #> SE df ta_LL ta_UL #> 1 0.5771157 39 23.74765 25.25235 #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimate plot_mdiff(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_mdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots for comparing continuous outcome variables between conditions — plot_mdiff","title":"Plots for comparing continuous outcome variables between conditions — plot_mdiff","text":"plot_mdiff helps visualize comparisons continuous outcome variable conditions. can plot raw data (available) condition, mean median (raw data ) condition, emphasizes 1-df comparison among conditions, plotting estimated difference confidence interval difference axis. can pass esci-estimate objects generated estimate_mdiff_one(), estimate_mdiff_two(), estimate_mdiff_paired(), estimate_mdiff_ind_contrast(), estimate_mdiff_2x2_between(), estimate_mdiff_2x2_mixed(). function returns ggplot2 object.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_mdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots for comparing continuous outcome variables between conditions — plot_mdiff","text":"","code":"plot_mdiff( estimate, effect_size = c(\"mean\", \"median\"), data_layout = c(\"random\", \"swarm\", \"none\"), data_spread = 0.15, error_layout = c(\"halfeye\", \"eye\", \"gradient\", \"none\"), error_scale = 0.3, error_nudge = 0.4, error_normalize = c(\"groups\", \"all\", \"panels\"), difference_axis_units = c(\"raw\", \"sd\"), difference_axis_breaks = 5, difference_axis_space = 1, simple_contrast_labels = TRUE, ylim = c(NA, NA), ybreaks = 5, rope = c(NA, NA), rope_units = c(\"raw\", \"sd\"), ggtheme = NULL )"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_mdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots for comparing continuous outcome variables between conditions — plot_mdiff","text":"estimate esci-estimate object generated estimate_mdiff_ function effect_size Optional; one 'mean' 'median' determine measure central tendency plotted. Note median available estimate generated raw data. Defauls 'mean' data_layout Optional; one 'random', 'swarm', 'none' determine raw data (available) displayed. Defaults 'random' data_spread Optional numeric determining width raw data use condition. Defaults 0.15 (relative 1 unit per condition) error_layout Optional; one 'halfeye', 'eye', 'gradient' 'none' determine expected error distribution displayed estimated parameter. Defaults 'halfeye'. Currently apply 'median' selected effect size, case simple error bar used error_scale Optional numeric determining width expected error distribution. Defaults 0.3 error_nudge Optional numeric determining degree measures central tendency shifted right raw data; defaults 0.4 error_normalize Optional; one 'groups', '', 'panels' determine width expected error distributions normalized. Defaults 'groups'. See documentation ggdist difference_axis_units Optional; one 'raw' 'sd' determine markings difference axis raw-score units standard-deviation units. 'sd' standard deviation mean difference used, true even 'median' selected effect size difference_axis_breaks Optional numeric > 1 suggested number breaks difference axis. Defaults 5 difference_axis_space Optional numeric > 0 indicate spacing difference axis. Defaults 1 simple_contrast_labels Optional logical determine contrasts given simple labels ('Reference', 'Comparison', 'Difference') descriptive labels based contrast specified. ylim Optional 2-item vector specifying y-axis limits. Defaults c(NA NA); Use NA specify auto-limit. ybreaks Optional numeric > 2 suggested number y-axis breaks; defaults 5 rope Optional 2-item vector item 2 >= item 1. Use specify range values use visualize hypothesis test. values , point-null hypothesis test visualized. item2 > item1 interval-null hypothesis test visualized. Defaults c(NA, NA), visualize hypothesis test rope_units Optional; one 'raw' 'sd' indicate units rope passed. Defaults 'raw' ggtheme Optional ggplot2 theme object specify visual style plot. Defaults ggplot2::theme_classic()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_mdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots for comparing continuous outcome variables between conditions — plot_mdiff","text":"Returns ggplot object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_mdiff.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plots for comparing continuous outcome variables between conditions — plot_mdiff","text":"function developed primarily student use within jamovi learning along text book Introduction New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024). Expect breaking changes function improved general use. Work still done includes: Revise avoid deprecated ggplot features Revise consistent ability control aesthetics consistent layer names","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_mdiff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots for comparing continuous outcome variables between conditions — plot_mdiff","text":"","code":"# From summary data estimate <- estimate_mdiff_two( comparison_mean = 12.09, comparison_sd = 5.52, comparison_n = 103, reference_mean = 6.88, reference_sd = 4.22, reference_n = 48, grouping_variable_levels = c(\"Ref-Laptop\", \"Comp-Pen\"), outcome_variable_name = \"% Transcription\", grouping_variable_name = \"Note-taking type\", assume_equal_variance = TRUE ) if (FALSE) { # To visualize the estimated median difference (raw data only) plot_mdiff(estimate, effect_size = \"median\") } # From raw data data(\"data_penlaptop1\") estimate <- esci::estimate_mdiff_two( data = data_penlaptop1, outcome_variable = transcription, grouping_variable = condition, switch_comparison_order = TRUE, assume_equal_variance = TRUE ) if (FALSE) { # To visualize the estimated mean difference plot_mdiff(estimate, effect_size = \"mean\") }"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_meta.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a forest plot displaying results of a meta-analysis — plot_meta","title":"Generates a forest plot displaying results of a meta-analysis — plot_meta","text":"`plot_meta' returns ggplot2 object visualizing results meta-analysis, showing study effect size CI, overall effect size CI diamond, effect sizes estimated moderator level (defined), (optionally) prediction intervals subsequent studies. function requires input esci_estimate object generated esci meta-analysis function: meta_any(), meta_d1(), meta_d2(), meta_mdiff_two(), meta_mean(), meta_pdiff_two(), meta_proportion(), meta_r().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_meta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a forest plot displaying results of a meta-analysis — plot_meta","text":"","code":"plot_meta( estimate, mark_zero = TRUE, include_PIs = FALSE, report_CIs = FALSE, explain_DR = FALSE, meta_diamond_height = 0.35, ggtheme = ggplot2::theme_classic() )"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_meta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a forest plot displaying results of a meta-analysis — plot_meta","text":"estimate esci_estimate object generated esci meta_ function mark_zero Boolean; defaults TRUE include dotted line indicated effect (effect_size = 0) include_PIs Boolean; defaults FALSE; set TRUE include prediction intervals overall effect moderator level (defined) report_CIs Boolean; defaults FALSE; set TRUE include printed representation study effect size CI along right- hand figure explain_DR Boolean; dedaults FALSE; set TRUE moderator defined show RE FE effect sizes represent diamond ration measure effect-size heterogeneity calculated meta_diamond_height Optional real number > 0 indicate height meta-analytic diamond drawn; defaults 0.35 ggtheme Optional ggplot2 theme object control overall styling; defaults ggplot2::theme_classic()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_meta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a forest plot displaying results of a meta-analysis — plot_meta","text":"Returns ggplot object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_meta.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generates a forest plot displaying results of a meta-analysis — plot_meta","text":"function developed primarily student use within jamovi learning along text book Introduction New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024). Expect breaking changes function improved general use. Work still done includes: Revise avoid deprecated ggplot features Revise consistent ability control aesthetics consistent layer names","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_meta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a forest plot displaying results of a meta-analysis — plot_meta","text":"","code":"# Data set -- see Introduction to the New Statistics, 1st edition brain_scan_persuasion <- data.frame( study_name = c(\"McCabe 1\", \"McCabe 2\", paste(\"Michael\", seq(1:10), sep = \" \")), nbM = c(2.89, 2.69, 2.90, 2.62, 2.96, 2.93, 2.86, 2.50, 2.41, 2.54, 2.73, 2.66), nbS = c(0.79, 0.55, 0.58, 0.54, 0.36, 0.60, 0.59, 0.84, 0.78, 0.66, 0.67, 0.65), nbN = c(28, 26, 98, 42, 24, 184, 274, 58, 34, 99, 98, 94), bM = c(3.12, 3.00, 2.86, 2.85, 3.07, 2.89, 2.91, 2.60, 2.74, 2.72, 2.68, 2.64), bS = c(0.65, 0.54, 0.61, 0.57, 0.55, 0.60, 0.52, 0.83, 0.51, 0.68, 0.69, 0.71), bN = c(26, 28, 99, 33, 21, 184, 255, 55, 34, 95, 93, 97), mod = as.factor( c(\"Simple\", \"Critique\", \"Simple\",\"Simple\",\"Simple\", \"Simple\",\"Simple\",\"Critique\",\"Critique\",\"Critique\", \"Critique\",\"Critique\") ), ds = c(0.31217944, 0.56073138, -0.06693802, 0.41136192, 0.23581389, -0.06652995, 0.08958082, 0.11892778, 0.49506069, 0.26765910, -0.07326, -0.02925) ) # Meta-analysis: random effects, no moderator estimate <- esci::meta_mdiff_two( data = brain_scan_persuasion, comparison_means = bM, comparison_sds = bS, comparison_ns = bN, reference_means = nbM, reference_sds = nbS, reference_ns = nbN, labels = study_name, effect_label = \"Brain Photo Rating - No Brain Photo Rating\", assume_equal_variance = TRUE, random_effects = TRUE ) if (FALSE) { # Forest plot esci::plot_meta(estimate) } # Meta-analysis: random effects, moderator if (FALSE) { # Forest plot esci::plot_meta(estimate) } # Meta-analysis: random effects, moderator, output d_s if (FALSE) { # Forest plot esci::plot_meta(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_pdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots for comparing categorical outcome variables between conditions — plot_pdiff","title":"Plots for comparing categorical outcome variables between conditions — plot_pdiff","text":"plot_pdiff helps visualize comparisons categorical outcome variable conditions. plots proportions cases level grouping variable emphasizes 1-df comparison among conditions, plotting estimated difference confidence interval difference axis. can pass esci-estimate objects generated estimate_pdiff_one(), estimate_pdiff_two(), estimate_pdiff_paired(), estimate_pdiff_ind_contrast() function returns ggplot2 object.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_pdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots for comparing categorical outcome variables between conditions — plot_pdiff","text":"","code":"plot_pdiff( estimate, error_layout = c(\"halfeye\", \"eye\", \"gradient\", \"none\"), error_scale = 0.3, error_normalize = c(\"groups\", \"all\", \"panels\"), difference_axis_breaks = 5, difference_axis_space = 1, simple_contrast_labels = TRUE, ylim = c(NA, NA), ybreaks = 5, rope = c(NA, NA), ggtheme = NULL )"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_pdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots for comparing categorical outcome variables between conditions — plot_pdiff","text":"estimate esci-estimate object generated estimate_pdiff_ function error_layout Optional; one 'halfeye', 'eye', 'gradient' 'none' determine expected error distribution displayed estimated parameter. Defaults 'halfeye'. Currently apply 'median' selected effect size, case simple error bar used error_scale Optional numeric determining width expected error distribution. Defaults 0.3 error_normalize Optional; one 'groups', '', 'panels' determine width expected error distributions normalized. Defaults 'groups'. See documentation ggdist difference_axis_breaks Optional numeric > 1 suggested number breaks difference axis. Defaults 5 difference_axis_space Optional numeric > 0 indicate spacing difference axis. Defaults 1 simple_contrast_labels Optional logical determine contrasts given simple labels ('Reference', 'Comparison', 'Difference') descriptive labels based contrast specified. ylim Optional 2-item vector specifying y-axis limits. Defaults c(NA NA); Use NA specify auto-limit. ybreaks Optional numeric > 2 suggested number y-axis breaks; defaults 5 rope Optional 2-item vector item 2 >= item 1. Use specify range values use visualize hypothesis test. values , point-null hypothesis test visualized. item2 > item1 interval-null hypothesis test visualized. Defaults c(NA, NA), visualize hypothesis test ggtheme Optional ggplot2 theme object specify visual style plot. Defaults ggplot2::theme_classic()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_pdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots for comparing categorical outcome variables between conditions — plot_pdiff","text":"Returns ggplot object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_pdiff.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plots for comparing categorical outcome variables between conditions — plot_pdiff","text":"function developed primarily student use within jamovi learning along text book Introduction New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024). Expect breaking changes function improved general use. Work still done includes: Revise avoid deprecated ggplot features Revise consistent ability control aesthetics consistent layer names","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_pdiff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots for comparing categorical outcome variables between conditions — plot_pdiff","text":"","code":"estimate <- estimate_pdiff_two( comparison_cases = 10, comparison_n = 20, reference_cases = 78, reference_n = 252, grouping_variable_levels = c(\"Original\", \"Replication\"), conf_level = 0.95 ) if (FALSE) { # To visualize the estimate plot_pdiff(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot an estimated proportion — plot_proportion","title":"Plot an estimated proportion — plot_proportion","text":"plot_proportion creates ggplot2 plot suitable visualizing estimated proportion categorical variable. function can passed esci_estimate object generated estimate_proportion()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot an estimated proportion — plot_proportion","text":"","code":"plot_proportion( estimate, error_layout = c(\"halfeye\", \"eye\", \"gradient\", \"none\"), error_scale = 0.3, error_normalize = c(\"groups\", \"all\", \"panels\"), rope = c(NA, NA), plot_possible = FALSE, ggtheme = NULL )"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_proportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot an estimated proportion — plot_proportion","text":"estimate esci_estimate object generated estimate_proportion() error_layout Optional; One 'halfeye', 'eye', 'gradient' 'none' expected sampling error measure central tendency displayed. Caution - displayed error distributions seem correct yet error_scale Optional real number > 0 speciyin width expected sampling error visualization; default 0.3 error_normalize Optional; One 'groups' (default), '', 'panels' specifying width expected sampling error distributions calculated. rope Optional two-item vector specifying region practical equivalence (ROPE) highlighted plot. point null hypothesis, pass value (e.g. c(0, 0) test point null exaclty 0); interval null pass ascending values (e.g. c(-1, 1)) plot_possible Boolean; defaults FALSE; TRUE plot lines discrete proportion possible given sample size (e.g proportion 10 total cases, draw lines 0, .1, .2, etc.) ggtheme Optional ggplot2 theme object control overall styling; defaults ggplot2::theme_classic()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot an estimated proportion — plot_proportion","text":"Returns ggplot object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_proportion.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot an estimated proportion — plot_proportion","text":"function developed primarily student use within jamovi learning along text book Introduction New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024). Expect breaking changes function improved general use. Work still done includes: Revise avoid deprecated ggplot features Revise consistent ability control aesthetics consistent layer names","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_proportion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot an estimated proportion — plot_proportion","text":"","code":"estimate <- esci::estimate_proportion( cases = c(8, 22-8), outcome_variable_levels = c(\"Affected\", \"Not Affected\") ) if (FALSE) { # To visualize the estimate plot_proportion(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_rdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots for comparing Pearson r values between conditions — plot_rdiff","title":"Plots for comparing Pearson r values between conditions — plot_rdiff","text":"plot_rdiff helps visualize comparisons Pearson's r estimates conditions. plots Pearson's r value level grouping variable emphasizes 1-df comparison among conditions, plotting estimated difference confidence interval difference axis. can pass esci-estimate objects generated estimate_rdiff_two(). function returns ggplot2 object.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_rdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots for comparing Pearson r values between conditions — plot_rdiff","text":"","code":"plot_rdiff( estimate, error_layout = c(\"halfeye\", \"eye\", \"gradient\", \"none\"), error_scale = 0.3, error_normalize = c(\"groups\", \"all\", \"panels\"), difference_axis_breaks = 5, simple_contrast_labels = TRUE, ylim = c(NA, NA), ybreaks = 5, rope = c(NA, NA), ggtheme = NULL )"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_rdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots for comparing Pearson r values between conditions — plot_rdiff","text":"estimate esci-estimate object generated estimate_pdiff_ function error_layout Optional; one 'halfeye', 'eye', 'gradient' 'none' determine expected error distribution displayed estimated parameter. Defaults 'halfeye'. Currently apply 'median' selected effect size, case simple error bar used error_scale Optional numeric determining width expected error distribution. Defaults 0.3 error_normalize Optional; one 'groups', '', 'panels' determine width expected error distributions normalized. Defaults 'groups'. See documentation ggdist difference_axis_breaks Optional numeric > 1 suggested number breaks difference axis. Defaults 5 simple_contrast_labels Optional logical determine contrasts given simple labels ('Reference', 'Comparison', 'Difference') descriptive labels based contrast specified. ylim Optional 2-item vector specifying y-axis limits. Defaults c(NA NA); Use NA specify auto-limit. ybreaks Optional numeric > 2 suggested number y-axis breaks; defaults 5 rope Optional 2-item vector item 2 >= item 1. Use specify range values use visualize hypothesis test. values , point-null hypothesis test visualized. item2 > item1 interval-null hypothesis test visualized. Defaults c(NA, NA), visualize hypothesis test ggtheme Optional ggplot2 theme object specify visual style plot. Defaults ggplot2::theme_classic()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_rdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots for comparing Pearson r values between conditions — plot_rdiff","text":"Returns ggplot object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_rdiff.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plots for comparing Pearson r values between conditions — plot_rdiff","text":"function developed primarily student use within jamovi learning along text book Introduction New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024). Expect breaking changes function improved general use. Work still done includes: Revise avoid deprecated ggplot features Revise consistent ability control aesthetics consistent layer names","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_rdiff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots for comparing Pearson r values between conditions — plot_rdiff","text":"","code":"# From summary data estimate <- esci::estimate_rdiff_two( comparison_r = .53, comparison_n = 45, reference_r = .41, reference_n = 59, grouping_variable_levels = c(\"Females\", \"Males\"), x_variable_name = \"Satisfaction with life\", y_variable_name = \"Body satisfaction\", grouping_variable_name = \"Gender\", conf_level = .95 ) estimate #> Analysis of summary data: #> #> -- es_r -- #> grouping_variable_name grouping_variable_level x_variable_name #> 1 Gender Females Satisfaction with life #> 2 Gender Males Satisfaction with life #> y_variable_name effect effect_size LL UL SE n df #> 1 Body satisfaction Females 0.41 0.1685419 0.6005378 0.1092338 59 57 #> 2 Body satisfaction Males 0.53 0.2744717 0.7096862 0.1084084 45 43 #> ta_LL ta_UL #> 1 0.2091420 0.5729339 #> 2 0.3188047 0.6847105 #> #> -- es_r_difference -- #> type grouping_variable_name grouping_variable_level #> 1 Comparison Gender Males #> 2 Reference Gender Females #> 3 Difference Gender Males - Females #> x_variable_name y_variable_name effect effect_size #> 1 Satisfaction with life Body satisfaction Males 0.53 #> 2 Satisfaction with life Body satisfaction Females 0.41 #> 3 Satisfaction with life Body satisfaction Males - Females 0.12 #> LL UL SE n df ta_LL ta_UL rz sem #> 1 0.2744717 0.7096862 0.1084084 45 43 0.3188047 0.6847105 0.5901452 0.1543033 #> 2 0.1685419 0.6005378 0.1092338 59 57 0.2091420 0.5729339 0.4356112 0.1336306 #> 3 -0.1987466 0.4209803 NA NA NA -0.1467413 0.3735336 0.1545339 0.2041241 #> z p #> 1 3.8245778 0.0001309964 #> 2 3.2598159 0.0011148455 #> 3 0.7570586 0.4490147644 #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. # To evaluate a hypothesis (interval null from -0.1 to 0.1): esci::test_rdiff(estimate) #> $properties #> $properties$effect_size_name #> [1] \"r\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] FALSE #> #> $properties$rope #> [1] 0 0 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name #> 1 Nil Hypothesis Test Satisfaction with life and Body satisfaction #> effect null_words confidence LL UL #> 1 Males - Females 0.00 95 -0.1987466 0.4209803 #> CI CI_compare t df p #> 1 95% CI [-0.1987466, 0.4209803] The 95% CI contains H_0 0.7570586 NA 0.4490148 #> p_result null_decision #> 1 p ≥ 0.05 Fail to reject H_0 #> conclusion significant #> 1 At α = 0.05, 0.00 remains a plausible value of Ͱ_diff FALSE #> if (FALSE) { # To visualize the values of r and their difference esci::plot_rdiff(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_scatter.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a scatter plot of data for two continuous variables — plot_scatter","title":"Generates a scatter plot of data for two continuous variables — plot_scatter","text":"plot_scatter returns ggplot2 object data two continuous variables. Can indicate regression line confidence interval,prediction intervals regression residuals . function requires input esci_estimate object generated estimate_r()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_scatter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a scatter plot of data for two continuous variables — plot_scatter","text":"","code":"plot_scatter( estimate, show_line = FALSE, show_line_CI = FALSE, show_PI = FALSE, show_residuals = FALSE, show_mean_lines = FALSE, show_r = FALSE, predict_from_x = NULL, plot_as_z = FALSE, ggtheme = ggplot2::theme_classic() )"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_scatter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a scatter plot of data for two continuous variables — plot_scatter","text":"estimate esci_estimate object generated estimate_r() show_line Boolean; defaults FALSE; set TRUE show regression line show_line_CI Boolean; defaults FALSE; set TRUE show confidence interval regression line show_PI Boolean; defaults FALSE; set TRUE show prediction intervals show_residuals Boolean; defaults FALSE; set TRUE show residuals prediction show_mean_lines Boolean; defaults FALSE; set TRUE plot lines showing mean variable show_r Boolean; defaults FALSE; set TRUE print r value CI plot predict_from_x Optional real number range x variable plot; Defaults NULL; passed, graph shows predicted Y' x value plot_as_z Boolean; defaults FALSE; set TRUE convert x y scores z scores prior plotting ggtheme Optional ggplot2 theme object control overall styling; defaults ggplot2::theme_classic()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_scatter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a scatter plot of data for two continuous variables — plot_scatter","text":"Returns ggplot object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_scatter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generates a scatter plot of data for two continuous variables — plot_scatter","text":"function developed primarily student use within jamovi learning along text book Introduction New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024). Expect breaking changes function improved general use. Work still done includes: Revise avoid deprecated ggplot features Revise consistent ability control aesthetics consistent layer names","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_scatter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a scatter plot of data for two continuous variables — plot_scatter","text":"","code":"# From raw data thomason1 <- data.frame( ls_pre = c( 13, 12, 12, 9, 14, 17, 14, 9, 6, 7, 11, 15 ), ls_post = c( 14, 13, 16, 12, 15, 18, 13, 10, 10, 8, 14, 16 ) ) estimate <- esci::estimate_r( thomason1, ls_pre, ls_post ) estimate #> Analysis of raw data: #> Data frame = data #> #> ---Overview--- #> outcome_variable_name mean mean_LL mean_UL median median_LL median_UL #> 1 ls_pre 11.58333 9.476776 13.68989 12.0 9 14 #> 2 ls_post 13.25000 11.410019 15.08998 13.5 10 16 #> sd min max q1 q3 n missing df mean_SE median_SE #> 1 3.315483 6 17 9.0 14.00 12 0 11 0.9570974 1.208488 #> 2 2.895922 8 18 11.5 15.25 12 0 11 0.8359806 1.450186 #> #> -- es_r -- #> x_variable_name y_variable_name effect effect_size LL #> 1 ls_pre ls_post ls_pre and ls_post 0.8923908 0.62894 #> UL SE n df ta_LL ta_UL #> 1 0.967157 0.06139936 12 10 0.6882891 0.9596343 #> #> -- regression -- #> component values LL UL #> 1 Intercept (a) 4.2212267 0.8856544 7.556799 #> 2 Slope (b) 0.7794624 0.5017391 1.057186 #> [1] \"Ŷ = 4.221 + 0.7795*X\" #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. # To evaluate a hypothesis (interval null from -0.1 to 0.1): test_correlation(estimate, rope = c(-0.1, 0.1)) #> $properties #> $properties$effect_size_name #> [1] \"r\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] TRUE #> #> $properties$rope #> [1] -0.1 0.1 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name effect null_words #> 1 Nil Hypothesis Test ls_pre and ls_post ls_pre and ls_post 0.00 #> confidence LL UL CI #> 1 95 0.62894 0.967157 95% CI [0.62894, 0.967157] #> CI_compare t df p p_result #> 1 The 95% CI does not contain H_0 4.300635 10 1.703091e-05 p < 0.05 #> null_decision conclusion significant #> 1 Reject H_0 At α = 0.05, 0.00 is not a plausible value of Ͱ TRUE #> #> $interval_null #> test_type outcome_variable_name effect #> 1 Practical significance test ls_pre and ls_post ls_pre and ls_post #> rope confidence #> 1 (-0.10, 0.10) 95 #> CI #> 1 95% CI [0.62894, 0.967157]\\n90% CI [0.6882891, 0.9596343] #> rope_compare p_result conclusion #> 1 95% CI fully outside H_0 p < 0.05 At α = 0.05, conclude Ͱ is substantive #> significant #> 1 TRUE #> if (FALSE) { # To visualize the value of r plot_correlation(estimate) # To visualize the data (scatterplot) plot_scatter(estimate) # To visualize the data (scatterplot) and use regression to obtain Y' from X plot_scatter(estimate, predict_from_x = 10) } # From summary data estimate <- esci::estimate_r(r = 0.536, n = 50) estimate #> Analysis of summary data: #> #> -- es_r -- #> x_variable_name y_variable_name effect effect_size #> 1 My x variable My y variable My x variable and My y variable 0.536 #> LL UL SE n df ta_LL ta_UL #> 1 0.2978573 0.7058914 0.1018149 50 48 0.3391487 0.6820747 #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimate plot_correlation(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/print.esci_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Print an esci_estimate — print.esci_estimate","title":"Print an esci_estimate — print.esci_estimate","text":"Pretties printing complex esci_estimate object.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/print.esci_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print an esci_estimate — print.esci_estimate","text":"","code":"# S3 method for esci_estimate print(x, ..., verbose = FALSE)"},{"path":"https://rcalinjageman.github.io/esci/reference/print.esci_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print an esci_estimate — print.esci_estimate","text":"x object print; must class esci_estimate ... S3 signature generic plot function. verbose optional logical print details; defaults false","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_correlation.html","id":null,"dir":"Reference","previous_headings":"","what":"Test a hypothesis about the strength of a Pearson's r correlation — test_correlation","title":"Test a hypothesis about the strength of a Pearson's r correlation — test_correlation","text":"test_correlation suitable testing hypothesis strength correlation two continuous variables (designs Pearson's r suitable measure correlation).","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_correlation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test a hypothesis about the strength of a Pearson's r correlation — test_correlation","text":"","code":"test_correlation(estimate, rope = c(0, 0), output_html = FALSE)"},{"path":"https://rcalinjageman.github.io/esci/reference/test_correlation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test a hypothesis about the strength of a Pearson's r correlation — test_correlation","text":"estimate esci_estimate object generated estimate_r function rope two-element vector defining Region Practical Equivalence (ROPE). Specify c(0, 0) test point null exactly 0. Specify two ascending values -1 1 test interval null (e.g. c(.25, .45) test hypothesis Pearson's r population (rho) .25 .45). output_html TRUE return results HTML; FALSE (default) return standard output","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_correlation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test a hypothesis about the strength of a Pearson's r correlation — test_correlation","text":"Returns list 1-2 data frames point_null - always returned test_type - 'Nil hypothesis test', meaning test H0 = 0 outcome_variable_name - Name outcome variable effect - Label effect tested null_words - Express null words confidence - Confidence level, integer (95 95%, etc.) LL - Lower boundary confidence% CI effect UL - Upper boundary confidence% CI effect CI - Character representation CI effect CI_compare - Text description relation CI null t - applicable, t value hypothesis test df - applicable, degrees freedom hypothesis test p - applicable, p value hypothesis test p_result - Text representation p value obtained null_decision - Text represention decision null conclusion - Text representation conclusion draw significant - TRUE/FALSE significant alpha = 1-CI interval_null - returned interval null specified test_type - 'Practical significance test', meaning test interval null outcome_variable_name - effect - Name outcome variable rope - Test representaiton null interval confidence - Confidence level, integer (95 95%, etc.) CI - Character representation CI effect rope_compare - Text description relation CI null interval p_result - Text representation p value obtained conclusion - Text representation conclusion draw significant - TRUE/FALSE significant alpha = 1-CI","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_correlation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Test a hypothesis about the strength of a Pearson's r correlation — test_correlation","text":"function can passed esci_estimate object generated estimate_r(). can test hypotheses specific value difference (point null) range values (interval null)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_correlation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test a hypothesis about the strength of a Pearson's r correlation — test_correlation","text":"","code":"# example code estimate <- esci::estimate_r(r = 0.536, n = 50) # Test against a point null of exactly 0 test_correlation(estimate) #> $properties #> $properties$effect_size_name #> [1] \"r\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] FALSE #> #> $properties$rope #> [1] 0 0 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name #> 1 Nil Hypothesis Test My x variable and My y variable #> effect null_words confidence LL UL #> 1 My x variable and My y variable 0.00 95 0.2978573 0.7058914 #> CI CI_compare t df #> 1 95% CI [0.2978573, 0.7058914] The 95% CI does not contain H_0 4.103289 48 #> p p_result null_decision #> 1 4.073183e-05 p < 0.05 Reject H_0 #> conclusion significant #> 1 At α = 0.05, 0.00 is not a plausible value of Ͱ TRUE #> # Test against an interval null (-0.1, 0.1) test_correlation(estimate, rope = c(-0.1, 0.1)) #> $properties #> $properties$effect_size_name #> [1] \"r\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] TRUE #> #> $properties$rope #> [1] -0.1 0.1 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name #> 1 Nil Hypothesis Test My x variable and My y variable #> effect null_words confidence LL UL #> 1 My x variable and My y variable 0.00 95 0.2978573 0.7058914 #> CI CI_compare t df #> 1 95% CI [0.2978573, 0.7058914] The 95% CI does not contain H_0 4.103289 48 #> p p_result null_decision #> 1 4.073183e-05 p < 0.05 Reject H_0 #> conclusion significant #> 1 At α = 0.05, 0.00 is not a plausible value of Ͱ TRUE #> #> $interval_null #> test_type outcome_variable_name #> 1 Practical significance test My x variable and My y variable #> effect rope confidence #> 1 My x variable and My y variable (-0.10, 0.10) 95 #> CI #> 1 95% CI [0.2978573, 0.7058914]\\n90% CI [0.3391487, 0.6820747] #> rope_compare p_result conclusion #> 1 95% CI fully outside H_0 p < 0.05 At α = 0.05, conclude Ͱ is substantive #> significant #> 1 TRUE #>"},{"path":"https://rcalinjageman.github.io/esci/reference/test_mdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Test a hypothesis about a difference in a continuous outcome variable. — test_mdiff","title":"Test a hypothesis about a difference in a continuous outcome variable. — test_mdiff","text":"test_mdiff suitable conducting testing hypothesis magnitude difference two conditions continuous outcome variable. can test hypotheses differences means medians independent paired designs.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_mdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test a hypothesis about a difference in a continuous outcome variable. — test_mdiff","text":"","code":"test_mdiff( estimate, effect_size = c(\"mean\", \"median\"), rope = c(0, 0), rope_units = c(\"raw\", \"sd\"), output_html = FALSE )"},{"path":"https://rcalinjageman.github.io/esci/reference/test_mdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test a hypothesis about a difference in a continuous outcome variable. — test_mdiff","text":"estimate esci_estimate object generated estimate_mdiff_ function effect_size One 'mean' 'median'. effect size selected must available esci_estimate object; medians available estimate generated raw data. rope two-element vector defining Region Practical Equivalence (ROPE). Specify c(0, 0) test point null exactly 0. Specify two ascending values test interval null (e.g. c(-1, 1) test hypothesis tha difference -1 1). rope_units One 'raw' (default) 'sd', specifies units ROPE. 'sd' specified, rope defined standard deviation units (e.g. c(-1, 1) taken -1 1 standard deviations 0). sd used, ROPE converted raw scores test conducted raw scores. output_html TRUE return results HTML; FALSE (default) return standard output","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_mdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test a hypothesis about a difference in a continuous outcome variable. — test_mdiff","text":"Returns list 1-2 data frames point_null - always returned test_type - 'Nil hypothesis test', meaning test H0 = 0 outcome_variable_name - Name outcome variable effect - Label effect tested null_words - Express null words confidence - Confidence level, integer (95 95%, etc.) LL - Lower boundary confidence% CI effect UL - Upper boundary confidence% CI effect CI - Character representation CI effect CI_compare - Text description relation CI null t - applicable, t value hypothesis test df - applicable, degrees freedom hypothesis test p - applicable, p value hypothesis test p_result - Text representation p value obtained null_decision - Text represention decision null conclusion - Text representation conclusion draw significant - TRUE/FALSE significant alpha = 1-CI interval_null - returned interval null specified test_type - 'Practical significance test', meaning test interval null outcome_variable_name - effect - Name outcome variable rope - Test representaiton null interval confidence - Confidence level, integer (95 95%, etc.) CI - Character representation CI effect rope_compare - Text description relation CI null interval p_result - Text representation p value obtained conclusion - Text representation conclusion draw significant - TRUE/FALSE significant alpha = 1-CI","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_mdiff.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Test a hypothesis about a difference in a continuous outcome variable. — test_mdiff","text":"function can passed esci_estimate object generated estimate_mdiff_one(), estimate_mdiff_two(), estimate_mdiff_paired(), estimate_mdiff_ind_contrast(). can test hypotheses specific value difference (point null) range values (interval null)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_mdiff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test a hypothesis about a difference in a continuous outcome variable. — test_mdiff","text":"","code":"# example code data(\"data_penlaptop1\") estimate <- esci::estimate_mdiff_two( data = data_penlaptop1, outcome_variable = transcription, grouping_variable = condition, switch_comparison_order = TRUE, assume_equal_variance = TRUE ) # Test mean difference against point null of 0 esci::test_mdiff( estimate, effect_size = \"mean\" ) #> $properties #> $properties$effect_size_name #> [1] \"mean\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] FALSE #> #> $properties$rope #> [1] 0 0 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name effect null_words confidence #> 1 Nil Hypothesis Test transcription Pen ‒ Laptop 0.00 95 #> LL UL CI #> 1 -8.729915 -2.685265 95% CI [-8.729915, -2.685265] #> CI_compare t df p p_result #> 1 The 95% CI does not contain H_0 -3.77382 63 0.0003579282 p < 0.05 #> null_decision conclusion #> 1 Reject H_0 At α = 0.05, 0.00 is not a plausible value of μ_diff #> significant #> 1 TRUE #> # Test median difference against point null of 0 # Note that t, df, p return NA because test is completed # by interval. esci::test_mdiff( estimate, effect_size = \"median\" ) #> $properties #> $properties$effect_size_name #> [1] \"median\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] FALSE #> #> $properties$rope #> [1] 0 0 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name effect null_words confidence #> 1 Nil Hypothesis Test transcription Pen ‒ Laptop 0.00 95 #> LL UL CI #> 1 -8.085644 -0.3143563 95% CI [-8.085644, -0.3143563] #> CI_compare t df p p_result null_decision #> 1 The 95% CI does not contain H_0 NA NA NA p < 0.05 Reject H_0 #> conclusion significant #> 1 At α = 0.05, 0.00 is not a plausible value of η_diff TRUE #> # Test mean difference against interval null of -10 to 10 esci::test_mdiff( estimate, effect_size = \"mean\", rope = c(-10, 10) ) #> $properties #> $properties$effect_size_name #> [1] \"mean\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] TRUE #> #> $properties$rope #> [1] -10 10 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name effect null_words confidence #> 1 Nil Hypothesis Test transcription Pen ‒ Laptop 0.00 95 #> LL UL CI #> 1 -8.729915 -2.685265 95% CI [-8.729915, -2.685265] #> CI_compare t df p p_result #> 1 The 95% CI does not contain H_0 -3.77382 63 0.0003579282 p < 0.05 #> null_decision conclusion #> 1 Reject H_0 At α = 0.05, 0.00 is not a plausible value of μ_diff #> significant #> 1 TRUE #> #> $interval_null #> test_type outcome_variable_name effect #> 1 Practical significance test transcription Pen ‒ Laptop #> rope confidence #> 1 (-10.00, 10.00) 95 #> CI #> 1 95% CI [-8.729915, -2.685265]\\n90% CI [-8.232423, -3.182757] #> rope_compare p_result conclusion #> 1 90% CI fully inside H_0 p < 0.05 At α = 0.05, conclude μ_diff is negligible #> significant #> 1 TRUE #> # Test mean difference against interval null of d (-0.20, 0.20) d = 0.2 is often # thought of as a small effect, so this test examines if the effect is # negligible (clearly between negligble and small), substantive (clearly more # than small), or unclear. The d boundaries provided are converted to raw scores # and then the CI of the observed effect is compared to the raw-score boundaries esci::test_mdiff( estimate, effect_size = \"mean\", rope = c(-0.2, 0.2), rope_units = \"sd\" ) #> $properties #> $properties$effect_size_name #> [1] \"mean\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] TRUE #> #> $properties$rope #> [1] -0.2 0.2 #> #> $properties$rope_units #> [1] \"sd\" #> #> #> $point_null #> test_type outcome_variable_name effect null_words confidence #> 1 Nil Hypothesis Test transcription Pen ‒ Laptop 0.00 95 #> LL UL CI #> 1 -8.729915 -2.685265 95% CI [-8.729915, -2.685265] #> CI_compare t df p p_result #> 1 The 95% CI does not contain H_0 -3.77382 63 0.0003579282 p < 0.05 #> null_decision conclusion #> 1 Reject H_0 At α = 0.05, 0.00 is not a plausible value of μ_diff #> significant #> 1 TRUE #> #> $interval_null #> test_type outcome_variable_name effect #> 1 Practical significance test transcription Pen ‒ Laptop #> rope confidence #> 1 (-1.21805, 1.21805) 95 #> CI #> 1 95% CI [-8.729915, -2.685265]\\n90% CI [-8.232423, -3.182757] #> rope_compare p_result conclusion #> 1 95% CI fully outside H_0 p < 0.05 At α = 0.05, conclude μ_diff is substantive #> significant #> 1 TRUE #>"},{"path":"https://rcalinjageman.github.io/esci/reference/test_pdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Test a hypothesis about a difference in proportion — test_pdiff","title":"Test a hypothesis about a difference in proportion — test_pdiff","text":"test_pdiff suitable testing hypothesis difference proportions two conditions categorical outcome variable. can test hypotheses independent paired designs.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_pdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test a hypothesis about a difference in proportion — test_pdiff","text":"","code":"test_pdiff(estimate, rope = c(0, 0), output_html = FALSE)"},{"path":"https://rcalinjageman.github.io/esci/reference/test_pdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test a hypothesis about a difference in proportion — test_pdiff","text":"estimate esci_estimate object generated estimate_pdiff_ function rope two-element vector defining Region Practical Equivalence (ROPE). Specify c(0, 0) test point null exactly 0. Specify two ascending values -1 1 test interval null (e.g. c(-.25, .25) test hypothesis difference proportion -.25 .25). output_html TRUE return results HTML; FALSE (default) return standard output","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_pdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test a hypothesis about a difference in proportion — test_pdiff","text":"Returns list 1-2 data frames point_null - always returned test_type - 'Nil hypothesis test', meaning test H0 = 0 outcome_variable_name - Name outcome variable effect - Label effect tested null_words - Express null words confidence - Confidence level, integer (95 95%, etc.) LL - Lower boundary confidence% CI effect UL - Upper boundary confidence% CI effect CI - Character representation CI effect CI_compare - Text description relation CI null t - applicable, t value hypothesis test df - applicable, degrees freedom hypothesis test p - applicable, p value hypothesis test p_result - Text representation p value obtained null_decision - Text represention decision null conclusion - Text representation conclusion draw significant - TRUE/FALSE significant alpha = 1-CI interval_null - returned interval null specified test_type - 'Practical significance test', meaning test interval null outcome_variable_name - effect - Name outcome variable rope - Test representaiton null interval confidence - Confidence level, integer (95 95%, etc.) CI - Character representation CI effect rope_compare - Text description relation CI null interval p_result - Text representation p value obtained conclusion - Text representation conclusion draw significant - TRUE/FALSE significant alpha = 1-CI","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_pdiff.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Test a hypothesis about a difference in proportion — test_pdiff","text":"function can passed esci_estimate object generated estimate_pdiff_one(), estimate_pdiff_two(), estimate_pdiff_paired(), estimate_pdiff_ind_contrast(). can test hypotheses specific value difference (point null) range values (interval null)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_pdiff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test a hypothesis about a difference in proportion — test_pdiff","text":"","code":"estimate <- estimate_pdiff_two( comparison_cases = 10, comparison_n = 20, reference_cases = 78, reference_n = 252, grouping_variable_levels = c(\"Original\", \"Replication\"), conf_level = 0.95 ) # Test against null of exactly test_pdiff(estimate) #> $properties #> $properties$effect_size_name #> [1] \"P\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] FALSE #> #> $properties$rope #> [1] 0 0 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name case_label effect #> 1 Nil Hypothesis Test My outcome variable P_Affected Replication ‒ Original #> null_words confidence LL UL CI #> 1 0.00 95 -0.02757339 0.4055261 95% CI [-0.02757339, 0.4055261] #> CI_compare t df p p_result null_decision #> 1 The 95% CI contains H_0 1.710401 NA 0.08719178 p ≥ 0.05 Fail to reject H_0 #> conclusion significant #> 1 At α = 0.05, 0.00 remains a plausible value of Π_diff FALSE #> # Test against null of (-0.1, 0.1) test_pdiff(estimate, rope = c(-0.1, 0.1)) #> $properties #> $properties$effect_size_name #> [1] \"P\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] TRUE #> #> $properties$rope #> [1] -0.1 0.1 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name case_label effect #> 1 Nil Hypothesis Test My outcome variable P_Affected Replication ‒ Original #> null_words confidence LL UL CI #> 1 0.00 95 -0.02757339 0.4055261 95% CI [-0.02757339, 0.4055261] #> CI_compare t df p p_result null_decision #> 1 The 95% CI contains H_0 1.710401 NA 0.08719178 p ≥ 0.05 Fail to reject H_0 #> conclusion significant #> 1 At α = 0.05, 0.00 remains a plausible value of Π_diff FALSE #> #> $interval_null #> test_type outcome_variable_name case_label #> 1 Practical significance test My outcome variable P_Affected #> effect rope confidence #> 1 Replication ‒ Original (-0.10, 0.10) 95 #> CI #> 1 95% CI [-0.02757339, 0.4055261]\\n90% CI [0.007242087, 0.3707107] #> rope_compare p_result #> 1 95% CI has values inside and outside H_0 p ≥ 0.05 #> conclusion significant #> 1 At α = 0.05, not clear if Π_diff is substantive or negligible FALSE #>"},{"path":"https://rcalinjageman.github.io/esci/reference/test_rdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Test a hypothesis about a difference in correlation strength — test_rdiff","title":"Test a hypothesis about a difference in correlation strength — test_rdiff","text":"test_rdiff suitable testing hypothesis difference correlation (r) two conditions. moment, can test hypotheses independent-group designs.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_rdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test a hypothesis about a difference in correlation strength — test_rdiff","text":"","code":"test_rdiff(estimate, rope = c(0, 0), output_html = FALSE)"},{"path":"https://rcalinjageman.github.io/esci/reference/test_rdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test a hypothesis about a difference in correlation strength — test_rdiff","text":"estimate esci_estimate object generated estimate_rdiff_ function rope two-element vector defining Region Practical Equivalence (ROPE). Specify c(0, 0) test point null exactly 0. Specify two ascending values -1 1 test interval null (e.g. c(-.25, .25) test hypothesis difference correlation -.25 .25). output_html TRUE return results HTML; FALSE (default) return standard output","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_rdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test a hypothesis about a difference in correlation strength — test_rdiff","text":"Returns list 1-2 data frames point_null - always returned test_type - 'Nil hypothesis test', meaning test H0 = 0 outcome_variable_name - Name outcome variable effect - Label effect tested null_words - Express null words confidence - Confidence level, integer (95 95%, etc.) LL - Lower boundary confidence% CI effect UL - Upper boundary confidence% CI effect CI - Character representation CI effect CI_compare - Text description relation CI null t - applicable, t value hypothesis test df - applicable, degrees freedom hypothesis test p - applicable, p value hypothesis test p_result - Text representation p value obtained null_decision - Text represention decision null conclusion - Text representation conclusion draw significant - TRUE/FALSE significant alpha = 1-CI interval_null - returned interval null specified test_type - 'Practical significance test', meaning test interval null outcome_variable_name - effect - Name outcome variable rope - Test representaiton null interval confidence - Confidence level, integer (95 95%, etc.) CI - Character representation CI effect rope_compare - Text description relation CI null interval p_result - Text representation p value obtained conclusion - Text representation conclusion draw significant - TRUE/FALSE significant alpha = 1-CI","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_rdiff.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Test a hypothesis about a difference in correlation strength — test_rdiff","text":"function can passed esci_estimate object generated estimate_rdiff_two(). can test hypotheses specific value difference (point null) range values (interval null)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_rdiff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test a hypothesis about a difference in correlation strength — test_rdiff","text":"","code":"# example code estimate <- esci::estimate_rdiff_two( comparison_r = .53, comparison_n = 45, reference_r = .41, reference_n = 59, grouping_variable_levels = c(\"Females\", \"Males\"), x_variable_name = \"Satisfaction with life\", y_variable_name = \"Body satisfaction\", grouping_variable_name = \"Gender\", conf_level = .95 ) test_rdiff(estimate) #> $properties #> $properties$effect_size_name #> [1] \"r\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] FALSE #> #> $properties$rope #> [1] 0 0 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name #> 1 Nil Hypothesis Test Satisfaction with life and Body satisfaction #> effect null_words confidence LL UL #> 1 Males - Females 0.00 95 -0.1987466 0.4209803 #> CI CI_compare t df p #> 1 95% CI [-0.1987466, 0.4209803] The 95% CI contains H_0 0.7570586 NA 0.4490148 #> p_result null_decision #> 1 p ≥ 0.05 Fail to reject H_0 #> conclusion significant #> 1 At α = 0.05, 0.00 remains a plausible value of Ͱ_diff FALSE #>"},{"path":"https://rcalinjageman.github.io/esci/news/index.html","id":"esci-version-102-release-data-march-2024","dir":"Changelog","previous_headings":"","what":"esci version 1.0.2 (Release data: March 2024)","title":"esci version 1.0.2 (Release data: March 2024)","text":"Changes: First release CRAN. Module now complete, documented, relatively complete test coverage. still pretty rough attempt, though. Expect breaking changes still come, especially graphing functions.","code":""}] +[{"path":"https://rcalinjageman.github.io/esci/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Robert J Calin-Jageman. Maintainer.","code":""},{"path":"https://rcalinjageman.github.io/esci/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Calin-Jageman RJ (2024). esci: Estimation Statistics Confidence Intervals. R package version 1.02.","code":"@Manual{, title = {esci: Estimation Statistics with Confidence Intervals}, author = {Robert J Calin-Jageman}, year = {2024}, note = {R package version 1.02}, }"},{"path":"https://rcalinjageman.github.io/esci/index.html","id":"esci-","dir":"","previous_headings":"","what":"Estimation Statistics with Confidence Intervals","title":"Estimation Statistics with Confidence Intervals","text":"esci provides student-friendly tools estimation statistics: effect sizes confidence intervals many research designs meta-analysis visualizations emphasizing effect sizes uncertainty strong hypothesis testing interval nulls esci R package module jamovi. ’re looking R package, stay . want esci jamovi, download install jamovi use module library add esci. Leave comments, bug reports, suggestions, questions esci esci still development; expect breaking changes future especially visualization functions. need production-ready estimation, turn statpsych esci built top statpsych metafor. , almost statistical calculations passed packages. exception confidence intervals Cohen’s d (see documentation). esci exist, ? provide design-based approach; function esci one type research design (e.g. two groups continuous variable); provides effect sizes relevant design one convenient function (e.g. mean difference, cohen’s d, median difference, ratio means, ratio medians). make visualization easier; esci provides visualizations emphasize effect sizes uncertainty integrate GUIs students; esci integrates jamovi integration JASP planned. visualiations produced esci exquisite large part lovely ggdist package Matthew Kay.","code":""},{"path":"https://rcalinjageman.github.io/esci/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Estimation Statistics with Confidence Intervals","text":"Assuming submission CRAN goes well, able install esci : , get stable branch directly github , try development branch:","code":"install.packages(\"esci\") # install.packages(\"devtools\") devtools::install_github('rcalinjageman/esci') # install.packages(\"devtools\") devtools::install_github('rcalinjageman/esci', branch = \"development\")"},{"path":"https://rcalinjageman.github.io/esci/index.html","id":"roadmap","dir":"","previous_headings":"","what":"Roadmap","title":"Estimation Statistics with Confidence Intervals","text":"Finish writing documentation tests Review functions consistency parameter names returned object names Rewrite visualization functions completely remove clunky approaches difference axis issues Complete JASP integration Rewrite jamovi integration Add prediction intervals basic designs Repeated measures 1 IV multiple groups Fully within-subjects 2x2 design Arbitrarily complex designs","code":""},{"path":"https://rcalinjageman.github.io/esci/index.html","id":"example","dir":"","previous_headings":"","what":"Example","title":"Estimation Statistics with Confidence Intervals","text":"","code":"library(esci) data(\"data_penlaptop1\") estimate <- estimate_mdiff_two(data_penlaptop1, transcription, condition) plot_mdiff(estimate)"},{"path":"https://rcalinjageman.github.io/esci/reference/CI_diamond_ratio.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the diamond ratio for a meta-analtyic effect, a measure of\r\nheterogeneity — CI_diamond_ratio","title":"Estimate the diamond ratio for a meta-analtyic effect, a measure of\r\nheterogeneity — CI_diamond_ratio","text":"CI_diamond_ratio returns diamond ratio CI meta-analytic effect, ratio random-effects CI width fixed-effects CI width. diamond ratio measure effect-size heterogeneity.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_diamond_ratio.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the diamond ratio for a meta-analtyic effect, a measure of\r\nheterogeneity — CI_diamond_ratio","text":"","code":"CI_diamond_ratio(RE, FE, vi, conf_level = 0.95)"},{"path":"https://rcalinjageman.github.io/esci/reference/CI_diamond_ratio.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Estimate the diamond ratio for a meta-analtyic effect, a measure of\r\nheterogeneity — CI_diamond_ratio","text":"Cairns, Maxwell, Geoff Cumming, Robert Calin‐Jageman, Luke . Prendergast. “Diamond Ratio: Visual Indicator Extent Heterogeneity Meta‐analysis.” British Journal Mathematical Statistical Psychology 75, . 2 (May 2022): 201–19. https://doi.org/10.1111/bmsp.12258.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_diamond_ratio.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the diamond ratio for a meta-analtyic effect, a measure of\r\nheterogeneity — CI_diamond_ratio","text":"RE metafor object random effects result FE metafor object fixed effects result vi vector effect size variances conf_level confidence level confidence interval. Given decimal form. Defaults 0.95.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_diamond_ratio.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the diamond ratio for a meta-analtyic effect, a measure of\r\nheterogeneity — CI_diamond_ratio","text":"Returns list 3 properties: diamond_ratio LL - lower limit conf_level% CI, Sub-Q approach UL - upper limit conf_level% CI, Sub-Q approach LL_bWT_DL - lower limit conf_level% CI, bWT-DL approach UL_bWT_DL - upper limit conf_level% CI, bWT-DL approach","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_diamond_ratio.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate the diamond ratio for a meta-analtyic effect, a measure of\r\nheterogeneity — CI_diamond_ratio","text":"Calculation CI based code provided Maxwell Cairns (see Cairns et al., 2022). Specifically, function implements Cairns et al (2022) called Sub-Q approach, generaly provides best CI coverage simulations. comparison, function also returns CI produced bWT-DL approach (generally worse performance).","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_diamond_ratio.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the diamond ratio for a meta-analtyic effect, a measure of\r\nheterogeneity — CI_diamond_ratio","text":"","code":"mydata <- esci::data_mccabemichael_brain # Use esci to obtain effect sizes and sample variances, storing only raw_data mydata <- esci::meta_mdiff_two( data = mydata, comparison_means = \"M Brain\", comparison_ns = \"n Brain\", comparison_sds = \"s Brain\", reference_means = \"M No Brain\", reference_ns = \"n No Brain\", reference_sds = \"s No Brain\", random_effects = FALSE )$raw_data # Conduct fixed effects meta-analysis FE <- metafor::rma( data = mydata, yi = effect_size, vi = sample_variance, method=\"FE\" ) # Conduct random effect meta-analysis RE <- metafor::rma( data = mydata, yi = effect_size, vi = sample_variance, method=\"DL\" ) # Get the diamond ratio esci::CI_diamond_ratio( RE = RE, FE = FE, vi = mydata$sample_variance ) #> $diamond_ratio #> [1] 1.399091 #> #> $LL #> [1] 1 #> #> $UL #> [1] 2.667069 #> #> $LL_bWT_DL #> [1] 1 #> #> $UL_bWT_DL #> [1] 3.091891 #>"},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_ind_contrast.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","title":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","text":"CI_smd_ind_contrast returns point estimate confidence interval standardized mean difference (smd aka Cohen's d aka Hedges g). standardized mean difference difference means standardized standard deviation: \\[d = \\frac{ \\psi }{s}\\]","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_ind_contrast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","text":"","code":"CI_smd_ind_contrast( means, sds, ns, contrast, conf_level = 0.95, assume_equal_variance = FALSE, correct_bias = TRUE )"},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_ind_contrast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","text":"means vector 2 means sds vector standard deviations, length means ns vector sample sizes, length means contrast vector group weights, length means conf_level confidence level confidence interval, decimal form. Defaults 0.95. assume_equal_variance Defaults FALSE correct_bias Defaults TRUE; attempts correct slight upward bias d derived sample. 8/9/2023 - Bias correction added 2 groups equal variance assumed, based recent updates statpscyh","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_ind_contrast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","text":"Returns list named elements: effect_size - point estimate sample lower - lower bound CI upper - upper bound CI numerator - numerator Cohen's d_biased; mean difference contrast denominator - denominator Cohen's d_biased; equal variance assumed sd_pooled, otherwise sd_avg df - degrees freedom used correction CI calculation se - standard error estimate; warning totally sure yet moe - margin error; 1/2 length CI d_biased - Cohen's d without correction applied properties - list properties result Properties effect_size_name - equal variance assumed d_s, otherwise d_avg effect_size_name_html - html representation d_name denominator_name - equal variance assumed sd_pooled otherwise sd_avg denominator_name_html - html representation denominator name bias_corrected - TRUE/FALSE bias correction applied message - message explaining denominator correction status message_html - html representation message","code":""},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_ind_contrast.html","id":"it-s-a-bit-complicated","dir":"Reference","previous_headings":"","what":"It's a bit complicated","title":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","text":"standardized mean difference turns complicated. First, many names: standardized mean difference (smd) Cohen's d bias sample d corrected, also called Hedge's g Second, choice standardizer requires thought: sd_pooled - used assuming groups exact variance sd_avg - require assumption equal variance possibilities, , dealt function choice standardizer important, noted subscript: d_s -- assumes equal variance, standardized sd_pooled d_avg - assume equal variance, standardized sd_avg third complication issue bias: d estimated sample slight upward bias smaller sample sizes. total sample size > 30, slight bias becomes fairly neglible (kind like small upward bias sample standard deviation). bias can corrected equal variance assumed design study simple (2 groups). complex designs (>2 groups) without assumption equal variance, now also approximate approach correcting bias Bonett. Corrections bias produce long-run reduction average bias. Corrections bias approximate.","code":""},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_ind_contrast.html","id":"when-equal-variance-is-assumed","dir":"Reference","previous_headings":"","what":"When equal variance is assumed","title":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","text":"equal variance assumed, standardized mean difference d_s, defined Kline, p. 196: \\[ d_s = \\frac{ \\psi }{ sd_{pooled} } \\] psi defined Kline, equation 7.8: \\[ \\psi =\\sum_{=1}^{}c_iM_i \\] sd_pooled defined Kline, equation 3.11 \\[sd_{pooled} = { \\frac{ \\sum_{=1}^{} (n_i -1) s_i^2 } { \\sum_{=1}^{} (n_i-1) } }\\] CI d_s derived lambda-prime transformation Lecoutre, 2007 code adapted Cousineau & Goulet-Pelletier, 2020. Kelley, 2007 explains general approach linear contrasts. approach generating CI 'exact', meaning coverage desired assumptions met (ha!). Correction upward bias can applied.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_ind_contrast.html","id":"when-equal-variance-is-not-assumed","dir":"Reference","previous_headings":"","what":"When equal variance is not assumed","title":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","text":"equal variance assumed, standardized mean difference d_avg, defined Bonett, equation 6: \\[ d_{avg} = \\frac{ \\psi }{ sd_{avg} }\\] sd_avg square root average group variances, given Bonett, explanation equation 6: \\[sd_{avg} = \\sqrt{ \\frac{ \\sum_{=1}^{} s_i^2 }{ } }\\]","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_ind_contrast.html","id":"if-only-groups","dir":"Reference","previous_headings":"","what":"If only 2 groups","title":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","text":"CI derived lambda-prime transformation using df se Huynh, 1989 -- see especially Delacre et al., 2021 also 'exact' approach, correction can applied","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_ind_contrast.html","id":"if-more-than-groups","dir":"Reference","previous_headings":"","what":"If more than 2 groups","title":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","text":"CI approximated using approach Bonett, 2008 approximate correction developed Bonett used","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_ind_contrast.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","text":"Bonett D. G. (2023). statpsych: Statistical Methods Psychologists. R package version 1.4.0. https://dgbonett.github.io/statpsych Bonett, D. G. (2018). R code posted personal website (now removed). Formally https://people.ucsc.edu/~dgbonett/psyc204.html Bonett, D. G. (2008). Confidence Intervals Standardized Linear Contrasts Means. Psychological Methods, 13(2), 99–109. https://doi.org/10.1037/1082-989X.13.2.99 Cousineau & Goulet-Pelletier (2020) https://psyarxiv.com/s2597/ Delacre et al., 2021, https://psyarxiv.com/tu6mp/ Huynh, C.-L. (1989). unified approach estimation effect size meta-analysis. NBER Working Paper Series, 58(58), 99–104. Kelley, K. (2007). Confidence intervals standardized effect sizes: Theory, application, implementation. Journal Statistical Software, 20(8), 1–24. https://doi.org/10.18637/jss.v020.i08 Lecoutre, B. (2007). Another Look Confidence Intervals Noncentral T Distribution. Journal Modern Applied Statistical Methods, 6(1), 107–116. https://doi.org/10.22237/jmasm/1177992600","code":""},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_ind_contrast.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate standardized mean difference (Cohen's d) for an independent groups\r\ncontrast — CI_smd_ind_contrast","text":"","code":"# Example from Kline, 2013 # Data in Table 3.4 # Worked out in Chapter 7 # See p. 202, non-central approach # With equal variance assumed and no correction, should give: # d_s = -0.8528028 [-2.121155, 0.4482578] CI_smd_ind_contrast( means = c(13, 11, 15), sds = c(2.738613, 2.236068, 2.000000), ns = c(5, 5, 5), contrast = contrast <- c(1, 0, -1), conf_level = 0.95, assume_equal_variance = TRUE, correct_bias = FALSE ) #> $effect_size #> [1] -0.8528028 #> #> $LL #> [1] -2.121155 #> #> $UL #> [1] 0.4482578 #> #> $numerator #> [1] -2 #> #> $denominator #> [1] 2.345208 #> #> $SE #> [1] 0.6554747 #> #> $df #> [1] 12 #> #> $d_biased #> [1] -0.8528028 #> #> $properties #> $properties$effect_size_name #> [1] \"d_s\" #> #> $properties$effect_size_name_html #> [1] \"d<\/i>s.biased<\/sub>\" #> #> $properties$denominator_name #> [1] \"s_p\" #> #> $properties$denominator_name_html #> [1] \"s<\/i>p<\/sub>\" #> #> $properties$bias_corrected #> [1] FALSE #> #> $properties$effect_size_category #> [1] \"difference\" #> #> $properties$effect_size_precision #> [1] \"magnitude\" #> #> $properties$conf_level #> [1] 0.95 #> #> $properties$assume_equal_variance #> [1] TRUE #> #> $properties$error_distribution #> [1] \"t_dist\" #> #> $properties$message #> This standardized mean difference is called d_s because the standardizer used was s_p. d_s has *not* been corrected for bias. Correction for bias can be important when df < 50. #> #> $properties$message_html #> This standardized mean difference is called d<\/i>s.biased<\/sub> #> because the standardizer used was s<\/i>p<\/sub>.
#> d<\/i>s.biased<\/sub> has *not* #> been corrected for bias. #> Correction for bias can be important when df<\/i> < 50.
#> #> # Example from [statpsych::ci.lc.stdmean.bs()] should give: # Estimate SE LL UL # Unweighted standardizer: -1.273964 0.3692800 -2.025039 -0.5774878 # Weighted standardizer: -1.273964 0.3514511 -1.990095 -0.6124317 # Group 1 standardizer: -1.273810 0.4849842 -2.343781 -0.4426775 CI_smd_ind_contrast( means = c(33.5, 37.9, 38.0, 44.1), sds = c(3.84, 3.84, 3.65, 4.98), ns = c(10,10,10,10), contrast = c(.5, .5, -.5, -.5), conf_level = 0.95, assume_equal_variance = FALSE, correct_bias = TRUE ) #> $effect_size #> [1] -1.273964 #> #> $LL #> [1] -2.025039 #> #> $UL #> [1] -0.5774878 #> #> $numerator #> [1] -5.35 #> #> $denominator #> [1] 4.11139 #> #> $SE #> [1] 0.36928 #> #> $df #> [1] 36 #> #> $d_biased #> [1] -1.301263 #> #> $properties #> $properties$effect_size_name #> [1] \"d_avg\" #> #> $properties$effect_size_name_html #> [1] \"d<\/i>avg<\/sub>\" #> #> $properties$denominator_name #> [1] \"s_avg\" #> #> $properties$denominator_name_html #> [1] \"s<\/i>avg<\/sub>\" #> #> $properties$bias_corrected #> [1] TRUE #> #> $properties$effect_size_category #> [1] \"difference\" #> #> $properties$effect_size_precision #> [1] \"magnitude\" #> #> $properties$conf_level #> [1] 0.95 #> #> $properties$assume_equal_variance #> [1] FALSE #> #> $properties$error_distribution #> [1] \"t_dist\" #> #> $properties$message #> This standardized mean difference is called d_avg because the standardizer used was s_avg. d_avg has been corrected for bias. Correction for bias can be important when df < 50. See the rightmost column for the biased value. #> #> $properties$message_html #> This standardized mean difference is called d<\/i>avg<\/sub> #> because the standardizer used was s<\/i>avg<\/sub>.
#> d<\/i>avg<\/sub> has #> been corrected for bias. #> Correction for bias can be important when df<\/i> < 50. See the rightmost column for the biased value.
#> #>"},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_one.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate standardized mean difference (Cohen's d1) for a single group — CI_smd_one","title":"Estimate standardized mean difference (Cohen's d1) for a single group — CI_smd_one","text":"CI_smd_one STILL NEEDS WORK VERIFY APPROACH SE MoE","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_one.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate standardized mean difference (Cohen's d1) for a single group — CI_smd_one","text":"","code":"CI_smd_one(mean, sd, n, reference_mean, conf_level = 0.95, correct_bias = TRUE)"},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_one.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate standardized mean difference (Cohen's d1) for a single group — CI_smd_one","text":"mean Mean single group outcome measure sd Standard deviation, > 0 n Sample size, integer > 2 reference_mean defaults 0 conf_level confidence level confidence interval, decimal form. Defaults 0.95. correct_bias Defaults TRUE","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_one.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate standardized mean difference (Cohen's d1) for a single group — CI_smd_one","text":"Returns list named elements: effect_size - point estimate sample lower - lower bound CI upper - upper bound CI numerator - numerator Cohen's d_biased; mean difference contrast denominator - denominator Cohen's d_biased; equal variance assumed sd_pooled, otherwise sd_avg df - degrees freedom used correction CI calculation se - standard error estimate; warning totally sure yet moe - margin error; 1/2 length CI d_biased - Cohen's d without correction applied properties - list properties result Properties effect_size_name - equal variance assumed d_s, otherwise d_avg effect_size_name_html - html representation d_name denominator_name - equal variance assumed sd_pooled otherwise sd_avg denominator_name_html - html representation denominator name bias_corrected - TRUE/FALSE bias correction applied message - message explaining denominator correction status message_html - html representation message","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/CI_smd_one.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate standardized mean difference (Cohen's d1) for a single group — CI_smd_one","text":"","code":"# example code esci::CI_smd_one(24.5, 3.65, 40, 20) #> $effect_size #> [1] 1.208989 #> #> $LL #> [1] 0.7951381 #> #> $UL #> [1] 1.613746 #> #> $numerator #> [1] 4.5 #> #> $denominator #> [1] 3.65 #> #> $SE #> [1] 0.2088481 #> #> $df #> [1] 39 #> #> $d_biased #> [1] 1.232877 #> #> $properties #> $properties$effect_size_name #> [1] \"d_1\" #> #> $properties$effect_size_name_html #> [1] \"d<\/i>1<\/sub>\" #> #> $properties$denominator_name #> [1] \"s\" #> #> $properties$denominator_name_html #> [1] \"s<\/i>\" #> #> $properties$bias_corrected #> [1] TRUE #> #> $properties$effect_size_category #> [1] \"difference\" #> #> $properties$effect_size_precision #> [1] \"magnitude\" #> #> $properties$conf_level #> [1] 0.95 #> #> $properties$message #> This standardized mean difference is called d_1 because the standardizer used was s. d_1 has been corrected for bias. Correction for bias can be important when df < 50. See the rightmost column for the biased value. #> #> $properties$message_html #> This standardized mean difference is called d<\/i>1<\/sub> #> because the standardizer used was s<\/i>.
#> d<\/i>1<\/sub> has #> been corrected for bias. #> Correction for bias can be important when df<\/i> < 50. See the rightmost column for the biased value.
#> #>"},{"path":"https://rcalinjageman.github.io/esci/reference/data_altruism_happiness.html","id":null,"dir":"Reference","previous_headings":"","what":"Altruism Happiness - Ch12 - from Brethel-Haurwitz and Marsh (2014) — data_altruism_happiness","title":"Altruism Happiness - Ch12 - from Brethel-Haurwitz and Marsh (2014) — data_altruism_happiness","text":"Happiness may important just person feeling ; happiness may also promote kind, altruistic behavior. Brethel-Haurwitz Marsh (2014) examined idea collecting data U.S. states. Gallup poll 2010 used measure state's well-index, measure mean happiness state's residents scale 0 100. Next, kidney donation database 1999-2010 used figure state's rate (number donations per 1 million people) non-directed kidney donations-giving one kidney stranger, extremely generous altruistic thing !","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_altruism_happiness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Altruism Happiness - Ch12 - from Brethel-Haurwitz and Marsh (2014) — data_altruism_happiness","text":"","code":"data_altruism_happiness"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_altruism_happiness.html","id":"data-altruism-happiness","dir":"Reference","previous_headings":"","what":"data_altruism_happiness","title":"Altruism Happiness - Ch12 - from Brethel-Haurwitz and Marsh (2014) — data_altruism_happiness","text":"data frame 50 rows 6 columns: State factor - State data collected Abbreviation factor - State data collected Well_Being_2010 numeric - State data collected Well_Being_2013 numeric - State data collected Kidney_Rate, per million population numeric - State data collected WB Change 2013-2010 numeric - State data collected","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_altruism_happiness.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Altruism Happiness - Ch12 - from Brethel-Haurwitz and Marsh (2014) — data_altruism_happiness","text":"https://journals.sagepub.com/doi/full/10.1177/0956797613516148","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_anchor_estimate_ma.html","id":null,"dir":"Reference","previous_headings":"","what":"Anchor Estimate ma - Ch9 - Many Labs replications of Jacowitz and Kahneman (1995) — data_anchor_estimate_ma","title":"Anchor Estimate ma - Ch9 - Many Labs replications of Jacowitz and Kahneman (1995) — data_anchor_estimate_ma","text":"extent wording question influence one's judgment? classic study, Jacowitz Kahneman (1995) asked participants estimate many babies born day United States. Participants given either low anchor (100 babies/day) high anchor (less 50,000 babies/day). saw low anchor estimated many fewer births/day saw high anchor, suggests wording can profound influence. correct answer, happens, ~11,000 births/day 2014. investigate extent results replicable, Many Labs project repeated classic study many different labs around world. can find summary data 30 labs Anchor Estimate ma data file","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_anchor_estimate_ma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Anchor Estimate ma - Ch9 - Many Labs replications of Jacowitz and Kahneman (1995) — data_anchor_estimate_ma","text":"","code":"data_anchor_estimate_ma"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_anchor_estimate_ma.html","id":"data-anchor-estimate-ma","dir":"Reference","previous_headings":"","what":"data_anchor_estimate_ma","title":"Anchor Estimate ma - Ch9 - Many Labs replications of Jacowitz and Kahneman (1995) — data_anchor_estimate_ma","text":"data frame 30 rows 9 columns: Location factor M Low numeric s Low numeric n Low integer M High numeric s High numeric n High integer USAorNot factor Country factor","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_anchor_estimate_ma.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Anchor Estimate ma - Ch9 - Many Labs replications of Jacowitz and Kahneman (1995) — data_anchor_estimate_ma","text":"https://econtent.hogrefe.com/doi/10.1027/1864-9335/a000178","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_basol_badnews.html","id":null,"dir":"Reference","previous_headings":"","what":"Basol badnews - Ch07 - from Basol et al. (2020) — data_basol_badnews","title":"Basol badnews - Ch07 - from Basol et al. (2020) — data_basol_badnews","text":"Climate change? Vaccines? Fake news conspiracy theories numerous issues can highly damaging, thriving social media age. Trying debunk conspiracy theory presenting facts evidence often work, alas. Psychological inoculation, also similar prebunking, presents mild form misinformation, preferably explanation, hope building resistance real-life fake news-sort vaccine fake news. Bad News game spin-research psychological inoculation. Basol et al. (2020) assessed possible effectiveness game fake news vaccine. getbadnews.com can click '' information, just start playing game- easy maybe even fun. encounter mock Twitter (now X) fake news messages illustrate common strategies making fake news memorable believable. make choices messages decide ones 'forward' try spread fake news building credibility score number 'followers'-rather like real life conspiracy theorist wanting spread word. Compete friends credibility number followers. Basol's online participants first saw 18 fictitious fake news tweets rated reliability (accuracy, believability), also rated confidence reliability rating. ratings 1 7 scale. BadNews group played game 15 minutes, whereas Control group played Tetris. gave reliability confidence ratings 18 tweets.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_basol_badnews.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Basol badnews - Ch07 - from Basol et al. (2020) — data_basol_badnews","text":"","code":"data_basol_badnews"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_basol_badnews.html","id":"data-basol-badnews","dir":"Reference","previous_headings":"","what":"data_basol_badnews","title":"Basol badnews - Ch07 - from Basol et al. (2020) — data_basol_badnews","text":"data frame 198 rows 3 columns: Diff reliability numeric Diff confidence numeric Condition factor","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_basol_badnews.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Basol badnews - Ch07 - from Basol et al. (2020) — data_basol_badnews","text":"https://doi.org/10.5334%2Fjoc.91","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bem_and_honorton_1994.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample data for estimate_proportion: Ten Ganzfeld studies from Bem and\r\nHonorton (1994) — data_bem_and_honorton_1994","title":"Sample data for estimate_proportion: Ten Ganzfeld studies from Bem and\r\nHonorton (1994) — data_bem_and_honorton_1994","text":"Three pilot studies helped refine procedures, four studies used novice receivers. Study 5 used 20 students music, drama, dance receivers, response suggestions creative people might likely show telepathy. Studies 6 7 used receivers participated earlier study. proportion hits expected chance .25","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bem_and_honorton_1994.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample data for estimate_proportion: Ten Ganzfeld studies from Bem and\r\nHonorton (1994) — data_bem_and_honorton_1994","text":"","code":"data_bem_and_honorton_1994"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_bem_and_honorton_1994.html","id":"data-bem-and-honorton-a-data-frame-with-rows-and-","dir":"Reference","previous_headings":"","what":"data_bem_and_honorton_1994 A data frame with 10 rows and 3","title":"Sample data for estimate_proportion: Ten Ganzfeld studies from Bem and\r\nHonorton (1994) — data_bem_and_honorton_1994","text":"columns: study Name study hits Number correct responses trials Number trials","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bem_and_honorton_1994.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Sample data for estimate_proportion: Ten Ganzfeld studies from Bem and\r\nHonorton (1994) — data_bem_and_honorton_1994","text":"Bem, D. J., & Honorton, C. (1994). Psi exist? Replicable evidence anomalous process information transfer. Psychological Bulletin, 115, 4–-18. doi:10.1037/0033-2909.115.1.4","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bem_psychic.html","id":null,"dir":"Reference","previous_headings":"","what":"Bem Psychic - Ch13 - from Bem and Honorton (1994) — data_bem_psychic","title":"Bem Psychic - Ch13 - from Bem and Honorton (1994) — data_bem_psychic","text":"Daryl Bem experienced mentalist research psychologist, , decade earlier, one several outside experts invited scrutinize laboratory experimental procedures parapsychology researcher Charles Honorton. Bem judged adequate, joined research effort became coauthor Honorton. Bem Honorton (1994) first reviewed early ganzfeld studies described experimental procedure improved reduce chance results influenced various possible biases, leakages information sender receiver. example, randomization procedure carried automatically computer, stimuli presented computer control. Bem Honorton presented data studies conducted improved procedure. Table 13.1 presents basic data 10 studies reported Bem Honorton (1994). Participants made single judgment, Pilot 1, example, 22 participants responded, 8 giving correct response. Three pilot studies helped refine procedures, four studies used novice receivers. Study 5 used 20 students music, drama, dance receivers, response suggestions creative people might likely show telepathy. Studies 6 7 used receivers participated earlier study. proportion hits expected chance .25, Table 13.1 shows Study 1 found proportions higher .25.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bem_psychic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bem Psychic - Ch13 - from Bem and Honorton (1994) — data_bem_psychic","text":"","code":"data_bem_psychic"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_bem_psychic.html","id":"data-bem-psychic","dir":"Reference","previous_headings":"","what":"data_bem_psychic","title":"Bem Psychic - Ch13 - from Bem and Honorton (1994) — data_bem_psychic","text":"data frame 10 rows 5 columns: Study factor Participants factor N(Trials) integer N(Hits) integer Proportion Hits numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bem_psychic.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Bem Psychic - Ch13 - from Bem and Honorton (1994) — data_bem_psychic","text":"https://psycnet.apa.org/record/1994-20286-001","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bodywellf.html","id":null,"dir":"Reference","previous_headings":"","what":"BodyWellF - Ch12 - Body Satisfaction and Well-being data for females from Figure 11.24 right panel — data_bodywellf","title":"BodyWellF - Ch12 - Body Satisfaction and Well-being data for females from Figure 11.24 right panel — data_bodywellf","text":"subset data_bodywell_fm, reports participants identified female. Data Subjective Wellbeing abd Body Satisfaction.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bodywellf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BodyWellF - Ch12 - Body Satisfaction and Well-being data for females from Figure 11.24 right panel — data_bodywellf","text":"","code":"data_bodywellf"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_bodywellf.html","id":"data-bodywellf","dir":"Reference","previous_headings":"","what":"data_bodywellf","title":"BodyWellF - Ch12 - Body Satisfaction and Well-being data for females from Figure 11.24 right panel — data_bodywellf","text":"data frame 59 rows 2 columns: Body Satisfaction numeric Well-numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bodywellfm.html","id":null,"dir":"Reference","previous_headings":"","what":"BodyWellFM - Ch12 - Body Satisfaction and Well-being data from Figure 11.1 — data_bodywellfm","title":"BodyWellFM - Ch12 - Body Satisfaction and Well-being data from Figure 11.1 — data_bodywellfm","text":"Survey data convenience sample Dominican University students. Reported measures Subjective Wellbeing abd Body Satisfaction.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bodywellfm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BodyWellFM - Ch12 - Body Satisfaction and Well-being data from Figure 11.1 — data_bodywellfm","text":"","code":"data_bodywellfm"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_bodywellfm.html","id":"data-bodywellfm","dir":"Reference","previous_headings":"","what":"data_bodywellfm","title":"BodyWellFM - Ch12 - Body Satisfaction and Well-being data from Figure 11.1 — data_bodywellfm","text":"data frame 106 rows 2 columns: Body Satisfaction numeric Well-numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bodywellm.html","id":null,"dir":"Reference","previous_headings":"","what":"BodyWellM - Ch12 - Body Satisfaction and Well-being data for males from Figure 11.24 left panel — data_bodywellm","title":"BodyWellM - Ch12 - Body Satisfaction and Well-being data for males from Figure 11.24 left panel — data_bodywellm","text":"subset data_bodywell_fm, reports participants identified male. Data Subjective Wellbeing abd Body Satisfaction.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bodywellm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BodyWellM - Ch12 - Body Satisfaction and Well-being data for males from Figure 11.24 left panel — data_bodywellm","text":"","code":"data_bodywellm"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_bodywellm.html","id":"data-bodywellm","dir":"Reference","previous_headings":"","what":"data_bodywellm","title":"BodyWellM - Ch12 - Body Satisfaction and Well-being data for males from Figure 11.24 left panel — data_bodywellm","text":"data frame 47 rows 2 columns: Body Satisfaction numeric Well-numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bushman_2005.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample data for estimate_mdiff_ind_contrast: Bushman (2005) — data_bushman_2005","title":"Sample data for estimate_mdiff_ind_contrast: Bushman (2005) — data_bushman_2005","text":"Bushman (2005) reported investigation people’s memory ads presented different types television show. wanted estimate extent violent content, compared neutral content, might lead reduced memory ads, perhaps reduced purchasing intentions advertised products. investigated questions shows sexual content. chose three- group design efficient way investigate types content. sample 252 typical TV viewers randomly assigned watch one three types show. watched Neutral show (e.g., America’s Funniest Animals), show Violent content (e.g., Cops), others show Sexual content (e.g., Sex City). viewers watched different shows, saw 12 ads inserted shows. ads genuine advertisements, little-known products, viewers never seen ads . enhance realism, viewers watched shows easy chairs, snacks soda available. viewing came surprise memory test ads (participants told purpose study). ’ll report data memory recognition: Participants saw list 12 products, four 4 brand names type product, just one appeared ad. set four 4 brands, participants choose one felt recognized ads just seen, maximum score 12. summary data set, providing sample sizes, means, standard deviations group study.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bushman_2005.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample data for estimate_mdiff_ind_contrast: Bushman (2005) — data_bushman_2005","text":"","code":"data_bushman_2005"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_bushman_2005.html","id":"data-bushman-a-data-frame-with-rows-and-columns-","dir":"Reference","previous_headings":"","what":"data_bushman_2005 A data frame with 3 rows and 4 columns:","title":"Sample data for estimate_mdiff_ind_contrast: Bushman (2005) — data_bushman_2005","text":"condition Factor indicating group: Neutral, Violent, Sexual n Group sample sizes m Group means s Group standard deviation","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_bushman_2005.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Sample data for estimate_mdiff_ind_contrast: Bushman (2005) — data_bushman_2005","text":"Bushman, B. (2005). Violence sex television programs sell products advertisements. Psychological Science, 16, 702-–708. doi:10.1111/j.1467-9280.2005.01599.x","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_campus_involvement.html","id":null,"dir":"Reference","previous_headings":"","what":"Campus Involvement - Ch11 - for End-of-Chapter Exercise 11.7 — data_campus_involvement","title":"Campus Involvement - Ch11 - for End-of-Chapter Exercise 11.7 — data_campus_involvement","text":"Clinton conducted survey college students determine extent subjective well-related campus involvement (Campus Involvement data set book website). Participants completed measure subjective well-(scale 1 5) measure campus involvement (scale 1 5). Participants also reported gender (male female) commuter status (resident commuter). Synthetic data simulated mimic survey data class project.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_campus_involvement.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Campus Involvement - Ch11 - for End-of-Chapter Exercise 11.7 — data_campus_involvement","text":"","code":"data_campus_involvement"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_campus_involvement.html","id":"data-campus-involvement","dir":"Reference","previous_headings":"","what":"data_campus_involvement","title":"Campus Involvement - Ch11 - for End-of-Chapter Exercise 11.7 — data_campus_involvement","text":"data frame 113 rows 6 columns: ID integer Gender factor GPA numeric CommuterStatus factor SWB numeric Campus Involvement numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_chap_8_paired_ex_8.18.html","id":null,"dir":"Reference","previous_headings":"","what":"Fictituous data from an unrealistically small HEAT\r\nstudy comparing scores for a single group of students before and after a\r\nworkshop on climate change. — data_chap_8_paired_ex_8.18","title":"Fictituous data from an unrealistically small HEAT\r\nstudy comparing scores for a single group of students before and after a\r\nworkshop on climate change. — data_chap_8_paired_ex_8.18","text":"Fictituous data unrealistically small HEAT study comparing scores single group students workshop climate change.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_chap_8_paired_ex_8.18.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fictituous data from an unrealistically small HEAT\r\nstudy comparing scores for a single group of students before and after a\r\nworkshop on climate change. — data_chap_8_paired_ex_8.18","text":"","code":"data_chap_8_paired_ex_8.18"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_chap_8_paired_ex_8.18.html","id":"data-chap-paired-ex-","dir":"Reference","previous_headings":"","what":"data_chap_8_paired_ex_8.18","title":"Fictituous data from an unrealistically small HEAT\r\nstudy comparing scores for a single group of students before and after a\r\nworkshop on climate change. — data_chap_8_paired_ex_8.18","text":"data frame 8 rows 2 columns: numeric numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_clean_moral.html","id":null,"dir":"Reference","previous_headings":"","what":"Clean moral - Ch07 - from Schnall et al. (2008), Study 1, and Johnson et al. (2014) — data_clean_moral","title":"Clean moral - Ch07 - from Schnall et al. (2008), Study 1, and Johnson et al. (2014) — data_clean_moral","text":"researchers claim moral judgments based rational considerations also one's current emotional state. extent can recent emotional experiences influence moral judgments? Schnall et al. (2008) examined question manipulating feelings cleanliness purity observing extent changes harshly participants judge morality others. Inscho Study 1, Schnall et al. asked participants complete word scramble task either neutral words (neutral prime) words related cleanliness (cleanliness prime). students completed set moral judgments controversial scenarios: Moral judgment average six items, rated scale 0 9, high meaning harsh. data study Clean moral file, also contains data replication Johnson et al. (2014)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_clean_moral.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clean moral - Ch07 - from Schnall et al. (2008), Study 1, and Johnson et al. (2014) — data_clean_moral","text":"","code":"data_clean_moral"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_clean_moral.html","id":"data-clean-moral","dir":"Reference","previous_headings":"","what":"data_clean_moral","title":"Clean moral - Ch07 - from Schnall et al. (2008), Study 1, and Johnson et al. (2014) — data_clean_moral","text":"data frame 208 rows 4 columns: Schnall Condition factor Schnall Moral judgment numeric Johnson Condition factor Johnson Moral judgment numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_clean_moral.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Clean moral - Ch07 - from Schnall et al. (2008), Study 1, and Johnson et al. (2014) — data_clean_moral","text":"https://econtent.hogrefe.com/doi/full/10.1027/1864-9335/a000186","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_college_survey_1.html","id":null,"dir":"Reference","previous_headings":"","what":"College survey 1 - Ch03 - for End-of-Chapter Exercise 3.3 — data_college_survey_1","title":"College survey 1 - Ch03 - for End-of-Chapter Exercise 3.3 — data_college_survey_1","text":"Data additional survey Dominican University students; reports various psychological behavioral measures.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_college_survey_1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"College survey 1 - Ch03 - for End-of-Chapter Exercise 3.3 — data_college_survey_1","text":"","code":"data_college_survey_1"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_college_survey_1.html","id":"data-college-survey-","dir":"Reference","previous_headings":"","what":"data_college_survey_1","title":"College survey 1 - Ch03 - for End-of-Chapter Exercise 3.3 — data_college_survey_1","text":"data frame 243 rows 23 columns: ID integer Gender factor Gender_Code factor Age integer Shool_Year factor School_Year_Code factor Transfer factor Transfer_Code logical Student_Athlete factor Student_Athlete_Code logical Wealth_SR numeric GPA numeric ACT integer Subjective_Well_Being numeric Positive_Affect numeric Negative_Affect numeric Relationship_Confidence numeric Exercise numeric Academic_Motivation_Intrinsic numeric Academic_Motivation_Extrinsic numeric Academic_Motivation_Amotivation numeric Intelligence_Value numeric Raven_Score numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_college_survey_2.html","id":null,"dir":"Reference","previous_headings":"","what":"College survey 2 - Ch05 - for End-of-Chapter Exercise 5.4 — data_college_survey_2","title":"College survey 2 - Ch05 - for End-of-Chapter Exercise 5.4 — data_college_survey_2","text":"College survey 2 - Ch05 - End--Chapter Exercise 5.4","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_college_survey_2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"College survey 2 - Ch05 - for End-of-Chapter Exercise 5.4 — data_college_survey_2","text":"","code":"data_college_survey_2"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_college_survey_2.html","id":"data-college-survey-","dir":"Reference","previous_headings":"","what":"data_college_survey_2","title":"College survey 2 - Ch05 - for End-of-Chapter Exercise 5.4 — data_college_survey_2","text":"data frame 138 rows 17 columns: ID integer Gender factor Gender_Code factor Age numeric Wealth_SR numeric School_Year factor School_Year_Code factor Transfer factor Transfer_Code logical GPA numeric Subjective_Well_Being numeric Positive_Affect numeric Negative_Affect numeric Academic_Engagement numeric Religious_Meaning numeric Health numeric Emotion_Recognition factor","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_damischrcj.html","id":null,"dir":"Reference","previous_headings":"","what":"DamischRCJ - Ch9 - from 6 Damisch studies, and Calin-Jageman and Caldwell (2014) — data_damischrcj","title":"DamischRCJ - Ch9 - from 6 Damisch studies, and Calin-Jageman and Caldwell (2014) — data_damischrcj","text":"DamischRCJ - Ch9 - 6 Damisch studies, Calin-Jageman Caldwell (2014)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_damischrcj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DamischRCJ - Ch9 - from 6 Damisch studies, and Calin-Jageman and Caldwell (2014) — data_damischrcj","text":"","code":"data_damischrcj"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_damischrcj.html","id":"data-damischrcj","dir":"Reference","previous_headings":"","what":"data_damischrcj","title":"DamischRCJ - Ch9 - from 6 Damisch studies, and Calin-Jageman and Caldwell (2014) — data_damischrcj","text":"data frame 8 rows 5 columns: Study factor Cohen's d unbiased numeric n Control integer n Lucky integer Research Group factor","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_effronraj_fakenews.html","id":null,"dir":"Reference","previous_headings":"","what":"EffronRaj fakenews - Ch8 - from Effron and Raj (2020) — data_effronraj_fakenews","title":"EffronRaj fakenews - Ch8 - from Effron and Raj (2020) — data_effronraj_fakenews","text":"Synthetic data meant represent Experiment 1 Effron & Raj, 2020. 138 U.S. adults, recruited August 2018 Prolific Academic, worked online. First, saw six fake headlines four times, time asked rate interesting/engaging/ funny/well-written headline . rating task simply ensured participants paid attention headline. stimuli 12 actual fake-news headlines American politics, accompanying photographs. Half appealed Republicans half Democrats. Later, 12 fake headlines presented one time, random mix six Old headlines-seen -six New headlines seen previously. stated clearly independent, non-partisan fact-checking established headlines true. Participants first rated, 0 () 100 (extremely) scale, degree judged unethical publish headline. Unethicality DV. also rated likely share headline saw posted acquaintance social media; three similar ratings. Finally, rated accurate believed headline .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_effronraj_fakenews.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"EffronRaj fakenews - Ch8 - from Effron and Raj (2020) — data_effronraj_fakenews","text":"","code":"data_effronraj_fakenews"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_effronraj_fakenews.html","id":"data-effronraj-fakenews","dir":"Reference","previous_headings":"","what":"data_effronraj_fakenews","title":"EffronRaj fakenews - Ch8 - from Effron and Raj (2020) — data_effronraj_fakenews","text":"data frame 138 rows 5 columns: ID factor UnethOld numeric UnethNew numeric AccurOld numeric AccurNew numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_effronraj_fakenews.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"EffronRaj fakenews - Ch8 - from Effron and Raj (2020) — data_effronraj_fakenews","text":"https://journals.sagepub.com/doi/full/10.1177/0956797619887896","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_emotion_heartrate.html","id":null,"dir":"Reference","previous_headings":"","what":"Emotion heartrate - Ch8 - from Lakens (2013) — data_emotion_heartrate","title":"Emotion heartrate - Ch8 - from Lakens (2013) — data_emotion_heartrate","text":"Anger powerful emotion. extent can feeling angry actually change heart rate? investigate, Lakens (2013) asked students record heart rate (beats per minute) rest (baseline) recalling time intense anger. conceptual replication classic study Ekman et al. (1983). Load Emotion heartrate data set book website.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_emotion_heartrate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Emotion heartrate - Ch8 - from Lakens (2013) — data_emotion_heartrate","text":"","code":"data_emotion_heartrate"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_emotion_heartrate.html","id":"data-emotion-heartrate","dir":"Reference","previous_headings":"","what":"data_emotion_heartrate","title":"Emotion heartrate - Ch8 - from Lakens (2013) — data_emotion_heartrate","text":"data frame 68 rows 3 columns: ID integer HR_baseline numeric HR_anger numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_emotion_heartrate.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Emotion heartrate - Ch8 - from Lakens (2013) — data_emotion_heartrate","text":"https://ieeexplore.ieee.org/abstract/document/6464255","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_exam_scores.html","id":null,"dir":"Reference","previous_headings":"","what":"Exam Scores - Ch11 - for End-of-Chapter Exercise 11.2 — data_exam_scores","title":"Exam Scores - Ch11 - for End-of-Chapter Exercise 11.2 — data_exam_scores","text":"extent initial performance class relate performance final exam? First exam final exam scores nine students enrolled introductory psychology course. Exam scores percentages, 0 = answers correct 100 = answers correct. Data synthetic represent patterns found previous psych stats course.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_exam_scores.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Exam Scores - Ch11 - for End-of-Chapter Exercise 11.2 — data_exam_scores","text":"","code":"data_exam_scores"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_exam_scores.html","id":"data-exam-scores","dir":"Reference","previous_headings":"","what":"data_exam_scores","title":"Exam Scores - Ch11 - for End-of-Chapter Exercise 11.2 — data_exam_scores","text":"data frame 9 rows 3 columns: StudentID factor First Exam numeric Final Exam numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_flag_priming_ma.html","id":null,"dir":"Reference","previous_headings":"","what":"Flag Priming ma - Ch9 - Many Labs replications of Carter et al. (2011) — data_flag_priming_ma","title":"Flag Priming ma - Ch9 - Many Labs replications of Carter et al. (2011) — data_flag_priming_ma","text":"extent exposed American flag influence political attitudes? One seminal study (Carter et al., 2011) explored issue subtly exposing participants either images American flag control images. Next, participants asked political attitudes, using 1-7 rating scale high scores indicate conservative attitudes. Participants exposed flag found express substantially conservative attitudes. Many Labs project replicated finding 25 different locations United States.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_flag_priming_ma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Flag Priming ma - Ch9 - Many Labs replications of Carter et al. (2011) — data_flag_priming_ma","text":"","code":"data_flag_priming_ma"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_flag_priming_ma.html","id":"data-flag-priming-ma","dir":"Reference","previous_headings":"","what":"data_flag_priming_ma","title":"Flag Priming ma - Ch9 - Many Labs replications of Carter et al. (2011) — data_flag_priming_ma","text":"data frame 25 rows 7 columns: Location factor M Flag numeric s Flag numeric n Flag integer M Noflag numeric s Noflag numeric n Noflag integer","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_flag_priming_ma.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Flag Priming ma - Ch9 - Many Labs replications of Carter et al. (2011) — data_flag_priming_ma","text":"https://openpsychologydata.metajnl.com/articles/10.5334/jopd.ad","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_gender_math_iat.html","id":null,"dir":"Reference","previous_headings":"","what":"Gender math IAT - Ch07 - Ithaca and SDSU replications of Nosek et al. (2002) — data_gender_math_iat","title":"Gender math IAT - Ch07 - Ithaca and SDSU replications of Nosek et al. (2002) — data_gender_math_iat","text":"extent men women differ attitudes towards mathematics? investigate, Nosek et al. (2002) asked male female students complete Implicit Association Test (IAT)-task designed measure participant's implicit (non-conscious) feelings towards topic. (never heard IAT, try : tiny.cc/harvardiat) IAT, students tested negative feelings towards mathematics art. Scores reflect degree student negative implicit attitudes mathematics art (positive score: negative feelings mathematics; 0: level negativity ; negative score: negative feelings art). data_gender_math_iat data two labs participated large-scale replication original study (Klein et al., 2014a, 2014b)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_gender_math_iat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gender math IAT - Ch07 - Ithaca and SDSU replications of Nosek et al. (2002) — data_gender_math_iat","text":"","code":"data_gender_math_iat"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_gender_math_iat.html","id":"data-gender-math-iat","dir":"Reference","previous_headings":"","what":"data_gender_math_iat","title":"Gender math IAT - Ch07 - Ithaca and SDSU replications of Nosek et al. (2002) — data_gender_math_iat","text":"data frame 155 rows 4 columns: Ithaca gender factor Ithaca IAT numeric SDSU gender factor SDSU IAT numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_gender_math_iat.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Gender math IAT - Ch07 - Ithaca and SDSU replications of Nosek et al. (2002) — data_gender_math_iat","text":"https://psycnet.apa.org/fulltext/2014-20922-002.html","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_gender_math_iat_ma.html","id":null,"dir":"Reference","previous_headings":"","what":"Gender math IAT ma - Ch9 - Many Labs replications of Nosek et al. (2002) — data_gender_math_iat_ma","title":"Gender math IAT ma - Ch9 - Many Labs replications of Nosek et al. (2002) — data_gender_math_iat_ma","text":"EOC Exercise 4 Chapter 7 encountered classic study Nosek et al. (2002), male female participants completed Implicit Association Test (IAT) measured extent negative attitudes towards mathematics, compared art. study found women, compared men, tended negative implicit attitudes towards mathematics. Many Labs project repeated study locations around world (Klein et al., 2014a, 2014b). Summary data 30 labs available Gender math IAT ma. Higher scores indicate implicit bias mathematics. See also data_gender_math_iat raw data two specific sites replication effort.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_gender_math_iat_ma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gender math IAT ma - Ch9 - Many Labs replications of Nosek et al. (2002) — data_gender_math_iat_ma","text":"","code":"data_gender_math_iat_ma"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_gender_math_iat_ma.html","id":"data-gender-math-iat-ma","dir":"Reference","previous_headings":"","what":"data_gender_math_iat_ma","title":"Gender math IAT ma - Ch9 - Many Labs replications of Nosek et al. (2002) — data_gender_math_iat_ma","text":"data frame 30 rows 9 columns: Location factor M Male numeric s Male numeric n Male integer M Female numeric s Female numeric n Female integer USAorNot factor Country factor","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_gender_math_iat_ma.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Gender math IAT ma - Ch9 - Many Labs replications of Nosek et al. (2002) — data_gender_math_iat_ma","text":"https://psycnet.apa.org/fulltext/2014-20922-002.html","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_halagappa.html","id":null,"dir":"Reference","previous_headings":"","what":"Halagappa - Ch14 - from Halagappa et al. (2007) — data_halagappa","title":"Halagappa - Ch14 - from Halagappa et al. (2007) — data_halagappa","text":"eating much less delay Alzheimer's? , great news. Halagappa et al. (2007) investigated possibility using mouse model, meaning used Alzheimer-prone mice, genetically predisposed develop neural degeneration typical Alzheimer's. researchers used six independent groups mice, three tested mouse middle age 10 months old, three mouse old age 17 months. age control group normal mice ate freely (NFree10 NFree17 groups), group Alzheimer-prone mice also ate freely (AFree10 AFree17 groups), another Alzheimer-prone group restricted 40% less food normal (ADiet10 ADiet17 groups). Table 14.2 lists factors define groups, group labels. discuss one measure mouse cognition: percent time spent near target water maze, higher values indicating better learning memory. Table 14.2 reports means standard deviations measure, group sizes.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_halagappa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Halagappa - Ch14 - from Halagappa et al. (2007) — data_halagappa","text":"","code":"data_halagappa"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_halagappa.html","id":"data-halagappa","dir":"Reference","previous_headings":"","what":"data_halagappa","title":"Halagappa - Ch14 - from Halagappa et al. (2007) — data_halagappa","text":"data frame 6 rows 4 columns: Groups factor Mean numeric SD numeric n integer","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_halagappa.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Halagappa - Ch14 - from Halagappa et al. (2007) — data_halagappa","text":"https://www.sciencedirect.com/science/article/abs/pii/S0969996106003251","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_halagappa_et_al_2007.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample data for estimate_mdiff_ind_contrast: Halagappa (2007) — data_halagappa_et_al_2007","title":"Sample data for estimate_mdiff_ind_contrast: Halagappa (2007) — data_halagappa_et_al_2007","text":"eating much less delay Alzheimer’s? , great news. Halagappa et al. (2007) investigated possibility using mouse model, meaning used Alzheimer-prone mice, genetically predisposed develop neural degeneration typical Alzheimer’s. researchers used six independent groups mice, three tested mouse middle age 10 months old, three mouse old age 17 months. age control group normal mice ate freely (NFree10 NFree17 groups), group Alzheimer-prone mice also ate freely (AFree10 AFree17 groups), another Alzheimer-prone group restricted 40% less food normal (ADiet10 ADiet17 groups). Table 14.2 lists factors define groups, group labels, means, M1, M2, … (’ll see displayed ESCI). ’ll discuss one measure mouse cognition: percent time spent near target water maze, higher values indicating better learning memory. summary data set, providing sample sizes, means, standard deviations group study.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_halagappa_et_al_2007.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample data for estimate_mdiff_ind_contrast: Halagappa (2007) — data_halagappa_et_al_2007","text":"","code":"data_halagappa_et_al_2007"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_halagappa_et_al_2007.html","id":"data-halagappa-et-al-a-data-frame-with-rows-and-","dir":"Reference","previous_headings":"","what":"data_halagappa_et_al_2007 A data frame with 3 rows and 4","title":"Sample data for estimate_mdiff_ind_contrast: Halagappa (2007) — data_halagappa_et_al_2007","text":"columns: condition Factor indicating group: NFree10, AFree10, ADiet10, NFree17, AFree17, ADiet17 n Group sample sizes m Group means s Group standard deviation","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_halagappa_et_al_2007.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Sample data for estimate_mdiff_ind_contrast: Halagappa (2007) — data_halagappa_et_al_2007","text":"Halagappa, V. K. M., Guo, Z., Pearson, M., Matsuoka, Y., Cutler, R. G., LaFerla, F. M., & Mattson, M. P. (2007). Intermittent fasting caloric restriction ameliorate age-related behavioral deficits triple-transgenic mouse model Alzheimer's disease. Neurobiology Disease, 26, 212–220. doi:10.1016/j.nbd.2006.12.019","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_home_prices.html","id":null,"dir":"Reference","previous_headings":"","what":"Home Prices - Ch12 - for End-of-Chapter Exercise 12.2 — data_home_prices","title":"Home Prices - Ch12 - for End-of-Chapter Exercise 12.2 — data_home_prices","text":"Maybe thinking buying house college? Regression can help hunt bargain. Download Home Prices data set. file contains real estate listings 1997 2003 city California. explore extent size home (square meters) predicts sale price.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_home_prices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Home Prices - Ch12 - for End-of-Chapter Exercise 12.2 — data_home_prices","text":"","code":"data_home_prices"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_home_prices.html","id":"data-home-prices","dir":"Reference","previous_headings":"","what":"data_home_prices","title":"Home Prices - Ch12 - for End-of-Chapter Exercise 12.2 — data_home_prices","text":"data frame 300 rows 8 columns: MLS integer Location factor Price integer Bedrooms integer Bathrooms integer Size (m2) numeric Status factor Status_Code logical","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_kardas_expt_3.html","id":null,"dir":"Reference","previous_headings":"","what":"Kardas Expt 3 - Ch07 - from Kardas and O'Brien (2018), Experiment 3 — data_kardas_expt_3","title":"Kardas Expt 3 - Ch07 - from Kardas and O'Brien (2018), Experiment 3 — data_kardas_expt_3","text":"Suppose want change battery phone, cook perfect souffle, perform three-ball juggle. Just numerous people every day, might search online find video shows . Suppose watch video just . First question: well predict perform task? Second question: well actually perform task, first time tried? Now suppose watch video many times: consider two questions. questions investigated series studies Kardas O'Brien (2018). first quick analyses Kardas Experiments 3 4-call Expt 3 Expt 4-focusing effect watching video many times rather . Expt 3, participants first watched brief video person performing moonwalk. Low Exposure group watched video , High Exposure group 20 times. participants predicted, 1 10 scale, well felt able perform moonwalk . Finally, attempted single performance moonwalk, videoed. videos rated, 1 10 scale, independent raters.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_kardas_expt_3.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kardas Expt 3 - Ch07 - from Kardas and O'Brien (2018), Experiment 3 — data_kardas_expt_3","text":"","code":"data_kardas_expt_3"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_kardas_expt_3.html","id":"data-kardas-expt-","dir":"Reference","previous_headings":"","what":"data_kardas_expt_3","title":"Kardas Expt 3 - Ch07 - from Kardas and O'Brien (2018), Experiment 3 — data_kardas_expt_3","text":"data frame 100 rows 3 columns: Exposure factor Prediction numeric Performance numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_kardas_expt_3.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Kardas Expt 3 - Ch07 - from Kardas and O'Brien (2018), Experiment 3 — data_kardas_expt_3","text":"https://journals.sagepub.com/doi/abs/10.1177/0956797617740646","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_kardas_expt_4.html","id":null,"dir":"Reference","previous_headings":"","what":"Kardas Expt 4 - Ch07 - from Kardas and O'Brien (2018), Experiment 4 — data_kardas_expt_4","title":"Kardas Expt 4 - Ch07 - from Kardas and O'Brien (2018), Experiment 4 — data_kardas_expt_4","text":"Suppose want change battery phone, cook perfect souffle, perform three-ball juggle. Just numerous people every day, might search online find video shows . Suppose watch video just . First question: well predict perform task? Second question: well actually perform task, first time tried? Now suppose watch video many times: consider two questions. questions investigated series studies Kardas O'Brien (2018). first quick analyses Kardas Experiments 3 4-call Expt 3 Expt 4-focusing effect watching video many times rather . Expt 4 conducted online participants recruited Amazon's Mechanical Turk, typically diverse students. online task based mirror-drawing game developed Bob students (Cusack et al., 2015, tiny.cc/bobmirrortrace). Participants first read description game scoring procedure. play, use computer trackpad trace target line, accurately quickly can. task tricky can see mirror image path tracing finger trackpad. running score displayed. final score percentage match target line path traced, scores can range 0 100","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_kardas_expt_4.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kardas Expt 4 - Ch07 - from Kardas and O'Brien (2018), Experiment 4 — data_kardas_expt_4","text":"","code":"data_kardas_expt_4"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_kardas_expt_4.html","id":"data-kardas-expt-","dir":"Reference","previous_headings":"","what":"data_kardas_expt_4","title":"Kardas Expt 4 - Ch07 - from Kardas and O'Brien (2018), Experiment 4 — data_kardas_expt_4","text":"data frame 270 rows 4 columns: Exposure factor Prediction integer Performance integer Time numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_kardas_expt_4.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Kardas Expt 4 - Ch07 - from Kardas and O'Brien (2018), Experiment 4 — data_kardas_expt_4","text":"https://journals.sagepub.com/doi/abs/10.1177/0956797617740646","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_labels_flavor.html","id":null,"dir":"Reference","previous_headings":"","what":"Labels flavor - Ch8 - from Floretta-Schiller et al. (2015) — data_labels_flavor","title":"Labels flavor - Ch8 - from Floretta-Schiller et al. (2015) — data_labels_flavor","text":"extent brand labels influence perceptions product? investigate, participants asked participate taste test. participants actually given grape juice, one glass poured bottle labeled 'Organic' glass bottle labeled 'Generic'. tasting (counterbalanced order), participants asked rate much enjoyed juice scale 1 () 10 (much). Participants also asked say much willing pay large container juice scale $1 $10. Load Labels flavor data set book website. data collected part class project Floretta-Schiller et al. (2015), whose work inspired clever study looking effects fast-food wrappers children's enjoyment food (Robinson et al., 2007).","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_labels_flavor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Labels flavor - Ch8 - from Floretta-Schiller et al. (2015) — data_labels_flavor","text":"","code":"data_labels_flavor"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_labels_flavor.html","id":"data-labels-flavor","dir":"Reference","previous_headings":"","what":"data_labels_flavor","title":"Labels flavor - Ch8 - from Floretta-Schiller et al. (2015) — data_labels_flavor","text":"data frame 51 rows 6 columns: ParticipantID integer Enjoy_Generic numeric Enjoy_Organic numeric Pay_Generic numeric Pay_Organic numeric Suspicious logical","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample data for estimate_mdiff_ind_contrast: Test score and times from\r\nLatimier et al (2019). — data_latimier","title":"Sample data for estimate_mdiff_ind_contrast: Test score and times from\r\nLatimier et al (2019). — data_latimier","text":"researchers interested different study approaches might impact learning. Working France, created three independent groups, comprising 95 adults. Participants worked online seven learning modules DNA. Reread group worked module worked second time, going next module. Quiz group worked module complete quiz going next module. Prequiz group work quiz seeing presentation module, went quiz presentation next module. Participants received feedback brief explanation answering question quiz, take long wished work module quiz. Seven days later participants completed final test.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample data for estimate_mdiff_ind_contrast: Test score and times from\r\nLatimier et al (2019). — data_latimier","text":"","code":"data_latimier"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier.html","id":"data-latimier-a-data-frame-with-rows-and-columns-","dir":"Reference","previous_headings":"","what":"data_latimier A data frame with 285 rows and 3 columns:","title":"Sample data for estimate_mdiff_ind_contrast: Test score and times from\r\nLatimier et al (2019). — data_latimier","text":"Group factor indicating membership Reread, Quiz, Prequiz group Test % correct final test Time Time taken final test","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Sample data for estimate_mdiff_ind_contrast: Test score and times from\r\nLatimier et al (2019). — data_latimier","text":"https://osf.io/23yad Latimier, ., Riegert, ., Peyre, H., Ly, S. T., Casati, R., & Ramus, F. (2019). pre-testing promote better retention post-testing? Npj Science Learning, 4(1), 1–7. https://doi.org/10.1038/s41539-019-0053-1","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Sample data for estimate_mdiff_ind_contrast: Test score and times from\r\nLatimier et al (2019). — data_latimier","text":"data set suitable estimating magnitude difference contrast independent groups.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_3groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Latimier 3Groups - Ch14 - 3 groups in Latimier et al. (2019) — data_latimier_3groups","title":"Latimier 3Groups - Ch14 - 3 groups in Latimier et al. (2019) — data_latimier_3groups","text":"researchers interested different study approaches might impact learning. Working France, created three independent groups, comprising 95 adults. Participants worked online seven learning modules DNA. Reread group worked module, worked second time going next module. Quiz group worked module, complete quiz going next module. Prequiz group work quiz seeing presentation module, went quiz presentation next module. Participants received feedback brief explanation answering question quiz, take long wished work module quiz. Seven days later, participants completed final test. data_latimier_3groups full data set. facilitate different student exercises, also seperate data entities group (data_latimier_prequiz, data_latimier_reread, etc.), every pair groups (data_latimier_quiz_prequiz, etc.).","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_3groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Latimier 3Groups - Ch14 - 3 groups in Latimier et al. (2019) — data_latimier_3groups","text":"","code":"data_latimier_3groups"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_3groups.html","id":"data-latimier-groups","dir":"Reference","previous_headings":"","what":"data_latimier_3groups","title":"Latimier 3Groups - Ch14 - 3 groups in Latimier et al. (2019) — data_latimier_3groups","text":"data frame 285 rows 3 columns: Group factor Test% numeric Time numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_prequiz.html","id":null,"dir":"Reference","previous_headings":"","what":"Latimier Prequiz - Ch03 - Prequiz group in Latimier et al. (2019) — data_latimier_prequiz","title":"Latimier Prequiz - Ch03 - Prequiz group in Latimier et al. (2019) — data_latimier_prequiz","text":"Just Prequiz group Latimier et al., 2019 See full details data_latimier_3_groups","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_prequiz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Latimier Prequiz - Ch03 - Prequiz group in Latimier et al. (2019) — data_latimier_prequiz","text":"","code":"data_latimier_prequiz"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_prequiz.html","id":"data-latimier-prequiz","dir":"Reference","previous_headings":"","what":"data_latimier_prequiz","title":"Latimier Prequiz - Ch03 - Prequiz group in Latimier et al. (2019) — data_latimier_prequiz","text":"data frame 95 rows 3 columns: Group factor Test% numeric TIme numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_prequiz.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Latimier Prequiz - Ch03 - Prequiz group in Latimier et al. (2019) — data_latimier_prequiz","text":"https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XPYPMF","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_quiz.html","id":null,"dir":"Reference","previous_headings":"","what":"Latimier Quiz - Ch03 - Quiz group in Latimier et al. (2019) — data_latimier_quiz","title":"Latimier Quiz - Ch03 - Quiz group in Latimier et al. (2019) — data_latimier_quiz","text":"Just Quiz group Latimier et al., 2019 See full details data_latimier_3_groups","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_quiz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Latimier Quiz - Ch03 - Quiz group in Latimier et al. (2019) — data_latimier_quiz","text":"","code":"data_latimier_quiz"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_quiz.html","id":"data-latimier-quiz","dir":"Reference","previous_headings":"","what":"data_latimier_quiz","title":"Latimier Quiz - Ch03 - Quiz group in Latimier et al. (2019) — data_latimier_quiz","text":"data frame 95 rows 3 columns: Group factor Test% numeric Time numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_quiz.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Latimier Quiz - Ch03 - Quiz group in Latimier et al. (2019) — data_latimier_quiz","text":"https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XPYPMF","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_quiz_prequiz.html","id":null,"dir":"Reference","previous_headings":"","what":"Latimier Quiz Prequiz - Ch07 - Quiz and Prequiz groups in Latimier et al. (2019) — data_latimier_quiz_prequiz","title":"Latimier Quiz Prequiz - Ch07 - Quiz and Prequiz groups in Latimier et al. (2019) — data_latimier_quiz_prequiz","text":"Just Quiz (RQ) Prequiz (QR) groups Latimier et al., 2019 See full details data_latimier_3_groups","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_quiz_prequiz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Latimier Quiz Prequiz - Ch07 - Quiz and Prequiz groups in Latimier et al. (2019) — data_latimier_quiz_prequiz","text":"","code":"data_latimier_quiz_prequiz"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_quiz_prequiz.html","id":"data-latimier-quiz-prequiz","dir":"Reference","previous_headings":"","what":"data_latimier_quiz_prequiz","title":"Latimier Quiz Prequiz - Ch07 - Quiz and Prequiz groups in Latimier et al. (2019) — data_latimier_quiz_prequiz","text":"data frame 190 rows 3 columns: Group factor Test% numeric Time numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_quiz_prequiz.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Latimier Quiz Prequiz - Ch07 - Quiz and Prequiz groups in Latimier et al. (2019) — data_latimier_quiz_prequiz","text":"https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XPYPMF","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_reread.html","id":null,"dir":"Reference","previous_headings":"","what":"Latimier Reread - Ch03 - Reread group in Latimier et al. (2019) — data_latimier_reread","title":"Latimier Reread - Ch03 - Reread group in Latimier et al. (2019) — data_latimier_reread","text":"Just Reread group Latimier et al., 2019 See full details data_latimier_3_groups","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_reread.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Latimier Reread - Ch03 - Reread group in Latimier et al. (2019) — data_latimier_reread","text":"","code":"data_latimier_reread"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_reread.html","id":"data-latimier-reread","dir":"Reference","previous_headings":"","what":"data_latimier_reread","title":"Latimier Reread - Ch03 - Reread group in Latimier et al. (2019) — data_latimier_reread","text":"data frame 95 rows 3 columns: Group factor Test% numeric Time numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_reread.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Latimier Reread - Ch03 - Reread group in Latimier et al. (2019) — data_latimier_reread","text":"https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XPYPMF","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_reread_prequiz.html","id":null,"dir":"Reference","previous_headings":"","what":"Latimier Reread Prequiz - Ch07 - Reread and Prequiz groups in Latimier et al. (2019) — data_latimier_reread_prequiz","title":"Latimier Reread Prequiz - Ch07 - Reread and Prequiz groups in Latimier et al. (2019) — data_latimier_reread_prequiz","text":"Just Reread (RR) Prequiz (QR) groups Latimier et al., 2019 See full details data_latimier_3_groups","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_reread_prequiz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Latimier Reread Prequiz - Ch07 - Reread and Prequiz groups in Latimier et al. 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(2019) — data_latimier_reread_prequiz","text":"https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XPYPMF","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_reread_quiz.html","id":null,"dir":"Reference","previous_headings":"","what":"Latimier Reread Quiz - Ch07 - Reread and Quiz groups in Latimier et al. (2019) — data_latimier_reread_quiz","title":"Latimier Reread Quiz - Ch07 - Reread and Quiz groups in Latimier et al. (2019) — data_latimier_reread_quiz","text":"Just Reread Quiz groups Latimier et al., 2019 See full details data_latimier_3_groups","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_reread_quiz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Latimier Reread Quiz - Ch07 - Reread and Quiz groups in Latimier et al. (2019) — data_latimier_reread_quiz","text":"","code":"data_latimier_reread_quiz"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_reread_quiz.html","id":"data-latimier-reread-quiz","dir":"Reference","previous_headings":"","what":"data_latimier_reread_quiz","title":"Latimier Reread Quiz - Ch07 - Reread and Quiz groups in Latimier et al. (2019) — data_latimier_reread_quiz","text":"data frame 190 rows 3 columns: Group factor Test% numeric Time numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_latimier_reread_quiz.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Latimier Reread Quiz - Ch07 - Reread and Quiz groups in Latimier et al. (2019) — data_latimier_reread_quiz","text":"https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/XPYPMF","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_macnamara_r_ma.html","id":null,"dir":"Reference","previous_headings":"","what":"Macnamara r ma - Ch11 - from Macnamara et al. (2014) — data_macnamara_r_ma","title":"Macnamara r ma - Ch11 - from Macnamara et al. (2014) — data_macnamara_r_ma","text":"genius born made? us Michael Jordan Mozart worked sufficiently hard develop requisite skills? Meta-analysis correlations can help answer questions. issue extent practice effort may sufficient achieving highest levels expertise. Ericsson et al. (1993) argued years effort matters : 'Many characteristics believed reflect innate talent actually result intense practice extended minimum 10 years' (p. 363). view enormously popularized Malcolm Gladwell (2008), argued book Outliers 10,000 hours focused practice key achieving expertise. However, view now challenged, one important contribution large meta-analysis correlations amount intense practice level achievement: Macnamara et al. (2014) combined 157 correlations reported wide range fields, sports music education, found correlation r = .35 (.30, .39). Table 11.1 shows 16 main correlations music, Macnamara et al. (2014).","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_macnamara_r_ma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Macnamara r ma - Ch11 - from Macnamara et al. (2014) — data_macnamara_r_ma","text":"","code":"data_macnamara_r_ma"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_macnamara_r_ma.html","id":"data-macnamara-r-ma","dir":"Reference","previous_headings":"","what":"data_macnamara_r_ma","title":"Macnamara r ma - Ch11 - from Macnamara et al. (2014) — data_macnamara_r_ma","text":"data frame 16 rows 4 columns: Study factor r numeric N integer Instrument_Type factor","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_macnamara_r_ma.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Macnamara r ma - Ch11 - from Macnamara et al. (2014) — data_macnamara_r_ma","text":"https://journals.sagepub.com/doi/full/10.1177/0956797614535810","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_mccabemichael_brain.html","id":null,"dir":"Reference","previous_headings":"","what":"McCabeMichael brain - Ch9 - from Michael et al. (2013) — data_mccabemichael_brain","title":"McCabeMichael brain - Ch9 - from Michael et al. (2013) — data_mccabemichael_brain","text":"probably seen cross sections brain brightly colored areas indicating brain regions active particular type cognition emotion. Search online fMRI (functional magnetic resonance imaging) brain scans see pictures learn made. can fascinating-last able see thinking works? 2008, McCabe Castel published studies investigated adding brain picture might alter judgments credibility scientific article. one group participants, article accompanied brain image irrelevant article. second, independent group, image. Participants read article, gave rating statement 'scientific reasoning article made sense'. response options 1 (strongly disagree), 2 (disagree), 3 (agree), 4 (strongly agree). researchers reported mean ratings higher brain picture without, difference small. seemed irrelevant brain picture may , small influence. authors drew appropriately cautious conclusions, result quickly attracted attention many media reports greatly overstated . least according popular media, seemed adding brain picture made story convincing. Search 'McCabe seeing believing', similar, find media reports blog posts. warned readers watch brain pictures, , said, can trick believing things true. result intrigued New Zealander colleagues mine discovered , despite wide recognition, finding replicated. ran replication studies using materials used original researchers, found generally small ESs. joined team data analysis stage research published (Michael et al., 2013). discuss meta-analysis two original studies eight replications team. studies sufficiently similar meta-analysis, especially considering Michael studies designed many features matched original studies. data set include two additional critique studies run Michael team. See also data_mccabemichael_brain2","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_mccabemichael_brain.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"McCabeMichael brain - Ch9 - from Michael et al. (2013) — data_mccabemichael_brain","text":"","code":"data_mccabemichael_brain"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_mccabemichael_brain.html","id":"data-mccabemichael-brain","dir":"Reference","previous_headings":"","what":"data_mccabemichael_brain","title":"McCabeMichael brain - Ch9 - from Michael et al. (2013) — data_mccabemichael_brain","text":"data frame 10 rows 9 columns: Study name factor M Brain numeric s Brain numeric n Brain numeric M Brain numeric s Brain numeric n Brain numeric SimpleCritique factor Research group factor","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_mccabemichael_brain.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"McCabeMichael brain - Ch9 - from Michael et al. (2013) — data_mccabemichael_brain","text":"https://link.springer.com/article/10.3758/s13423-013-0391-6","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_mccabemichael_brain2.html","id":null,"dir":"Reference","previous_headings":"","what":"McCabeMichael brain2 - Ch9 - from Michael et al. (2013) — data_mccabemichael_brain2","title":"McCabeMichael brain2 - Ch9 - from Michael et al. (2013) — data_mccabemichael_brain2","text":"data_mccabemichael_brain includes two additional critique studies run Michael team.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_mccabemichael_brain2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"McCabeMichael brain2 - Ch9 - from Michael et al. (2013) — data_mccabemichael_brain2","text":"","code":"data_mccabemichael_brain2"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_mccabemichael_brain2.html","id":"data-mccabemichael-brain-","dir":"Reference","previous_headings":"","what":"data_mccabemichael_brain2","title":"McCabeMichael brain2 - Ch9 - from Michael et al. (2013) — data_mccabemichael_brain2","text":"data frame 12 rows 9 columns: Study name factor M Brain numeric s Brain numeric n Brain numeric M Brain numeric s Brain numeric n Brain numeric SimpleCritique factor Research group factor","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_mccabemichael_brain2.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"McCabeMichael brain2 - Ch9 - from Michael et al. (2013) — data_mccabemichael_brain2","text":"https://link.springer.com/article/10.3758/s13423-013-0391-6","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_meditationbrain.html","id":null,"dir":"Reference","previous_headings":"","what":"MeditationBrain - Ch15 - from Holzel et al. (2011) — data_meditationbrain","title":"MeditationBrain - Ch15 - from Holzel et al. (2011) — data_meditationbrain","text":"example well-known study mindfulness meditation Holzel et al. (2011). People wanted reduce stress, experienced meditators, assigned Meditation (n = 16) Control (n = 17) group. Meditation group participated 8 weeks intensive training practice mindfulness meditation. researchers used questionnaire assess range emotional cognitive variables (Pretest) (Posttest) 8-week period. assessment conducted participants meditating. study notable including brain imaging assess possible changes participants' brains Pretest Posttest. researchers measured gray matter concentration, increases brain regions experience higher frequent activation. researchers expected hippocampus may especially responsive meditation implicated regulation emotion, arousal, general responsiveness. therefore included planned analysis assessment changes gray matter concentration hippocampus.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_meditationbrain.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MeditationBrain - Ch15 - from Holzel et al. (2011) — data_meditationbrain","text":"","code":"data_meditationbrain"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_meditationbrain.html","id":"data-meditationbrain","dir":"Reference","previous_headings":"","what":"data_meditationbrain","title":"MeditationBrain - Ch15 - from Holzel et al. (2011) — data_meditationbrain","text":"data frame 33 rows 7 columns: Pretest numeric Posttest numeric Group factor ControlPre numeric ControlPost numeric MeditationPre numeric MeditationPost numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_meditationbrain.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"MeditationBrain - Ch15 - from Holzel et al. (2011) — data_meditationbrain","text":"https://link.springer.com/chapter/10.1007/978-94-007-2079-4_9","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_organicmoral.html","id":null,"dir":"Reference","previous_headings":"","what":"OrganicMoral - Ch14 - from Eskine (2013) — data_organicmoral","title":"OrganicMoral - Ch14 - from Eskine (2013) — data_organicmoral","text":"extent might choosing organic foods make us morally smug? investigate, Eskine (2013) asked participants rate images organic food, neutral (control) food, comfort food. Next, guise different study, participants completed moral judgment scale read different controversial scenarios rated morally wrong judged (scale 1-7, high judgments mean wrong). Table 14.7 shows summary data, also available first four variables OrganicMoral file. file can see two variables, report full data-come shortly. use summary data. results Eskine (2013) published, Moery Calin-Jageman (2016) conducted series close replications. obtained original materials Eskine, piloted procedure, preregistered sampling analysis plan. OSF page, osf.io/atkn7, details. data one close replications last two variables OrganicMoral file. replication study, group names variable ReplicationGroup moral judgments MoralJudgment. (may need scroll right see variables.)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_organicmoral.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"OrganicMoral - Ch14 - from Eskine (2013) — data_organicmoral","text":"","code":"data_organicmoral"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_organicmoral.html","id":"data-organicmoral","dir":"Reference","previous_headings":"","what":"data_organicmoral","title":"OrganicMoral - Ch14 - from Eskine (2013) — data_organicmoral","text":"data frame 106 rows 6 columns: Group factor Mean numeric SD numeric N integer ReplicationGroup factor MoralJudgmentment numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_organicmoral.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"OrganicMoral - Ch14 - from Eskine (2013) — data_organicmoral","text":"https://journals.sagepub.com/doi/full/10.1177/1948550616639649","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_penlaptop1.html","id":null,"dir":"Reference","previous_headings":"","what":"% transcription scores from pen and laptop group of Meuller et al., 2014 — data_penlaptop1","title":"% transcription scores from pen and laptop group of Meuller et al., 2014 — data_penlaptop1","text":"% transcription scores pen laptop group Meuller et al., 2014","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_penlaptop1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"% transcription scores from pen and laptop group of Meuller et al., 2014 — data_penlaptop1","text":"","code":"data_penlaptop1"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_penlaptop1.html","id":"data-penlaptop-","dir":"Reference","previous_headings":"","what":"data_penlaptop1","title":"% transcription scores from pen and laptop group of Meuller et al., 2014 — data_penlaptop1","text":"data frame 65 rows 2 columns: condition factor transcription numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_penlaptop1.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"% transcription scores from pen and laptop group of Meuller et al., 2014 — data_penlaptop1","text":"https://journals.sagepub.com/doi/full/10.1177/0956797614524581","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_powerperformance_ma.html","id":null,"dir":"Reference","previous_headings":"","what":"PowerPerformance ma - Ch9 - from Burgmer and Englich (2012), and Cusack et al. (2015) — data_powerperformance_ma","title":"PowerPerformance ma - Ch9 - from Burgmer and Englich (2012), and Cusack et al. (2015) — data_powerperformance_ma","text":"extent feeling powerful affect performance motor skills? investigate, Burgmer Englich (2012) assigned German participants either power control conditions asked play golf (Experiment 1) darts (Experiment 2). found participants manipulated feel powerful performed substantially better control condition. study finding , Cusack et al. (2015) conducted five replications United States. Across replications tried different ways manipulating power, different types tasks (golf, mirror tracing, cognitive task), different levels difficulty, different types participant pools (undergraduates online). Summary data seven studies available PowerPerformance ma.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_powerperformance_ma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PowerPerformance ma - Ch9 - from Burgmer and Englich (2012), and Cusack et al. (2015) — data_powerperformance_ma","text":"","code":"data_powerperformance_ma"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_powerperformance_ma.html","id":"data-powerperformance-ma","dir":"Reference","previous_headings":"","what":"data_powerperformance_ma","title":"PowerPerformance ma - Ch9 - from Burgmer and Englich (2012), and Cusack et al. (2015) — data_powerperformance_ma","text":"data frame 8 rows 12 columns: StudyName factor Country factor Population factor Difficulty factor Task factor M Control numeric s Control numeric M Power numeric s Power numeric Cohen d unb numeric n Control integer n Power integer","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_powerperformance_ma.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"PowerPerformance ma - Ch9 - from Burgmer and Englich (2012), and Cusack et al. (2015) — data_powerperformance_ma","text":"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0140806","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_rattanmotivation.html","id":null,"dir":"Reference","previous_headings":"","what":"RattanMotivation - Ch14 - from Rattan et al. (2012) — data_rattanmotivation","title":"RattanMotivation - Ch14 - from Rattan et al. (2012) — data_rattanmotivation","text":"think react feedback gave encouragement reassurance, , instead, encouragement challenge? Carol Dweck colleagues investigated many questions people respond different types feedback. next example comes Dweck's research group illustrates data analysis starts full data, rather summary statistics. Rattan et al. (2012) asked college student participants imagine undertaking mathematics course just received low score (65%) first test year. Participants assigned randomly three groups, received different feedback along low score. Comfort group received positive encouragement also reassurance, Challenge group received positive encouragement also challenge, Control group received just positive encouragement. Participants responded range questions felt course professor. discuss data ratings motivation toward mathematics, made received feedback.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_rattanmotivation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"RattanMotivation - Ch14 - from Rattan et al. (2012) — data_rattanmotivation","text":"","code":"data_rattanmotivation"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_rattanmotivation.html","id":"data-rattanmotivation","dir":"Reference","previous_headings":"","what":"data_rattanmotivation","title":"RattanMotivation - Ch14 - from Rattan et al. (2012) — data_rattanmotivation","text":"data frame 54 rows 2 columns: Group factor Motivation numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_rattanmotivation.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"RattanMotivation - Ch14 - from Rattan et al. (2012) — data_rattanmotivation","text":"https://www.sciencedirect.com/science/article/pii/S0022103111003027","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_religionsharing.html","id":null,"dir":"Reference","previous_headings":"","what":"ReligionSharing - Ch14 - for End-of-Chapter Exercise 14.3 — data_religionsharing","title":"ReligionSharing - Ch14 - for End-of-Chapter Exercise 14.3 — data_religionsharing","text":"extent religious upbringing related prosocial behavior childhood? investigate, large international sample children asked play game given 10 stickers asked give stickers away another child able tested day. number stickers donated considered measure altruistic sharing. addition, parents child reported family's religion. Summary data provided.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_religionsharing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"ReligionSharing - Ch14 - for End-of-Chapter Exercise 14.3 — data_religionsharing","text":"","code":"data_religionsharing"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_religionsharing.html","id":"data-religionsharing","dir":"Reference","previous_headings":"","what":"data_religionsharing","title":"ReligionSharing - Ch14 - for End-of-Chapter Exercise 14.3 — data_religionsharing","text":"data frame 3 rows 4 columns: Group factor Mean numeric SD numeric N integer","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_religious_belief.html","id":null,"dir":"Reference","previous_headings":"","what":"Religious belief - Ch03 - for End-of-Chapter Exercise 3.5 — data_religious_belief","title":"Religious belief - Ch03 - for End-of-Chapter Exercise 3.5 — data_religious_belief","text":"look data religious beliefs. Religious belief file data large online survey participants asked report, scale 0 100, belief existence God. Age also reported.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_religious_belief.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Religious belief - Ch03 - for End-of-Chapter Exercise 3.5 — data_religious_belief","text":"","code":"data_religious_belief"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_religious_belief.html","id":"data-religious-belief","dir":"Reference","previous_headings":"","what":"data_religious_belief","title":"Religious belief - Ch03 - for End-of-Chapter Exercise 3.5 — data_religious_belief","text":"data frame 213 rows 3 columns: Response_ID character Belief_in_God factor Age integer","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_selfexplain.html","id":null,"dir":"Reference","previous_headings":"","what":"SelfExplain - Ch15 - from McEldoon et al. (2013) — data_selfexplain","title":"SelfExplain - Ch15 - from McEldoon et al. (2013) — data_selfexplain","text":"Self-explaining learning strategy students write say explanations material studying. Self-explaining generally found effective standard studying, may also take time. raises question whether study strategy extra time benefits learning. explore issue, grade school children took pretest mathematics conceptual knowledge, studied mathematics problems, took similar posttest (McEldoon et al., 2013). Participants randomly assigned one two study conditions: normal study + practice (Practice group), self-explaining (Self-Explain group). first condition intended make time spent learning similar two groups. can find part data study SelfExplain, scores percent correct.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_selfexplain.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SelfExplain - Ch15 - from McEldoon et al. (2013) — data_selfexplain","text":"","code":"data_selfexplain"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_selfexplain.html","id":"data-selfexplain","dir":"Reference","previous_headings":"","what":"data_selfexplain","title":"SelfExplain - Ch15 - from McEldoon et al. (2013) — data_selfexplain","text":"data frame 52 rows 4 columns: Student ID factor Condition factor Pretest numeric Posttest numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_selfexplain.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"SelfExplain - Ch15 - from McEldoon et al. (2013) — data_selfexplain","text":"https://bpspsychub.onlinelibrary.wiley.com/doi/abs/10.1111/j.2044-8279.2012.02083.x","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_simmonscredibility.html","id":null,"dir":"Reference","previous_headings":"","what":"SimmonsCredibility - Ch14 - from Simmons and Nelson (2020) — data_simmonscredibility","title":"SimmonsCredibility - Ch14 - from Simmons and Nelson (2020) — data_simmonscredibility","text":"excited! company developed wonderful new weight-loss program, now job develop ad campaign. choose BeforeAfter pair pictures, Figure 14.1, top panel? might Progressive sequence pictures person, bottom panel, effective? Pause, think, discuss. choose, ? might think BeforeAfter simpler dramatic. hand, Progressive highlights steady improvement claim program deliver. probably surprised learn BeforeAfter used often long favorite advertising industry, whereas Progressive used rarely. Luca Cian colleagues (Cian et al., 2020) curious know extent BeforeAfter actually effective, appealing, credible Progressive, , indeed, whether Progressive might score highly. reported seven studies various aspects question. focus Study 2, used three independent groups compare three conditions illustrated Figure 14.1. BeforeAfterInfo condition, middle panel, comprises three BeforeAfter pairs, thus providing extra information endpoints. researchers included condition case advantage Progressive might stem simply images, rather illustrates clear progressive sequence. randomly assigned 213 participants MTurk one three groups. Participants asked 'imagine decided lose weight', saw one three ads weight loss program called MRMDiets. answered question 'evaluate MRMDiets?' choosing 1-7 response several scales, including Unlikeable-Likable, Ineffective-Effective, credible-Credible. researchers averaged six scores give overall Credibility score, 1-7 scale, 7 credible. Simmons Nelson (2020) sufficiently intrigued carry two substantial close replications. cooperation original researchers, used materials procedure. used much larger groups preregistered research plan, including data analysis plan. focus first replication, 761 participants MTurk randomized three groups.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_simmonscredibility.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SimmonsCredibility - Ch14 - from Simmons and Nelson (2020) — data_simmonscredibility","text":"","code":"data_simmonscredibility"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_simmonscredibility.html","id":"data-simmonscredibility","dir":"Reference","previous_headings":"","what":"data_simmonscredibility","title":"SimmonsCredibility - Ch14 - from Simmons and Nelson (2020) — data_simmonscredibility","text":"data frame 3 rows 4 columns: Groups factor Mean numeric SD numeric n integer","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_simmonscredibility.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"SimmonsCredibility - Ch14 - from Simmons and Nelson (2020) — data_simmonscredibility","text":"http://datacolada.org/94","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_sleep_beauty.html","id":null,"dir":"Reference","previous_headings":"","what":"Sleep Beauty - Ch11 - for End-of-Chapter Exercise 11.6 — data_sleep_beauty","title":"Sleep Beauty - Ch11 - for End-of-Chapter Exercise 11.6 — data_sleep_beauty","text":"really thing beauty sleep? investigate, researchers decided examine extent sleep relates attractiveness. 70 college students self-reported amount sleep night . addition, photograph taken participant rated attractiveness scale 1 10 two judges opposite gender. average rating score used. can download data set (Sleep Beauty) book website.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_sleep_beauty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sleep Beauty - Ch11 - for End-of-Chapter Exercise 11.6 — data_sleep_beauty","text":"","code":"data_sleep_beauty"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_sleep_beauty.html","id":"data-sleep-beauty","dir":"Reference","previous_headings":"","what":"data_sleep_beauty","title":"Sleep Beauty - Ch11 - for End-of-Chapter Exercise 11.6 — data_sleep_beauty","text":"data frame 70 rows 2 columns: Sleep (hours) numeric Rated_Attractiveness numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_smithrecall.html","id":null,"dir":"Reference","previous_headings":"","what":"SmithRecall - Ch15 - from Smith et al. (2016) — data_smithrecall","title":"SmithRecall - Ch15 - from Smith et al. (2016) — data_smithrecall","text":"SmithRecall - Ch15 - Smith et al. (2016)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_smithrecall.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"SmithRecall - Ch15 - from Smith et al. (2016) — data_smithrecall","text":"","code":"data_smithrecall"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_smithrecall.html","id":"data-smithrecall","dir":"Reference","previous_headings":"","what":"data_smithrecall","title":"SmithRecall - Ch15 - from Smith et al. (2016) — data_smithrecall","text":"data frame 120 rows 6 columns: ID integer Study Technique factor Stress Status factor %Recalled numeric Items Recalled numeric Group factor","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_stickgold.html","id":null,"dir":"Reference","previous_headings":"","what":"Stickgold - Ch06 - from Stickgold et al. (2000) — data_stickgold","title":"Stickgold - Ch06 - from Stickgold et al. (2000) — data_stickgold","text":"Stickgold et al. (2000) found , remarkably, performance visual discrimination task actually improved 48-96 hours initial training, even without practice time. However, participants sleep deprived period? trained 11 participants new skill, sleep deprived. data (-14.7, -10.7, -10.7, 2.2, 2.4, 4.5, 7.2, 9.6, 10, 21.3, 21.8)-download Stickgold data set book website. data changes performance scores immediately training night without sleep: 0 represents change, positive scores represent improvement, negative scores represent decline. Data set courtesy DataCrunch (tiny.cc/Stickgold)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_stickgold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Stickgold - Ch06 - from Stickgold et al. (2000) — data_stickgold","text":"","code":"data_stickgold"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_stickgold.html","id":"data-stickgold","dir":"Reference","previous_headings":"","what":"data_stickgold","title":"Stickgold - Ch06 - from Stickgold et al. (2000) — data_stickgold","text":"data frame 11 rows 3 columns: Sleep deprived numeric B factor C factor","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_stickgold.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Stickgold - Ch06 - from Stickgold et al. (2000) — data_stickgold","text":"https://www.statcrunch.com/app/index.html?dataid=1053539","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_studystrategies.html","id":null,"dir":"Reference","previous_headings":"","what":"StudyStrategies - Ch14 - from O'Reilly et al. (1998) — data_studystrategies","title":"StudyStrategies - Ch14 - from O'Reilly et al. (1998) — data_studystrategies","text":"extent study strategy influence learning? investigate, psychology students randomly assigned three groups asked learn biology facts using one three different strategies: ) Self-Explain (explaining fact new knowledge gained relates already known), b) Elab Interrogation (elaborative interrogation: stating fact makes sense), c) Repetition Control (stating fact ). studying, students took 25-point fill--blank test (O'Reilly et al., 1998)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_studystrategies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"StudyStrategies - Ch14 - from O'Reilly et al. (1998) — data_studystrategies","text":"","code":"data_studystrategies"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_studystrategies.html","id":"data-studystrategies","dir":"Reference","previous_headings":"","what":"data_studystrategies","title":"StudyStrategies - Ch14 - from O'Reilly et al. (1998) — data_studystrategies","text":"data frame 3 rows 10 columns: Group factor TestMean numeric TestSD numeric TestN integer PrevKnowMean numeric PrevKnowSD numeric PrevKnowN integer EaseUseMean numeric EaseUseSD numeric EaseUseN integer","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_studystrategies.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"StudyStrategies - Ch14 - from O'Reilly et al. (1998) — data_studystrategies","text":"https://doi.org/10.1006/ceps.1997.0977","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_thomason1.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample data for estimate_mdiff_paired: LSAT scores from Thomason (2014) — data_thomason1","title":"Sample data for estimate_mdiff_paired: LSAT scores from Thomason (2014) — data_thomason1","text":"Thomason et al. (2014) reported 7 studies evaluating promising approach teaching critical thinking combines argument mapping form mastery learning (Mazur, 1997). Studies conducted United States, Canada, United Kingdom, study used single group students. Students tested various well-established measures critical thinking, (Pretest) (Posttest) training. Group sizes ranged 7 39. Thomason studies compared two conditions, Pretest Posttest, within participants, therefore used paired design. dataset first Thomason study, Thomason 1. used group N = 12 students, whose critical- thinking ability assessed Pretest Posttest using Logical Reasoning section Law School Aptitude Test (LSAT).","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_thomason1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample data for estimate_mdiff_paired: LSAT scores from Thomason (2014) — data_thomason1","text":"","code":"data_thomason1"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_thomason1.html","id":"data-thomason-a-data-frame-with-rows-and-columns-","dir":"Reference","previous_headings":"","what":"data_thomason1 A data frame with 12 rows and 2 columns:","title":"Sample data for estimate_mdiff_paired: LSAT scores from Thomason (2014) — data_thomason1","text":"pretest LSAT score argument mapping mastery training posttest LSAT score argument mapping mastery training","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_thomason1.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Sample data for estimate_mdiff_paired: LSAT scores from Thomason (2014) — data_thomason1","text":"Thomason, N. R., Adajian, T., Barnett, . E., Boucher, S., van der Brugge, E., Campbell, J., Knorpp, W., Lempert, R., Lengbeyer, L., Mandel, D. R., Rider, Y., van Gelder, T., & Wilkins, J. (2014). Critical thinking final report. University Melbourne, N66001-12-C-2004.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_thomason_1.html","id":null,"dir":"Reference","previous_headings":"","what":"Thomason 1 - Ch11 - from Thomason 1 — data_thomason_1","title":"Thomason 1 - Ch11 - from Thomason 1 — data_thomason_1","text":"Summary data unpublished study Neil Thomason colleagues, interested ways enhance students' critical thinking. investigating argument mapping, promising way use diagrams represent structure arguments. Students study completed established test critical thinking (Pretest), critical thinking course based argument mapping, second version test (Posttest).","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_thomason_1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Thomason 1 - Ch11 - from Thomason 1 — data_thomason_1","text":"","code":"data_thomason_1"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_thomason_1.html","id":"data-thomason-","dir":"Reference","previous_headings":"","what":"data_thomason_1","title":"Thomason 1 - Ch11 - from Thomason 1 — data_thomason_1","text":"data frame 12 rows 3 columns: Participant ID factor Pretest numeric Posttest numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_videogameaggression.html","id":null,"dir":"Reference","previous_headings":"","what":"VideogameAggression - Ch15 - from Hilgard (2015) — data_videogameaggression","title":"VideogameAggression - Ch15 - from Hilgard (2015) — data_videogameaggression","text":"Video games can violent can also challenging. extent might factors cause aggressive behavior? explore, Hilgard (2015) asked male participants play one four versions video game 15 minutes. game customized vary violence (shooting zombies helping aliens) difficulty (targets controlled tough AI dumb AI). game, players provoked given insulting evaluation confederate. Participants got decide long confederate hold hand painfully cold ice water (0 80 seconds), taken measure aggressive behavior. can find materials analysis plan study Open Science Framework: osf. io/cwenz. simplified version full data set.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_videogameaggression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"VideogameAggression - Ch15 - from Hilgard (2015) — data_videogameaggression","text":"","code":"data_videogameaggression"},{"path":[]},{"path":"https://rcalinjageman.github.io/esci/reference/data_videogameaggression.html","id":"data-videogameaggression","dir":"Reference","previous_headings":"","what":"data_videogameaggression","title":"VideogameAggression - Ch15 - from Hilgard (2015) — data_videogameaggression","text":"data frame 223 rows 3 columns: Violence factor Difficulty factor Agression numeric","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/data_videogameaggression.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"VideogameAggression - Ch15 - from Hilgard (2015) — data_videogameaggression","text":"https://journals.sagepub.com/doi/10.1177/0956797619829688","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/esci_document_data.html","id":null,"dir":"Reference","previous_headings":"","what":"also promote kind, altruistic behavior. Brethel-Haurwitz and Marsh (2014)\r\nexamined this idea by collecting data on U.S. states. A Gallup poll in 2010\r\nwas used to measure each state’s well-being index, a measure of mean\r\nhappiness for the state’s residents on a scale from 0 to 100. Next, a kidney\r\ndonation database for 1999–2010 was used to figure out each state’s rate\r\n(number of donations per 1 million people) of non-directed kidney\r\ndonations—that’s giving one kidney to a stranger, an extremely generous and\r\naltruistic thing to do!\r\ntheir classic study, Jacowitz and Kahneman (1995) asked participants to\r\nestimate how many babies are born each day in the United States. Participants\r\nwere given either a low anchor (“more than 100 babies/day”) or a high anchor\r\n(“less than 50,000 babies/day”). Those who saw the low anchor estimated many\r\nfewer births/day than those who saw the high anchor, which suggests that the\r\nwording can have a profound influence. The correct answer, as it happens, is\r\n~11,000 births/day in 2014. To investigate the extent that these results are\r\nreplicable, the Many Labs project repeated this classic study at many\r\ndifferent labs around the world. You can find the summary data for 30 of\r\nthese labs in the Anchor Estimate ma data file\r\nnumerous other issues can be highly damaging, but are thriving in this social\r\nmedia age. Trying to debunk a conspiracy theory by presenting facts and\r\nevidence often doesn’t work, alas. Psychological inoculation, also similar to\r\nprebunking, presents a mild form of misinformation, preferably with\r\nexplanation, in the hope of building resistance to real-life fake news—a sort\r\nof vaccine for fake news. The Bad News game is a spin-off from research on\r\npsychological inoculation. Basol et al. (2020) assessed the possible\r\neffectiveness of this game as a fake news vaccine. At getbadnews.com you can\r\nclick “About” for information, or just start playing the game— it’s easy and\r\nmaybe even fun. You encounter mock Twitter (now X) fake news messages that\r\nillustrate common strategies for making fake news memorable or believable.\r\nYou make choices between messages and decide which ones to “forward” as you\r\ntry to spread fake news while building your credibility score and number of\r\n“followers”—rather like real life for a conspiracy theorist wanting to spread\r\nthe word. Compete with your friends for credibility and number of followers.\r\nBasol’s online participants first saw 18 fictitious fake news tweets and\r\nrated each for reliability (accuracy, believability), and also rated their\r\nconfidence in that reliability rating. Both ratings were on a 1 to 7 scale.\r\nThose in the BadNews group then played the game for 15 minutes, whereas those\r\nin the Control group played Tetris. Then all once again gave reliability and\r\nconfidence ratings for the 18 tweets.\r\ndecade earlier, had been one of several outside experts invited to scrutinize\r\nthe laboratory and experimental procedures of parapsychology researcher\r\nCharles Honorton. Bem not only judged them adequate, but joined the research\r\neffort and became a coauthor with Honorton. Bem and Honorton (1994) first\r\nreviewed early ganzfeld studies and described how the experimental procedure\r\nhad been improved to reduce the chance that results could be influenced by\r\nvarious possible biases, or leakages of information from sender to receiver.\r\nFor example, the randomization procedure was carried out automatically by\r\ncomputer, and all stimuli were presented under computer control. Bem and\r\nHonorton then presented data from studies conducted with the improved\r\nprocedure. Table 13.1 presents basic data from 10 studies reported by Bem and\r\nHonorton (1994). Participants each made a single judgment, so in Pilot 1, for\r\nexample, 22 participants responded, with 8 of them giving a correct response.\r\nThree pilot studies helped refine the procedures, then four studies used\r\nnovice receivers. Study 5 used 20 students of music, drama, or dance as\r\nreceivers, in response to suggestions that creative people might be more\r\nlikely to show telepathy. Studies 6 and 7 used receivers who had participated\r\nin an earlier study. The proportion of hits expected by chance is .25, and\r\nTable 13.1 shows that all but Study 1 found proportions higher than .25.\r\nwho identified as female. Data is Subjective Wellbeing abd Body Satisfaction.\r\nwho identified as male. Data is Subjective Wellbeing abd Body Satisfaction.\r\nReported are measures of Subjective Wellbeing abd Body Satisfaction. — esci_document_data","title":"also promote kind, altruistic behavior. Brethel-Haurwitz and Marsh (2014)\r\nexamined this idea by collecting data on U.S. states. A Gallup poll in 2010\r\nwas used to measure each state’s well-being index, a measure of mean\r\nhappiness for the state’s residents on a scale from 0 to 100. Next, a kidney\r\ndonation database for 1999–2010 was used to figure out each state’s rate\r\n(number of donations per 1 million people) of non-directed kidney\r\ndonations—that’s giving one kidney to a stranger, an extremely generous and\r\naltruistic thing to do!\r\ntheir classic study, Jacowitz and Kahneman (1995) asked participants to\r\nestimate how many babies are born each day in the United States. Participants\r\nwere given either a low anchor (“more than 100 babies/day”) or a high anchor\r\n(“less than 50,000 babies/day”). Those who saw the low anchor estimated many\r\nfewer births/day than those who saw the high anchor, which suggests that the\r\nwording can have a profound influence. The correct answer, as it happens, is\r\n~11,000 births/day in 2014. To investigate the extent that these results are\r\nreplicable, the Many Labs project repeated this classic study at many\r\ndifferent labs around the world. You can find the summary data for 30 of\r\nthese labs in the Anchor Estimate ma data file\r\nnumerous other issues can be highly damaging, but are thriving in this social\r\nmedia age. Trying to debunk a conspiracy theory by presenting facts and\r\nevidence often doesn’t work, alas. Psychological inoculation, also similar to\r\nprebunking, presents a mild form of misinformation, preferably with\r\nexplanation, in the hope of building resistance to real-life fake news—a sort\r\nof vaccine for fake news. The Bad News game is a spin-off from research on\r\npsychological inoculation. Basol et al. (2020) assessed the possible\r\neffectiveness of this game as a fake news vaccine. At getbadnews.com you can\r\nclick “About” for information, or just start playing the game— it’s easy and\r\nmaybe even fun. You encounter mock Twitter (now X) fake news messages that\r\nillustrate common strategies for making fake news memorable or believable.\r\nYou make choices between messages and decide which ones to “forward” as you\r\ntry to spread fake news while building your credibility score and number of\r\n“followers”—rather like real life for a conspiracy theorist wanting to spread\r\nthe word. Compete with your friends for credibility and number of followers.\r\nBasol’s online participants first saw 18 fictitious fake news tweets and\r\nrated each for reliability (accuracy, believability), and also rated their\r\nconfidence in that reliability rating. Both ratings were on a 1 to 7 scale.\r\nThose in the BadNews group then played the game for 15 minutes, whereas those\r\nin the Control group played Tetris. Then all once again gave reliability and\r\nconfidence ratings for the 18 tweets.\r\ndecade earlier, had been one of several outside experts invited to scrutinize\r\nthe laboratory and experimental procedures of parapsychology researcher\r\nCharles Honorton. Bem not only judged them adequate, but joined the research\r\neffort and became a coauthor with Honorton. Bem and Honorton (1994) first\r\nreviewed early ganzfeld studies and described how the experimental procedure\r\nhad been improved to reduce the chance that results could be influenced by\r\nvarious possible biases, or leakages of information from sender to receiver.\r\nFor example, the randomization procedure was carried out automatically by\r\ncomputer, and all stimuli were presented under computer control. Bem and\r\nHonorton then presented data from studies conducted with the improved\r\nprocedure. Table 13.1 presents basic data from 10 studies reported by Bem and\r\nHonorton (1994). Participants each made a single judgment, so in Pilot 1, for\r\nexample, 22 participants responded, with 8 of them giving a correct response.\r\nThree pilot studies helped refine the procedures, then four studies used\r\nnovice receivers. Study 5 used 20 students of music, drama, or dance as\r\nreceivers, in response to suggestions that creative people might be more\r\nlikely to show telepathy. Studies 6 and 7 used receivers who had participated\r\nin an earlier study. The proportion of hits expected by chance is .25, and\r\nTable 13.1 shows that all but Study 1 found proportions higher than .25.\r\nwho identified as female. Data is Subjective Wellbeing abd Body Satisfaction.\r\nwho identified as male. Data is Subjective Wellbeing abd Body Satisfaction.\r\nReported are measures of Subjective Wellbeing abd Body Satisfaction. — esci_document_data","text":"also promote kind, altruistic behavior. Brethel-Haurwitz Marsh (2014) examined idea collecting data U.S. states. Gallup poll 2010 used measure state’s well-index, measure mean happiness state’s residents scale 0 100. Next, kidney donation database 1999–2010 used figure state’s rate (number donations per 1 million people) non-directed kidney donations—’s giving one kidney stranger, extremely generous altruistic thing ! classic study, Jacowitz Kahneman (1995) asked participants estimate many babies born day United States. Participants given either low anchor (“100 babies/day”) high anchor (“less 50,000 babies/day”). saw low anchor estimated many fewer births/day saw high anchor, suggests wording can profound influence. correct answer, happens, ~11,000 births/day 2014. investigate extent results replicable, Many Labs project repeated classic study many different labs around world. can find summary data 30 labs Anchor Estimate ma data file numerous issues can highly damaging, thriving social media age. Trying debunk conspiracy theory presenting facts evidence often doesn’t work, alas. Psychological inoculation, also similar prebunking, presents mild form misinformation, preferably explanation, hope building resistance real-life fake news—sort vaccine fake news. Bad News game spin-research psychological inoculation. Basol et al. (2020) assessed possible effectiveness game fake news vaccine. getbadnews.com can click “” information, just start playing game— ’s easy maybe even fun. encounter mock Twitter (now X) fake news messages illustrate common strategies making fake news memorable believable. make choices messages decide ones “forward” try spread fake news building credibility score number “followers”—rather like real life conspiracy theorist wanting spread word. Compete friends credibility number followers. Basol’s online participants first saw 18 fictitious fake news tweets rated reliability (accuracy, believability), also rated confidence reliability rating. ratings 1 7 scale. BadNews group played game 15 minutes, whereas Control group played Tetris. gave reliability confidence ratings 18 tweets. decade earlier, one several outside experts invited scrutinize laboratory experimental procedures parapsychology researcher Charles Honorton. Bem judged adequate, joined research effort became coauthor Honorton. Bem Honorton (1994) first reviewed early ganzfeld studies described experimental procedure improved reduce chance results influenced various possible biases, leakages information sender receiver. example, randomization procedure carried automatically computer, stimuli presented computer control. Bem Honorton presented data studies conducted improved procedure. Table 13.1 presents basic data 10 studies reported Bem Honorton (1994). Participants made single judgment, Pilot 1, example, 22 participants responded, 8 giving correct response. Three pilot studies helped refine procedures, four studies used novice receivers. Study 5 used 20 students music, drama, dance receivers, response suggestions creative people might likely show telepathy. Studies 6 7 used receivers participated earlier study. proportion hits expected chance .25, Table 13.1 shows Study 1 found proportions higher .25. identified female. Data Subjective Wellbeing abd Body Satisfaction. identified male. Data Subjective Wellbeing abd Body Satisfaction. Reported measures Subjective Wellbeing abd Body Satisfaction.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/esci_document_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"also promote kind, altruistic behavior. Brethel-Haurwitz and Marsh (2014)\r\nexamined this idea by collecting data on U.S. states. A Gallup poll in 2010\r\nwas used to measure each state’s well-being index, a measure of mean\r\nhappiness for the state’s residents on a scale from 0 to 100. Next, a kidney\r\ndonation database for 1999–2010 was used to figure out each state’s rate\r\n(number of donations per 1 million people) of non-directed kidney\r\ndonations—that’s giving one kidney to a stranger, an extremely generous and\r\naltruistic thing to do!\r\ntheir classic study, Jacowitz and Kahneman (1995) asked participants to\r\nestimate how many babies are born each day in the United States. Participants\r\nwere given either a low anchor (“more than 100 babies/day”) or a high anchor\r\n(“less than 50,000 babies/day”). Those who saw the low anchor estimated many\r\nfewer births/day than those who saw the high anchor, which suggests that the\r\nwording can have a profound influence. The correct answer, as it happens, is\r\n~11,000 births/day in 2014. To investigate the extent that these results are\r\nreplicable, the Many Labs project repeated this classic study at many\r\ndifferent labs around the world. You can find the summary data for 30 of\r\nthese labs in the Anchor Estimate ma data file\r\nnumerous other issues can be highly damaging, but are thriving in this social\r\nmedia age. Trying to debunk a conspiracy theory by presenting facts and\r\nevidence often doesn’t work, alas. Psychological inoculation, also similar to\r\nprebunking, presents a mild form of misinformation, preferably with\r\nexplanation, in the hope of building resistance to real-life fake news—a sort\r\nof vaccine for fake news. The Bad News game is a spin-off from research on\r\npsychological inoculation. Basol et al. (2020) assessed the possible\r\neffectiveness of this game as a fake news vaccine. At getbadnews.com you can\r\nclick “About” for information, or just start playing the game— it’s easy and\r\nmaybe even fun. You encounter mock Twitter (now X) fake news messages that\r\nillustrate common strategies for making fake news memorable or believable.\r\nYou make choices between messages and decide which ones to “forward” as you\r\ntry to spread fake news while building your credibility score and number of\r\n“followers”—rather like real life for a conspiracy theorist wanting to spread\r\nthe word. Compete with your friends for credibility and number of followers.\r\nBasol’s online participants first saw 18 fictitious fake news tweets and\r\nrated each for reliability (accuracy, believability), and also rated their\r\nconfidence in that reliability rating. Both ratings were on a 1 to 7 scale.\r\nThose in the BadNews group then played the game for 15 minutes, whereas those\r\nin the Control group played Tetris. Then all once again gave reliability and\r\nconfidence ratings for the 18 tweets.\r\ndecade earlier, had been one of several outside experts invited to scrutinize\r\nthe laboratory and experimental procedures of parapsychology researcher\r\nCharles Honorton. Bem not only judged them adequate, but joined the research\r\neffort and became a coauthor with Honorton. Bem and Honorton (1994) first\r\nreviewed early ganzfeld studies and described how the experimental procedure\r\nhad been improved to reduce the chance that results could be influenced by\r\nvarious possible biases, or leakages of information from sender to receiver.\r\nFor example, the randomization procedure was carried out automatically by\r\ncomputer, and all stimuli were presented under computer control. Bem and\r\nHonorton then presented data from studies conducted with the improved\r\nprocedure. Table 13.1 presents basic data from 10 studies reported by Bem and\r\nHonorton (1994). Participants each made a single judgment, so in Pilot 1, for\r\nexample, 22 participants responded, with 8 of them giving a correct response.\r\nThree pilot studies helped refine the procedures, then four studies used\r\nnovice receivers. Study 5 used 20 students of music, drama, or dance as\r\nreceivers, in response to suggestions that creative people might be more\r\nlikely to show telepathy. Studies 6 and 7 used receivers who had participated\r\nin an earlier study. The proportion of hits expected by chance is .25, and\r\nTable 13.1 shows that all but Study 1 found proportions higher than .25.\r\nwho identified as female. Data is Subjective Wellbeing abd Body Satisfaction.\r\nwho identified as male. Data is Subjective Wellbeing abd Body Satisfaction.\r\nReported are measures of Subjective Wellbeing abd Body Satisfaction. — esci_document_data","text":"","code":"esci_document_data(save_files = FALSE)"},{"path":"https://rcalinjageman.github.io/esci/reference/esci_plot_difference_axis_x.html","id":null,"dir":"Reference","previous_headings":"","what":"Add a difference axis to the x axis of an esci forest plot — esci_plot_difference_axis_x","title":"Add a difference axis to the x axis of an esci forest plot — esci_plot_difference_axis_x","text":"esci_plot_difference_axis_x can used redraw difference axis forest plot created plot_meta. must pass plot returned plot_meta effect size table containing estimated difference.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/esci_plot_difference_axis_x.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add a difference axis to the x axis of an esci forest plot — esci_plot_difference_axis_x","text":"","code":"esci_plot_difference_axis_x( myplot, difference_table, dlim = c(NA, NA), d_n.breaks = NULL, d_lab = NULL )"},{"path":"https://rcalinjageman.github.io/esci/reference/esci_plot_difference_axis_x.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add a difference axis to the x axis of an esci forest plot — esci_plot_difference_axis_x","text":"myplot required ggplot2 plot returned plot_meta function difference_table required data frame esci-estimate difference-based effect size dlim Optional 2-item vector provide lower uppper boundaries difference axis. Defaults c(NA, NA) auto-set boundaries. d_n.breaks Optional numeric > 2 suggest number breaks difference axis; defaults NULL case number breaks handled automatically ggplot d_lab Optional character serve label difference axis; defaults NULL","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_correlation.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_correlation","title":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_correlation","text":"estimate_correlation suitable design two continuous variables. estimates linear correlation two variables (Pearson's r). can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_correlation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_correlation","text":"","code":"estimate_correlation( data = NULL, x = NULL, y = NULL, r = NULL, n = NULL, x_variable_name = \"My x variable\", y_variable_name = \"My y variable\", conf_level = 0.95, save_raw_data = TRUE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_correlation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_correlation","text":"data raw data - data frame tibble x raw data - column name outcome variable, vector numeric data y raw data - column name outcome variable, vector numeric data r summary data - pearson's r correlation coefficient n summary data - Sample size, integer > 0 x_variable_name Optional friendly name x variable. Defaults 'x variable' outcome variable column name data frame passed. y_variable_name Optional friendly name y variable. Defaults 'y variable' outcome variable column name data frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. save_raw_data raw data; defaults TRUE; set FALSE save memory returning raw data estimate object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_correlation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_correlation","text":"Returns object class esci_estimate","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_correlation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_correlation","text":"generate estimate function, can visualize plot_correlation() can test hypotheses test_correlation(). addition, can use plot_scatter() visualize raw data conduct regression analysis returns predicted Y' values given X value. estimated correlation statpsych::ci.cor(), uses Fisher r--z approach.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_magnitude.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a continuous variable with no grouping (single-group design) — estimate_magnitude","title":"Estimates for a continuous variable with no grouping (single-group design) — estimate_magnitude","text":"estimate_magnitude suitable single group design continuous outcome variable. estimates population mean population median (raw data ) confidence intervals. can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_magnitude.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a continuous variable with no grouping (single-group design) — estimate_magnitude","text":"","code":"estimate_magnitude( data = NULL, outcome_variable = NULL, mean = NULL, sd = NULL, n = NULL, outcome_variable_name = \"My outcome variable\", conf_level = 0.95, save_raw_data = TRUE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_magnitude.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a continuous variable with no grouping (single-group design) — estimate_magnitude","text":"data raw data - data frame tibble outcome_variable raw data - column name outcome variable, vector numeric data mean summary data - numeric representing mean outcome variable sd summary data - numeric > 0, standard deviation outcome variable n summary data - integer > 0, sample size outcome variable outcome_variable_name Optional friendly name outcome variable. Defaults 'outcome variable' outcome variable column name data frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. save_raw_data raw data; defaults TRUE; set FALSE save memory returning raw data estimate object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_magnitude.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a continuous variable with no grouping (single-group design) — estimate_magnitude","text":"Returns object class esci_estimate overview outcome_variable_name - mean - mean_LL - mean_UL - median - median_LL - median_UL - sd - min - max - q1 - q3 - n - missing - df - mean_SE - median_SE - es_mean outcome_variable_name - effect - effect_size - LL - UL - SE - df - ta_LL - ta_UL - es_median outcome_variable_name - effect - effect_size - LL - UL - SE - df - ta_LL - ta_UL - raw_data grouping_variable - outcome_variable -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_magnitude.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a continuous variable with no grouping (single-group design) — estimate_magnitude","text":"Reach function place one-sample t-test z-test. generate estimate function, can visualize plot_magnitude(). want compare sample known value reference, use estimate_mdiff_one(). estimated mean statpsych::ci.mean1(). estimated median statpsych::ci.median1()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_magnitude.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates for a continuous variable with no grouping (single-group design) — estimate_magnitude","text":"","code":"# From raw data data(\"data_penlaptop1\") estimate <- esci::estimate_magnitude( data = data_penlaptop1[data_penlaptop1$condition == \"Pen\", ], outcome_variable = transcription ) estimate #> Analysis of raw data: #> Data frame = data #> Outcome variable(s) = transcription #> #> ---Overview--- #> outcome_variable_name mean mean_LL mean_UL median median_LL median_UL #> 1 transcription 8.811765 7.154642 10.46889 8.6 6.5 11.1 #> sd min max q1 q3 n missing df mean_SE median_SE #> 1 4.749339 1 20.1 5.2 11.275 34 0 33 0.8145049 1.021201 #> #> -- es_mean -- #> outcome_variable_name effect effect_size LL UL SE #> 1 transcription transcription 8.811765 7.154642 10.46889 0.8145049 #> df ta_LL ta_UL #> 1 33 7.746606 9.876923 #> #> -- es_median -- #> outcome_variable_name effect effect_size LL UL SE df ta_LL #> 1 transcription transcription 8.6 6.5 11.1 1.021201 33 7.1 #> ta_UL #> 1 10.8 #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimate plot_magnitude(estimate) } # From summary data mymean <- 24.5 mysd <- 3.65 myn <- 40 estimate <- esci::estimate_magnitude( mean = mymean, sd = mysd, n = myn ) estimate #> Analysis of raw data: #> Outcome variable(s) = My outcome variable #> #> ---Overview--- #> outcome_variable_name mean mean_LL mean_UL sd n df mean_SE #> 1 My outcome variable 24.5 23.33267 25.66733 3.65 40 39 0.5771157 #> #> -- es_mean -- #> outcome_variable_name effect effect_size LL UL #> 1 My outcome variable My outcome variable 24.5 23.33267 25.66733 #> SE df ta_LL ta_UL #> 1 0.5771157 39 23.74765 25.25235 #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimate plot_mdiff(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_between.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a 2x2 between-subjects design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_between","title":"Estimates for a 2x2 between-subjects design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_between","text":"estimate_mdiff_2x2_between suitable 2x2 -subjects design continuous outcome variable. estimates main effect, simple effects first factor, interaction. can express estimates mean differences, standardized mean differences (Cohen's d), median differences (raw data ). can pass raw data summary data (summary data return medians).","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_between.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a 2x2 between-subjects design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_between","text":"","code":"estimate_mdiff_2x2_between( data = NULL, outcome_variable = NULL, grouping_variable_A = NULL, grouping_variable_B = NULL, means = NULL, sds = NULL, ns = NULL, grouping_variable_A_levels = NULL, grouping_variable_B_levels = NULL, outcome_variable_name = \"My outcome variable\", grouping_variable_A_name = \"A\", grouping_variable_B_name = \"A\", conf_level = 0.95, assume_equal_variance = FALSE, save_raw_data = TRUE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_between.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a 2x2 between-subjects design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_between","text":"data raw data - data frame tibble outcome_variable raw data - column name outcome variable, vector numeric data grouping_variable_A raw data - column name grouping variable, vector group names, 2 levels allowed grouping_variable_B raw data - column name grouping variable, vector group names, 2 levels allowed means summary data - vector 4 means: A1B1, A1B2, A2B1, A2B2 sds summary data - vector 4 standard deviations, order ns summary data - vector 4 sample sizes grouping_variable_A_levels summary data - optional vector 2 group labels grouping_variable_B_levels summary data - optional vector 2 group labels outcome_variable_name Optional friendly name outcome variable. Defaults 'outcome variable' outcome variable column name data frame passed. grouping_variable_A_name Optional friendly name grouping variable. Defaults '' grouping variable column name data.frame passed. grouping_variable_B_name Optional friendly name grouping variable. Defaults '' grouping variable column name data.frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. assume_equal_variance Defaults FALSE save_raw_data raw data; defaults TRUE; set FALSE save memory returning raw data estimate object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_between.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a 2x2 between-subjects design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_between","text":"Returns object class esci_estimate es_mean_difference type - outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - SE - df - ta_LL - ta_UL - effect_type - effects_complex - es_median_difference type - outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - SE - ta_LL - ta_UL - effect_type - effects_complex - es_smd outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - numerator - denominator - SE - df - d_biased - effect_type - effects_complex - overview outcome_variable_name - grouping_variable_name - grouping_variable_level - mean - mean_LL - mean_UL - median - median_LL - median_UL - sd - min - max - q1 - q3 - n - missing - df - mean_SE - median_SE - raw_data grouping_variable - outcome_variable - grouping_variable_A - grouping_variable_B -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_between.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a 2x2 between-subjects design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_between","text":"Reach function place 2x2 -subjects ANOVA. generate estimate function, can visualize plot_mdiff() can visualize interaction specifically plot_interaction(). can test hypotheses test_mdiff(). estimated mean differences statpsych::ci.2x2.mean.bs(). estimated SMDs statpsych::ci.2x2.stdmean.bs(). estimated median differences statpsych::ci.2x2.median.bs()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_between.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates for a 2x2 between-subjects design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_between","text":"","code":"# From summary data means <- c(1.5, 1.14, 1.38, 2.22) sds <- c(1.38, .96,1.5, 1.68) ns <- c(26, 26, 25, 26) grouping_variable_A_levels <- c(\"Evening\", \"Morning\") grouping_variable_B_levels <- c(\"Sleep\", \"No Sleep\") estimates <- estimate_mdiff_2x2_between( means = means, sds = sds, ns = ns, grouping_variable_A_levels = grouping_variable_A_levels, grouping_variable_B_levels = grouping_variable_B_levels, grouping_variable_A_name = \"Testing Time\", grouping_variable_B_name = \"Rest\", outcome_variable_name = \"False Memory Score\", assume_equal_variance = TRUE ) if (FALSE) { # To visualize the estimated mean difference for the interaction plot_mdiff(estimate$interaction, effect_size = \"mean\") # To visualize the interaction as a line plot plot_interaction(estimates) # Same but with fan effect representing each simple-effect CI plot_interaction(estimates, show_CI = TRUE) }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_mixed.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a 2x2 mixed factorial design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_mixed","title":"Estimates for a 2x2 mixed factorial design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_mixed","text":"estimate_mdiff_2x2_mixed suitable 2x2 mixed-factorial design continuous outcome variable. estimates main effect, simple effects repeated-measures factor, interaction. can express estimates mean differences. function accepts raw data . Standardized mean differences (yet) available; stay tuned. Median differences also yet available.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_mixed.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a 2x2 mixed factorial design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_mixed","text":"","code":"estimate_mdiff_2x2_mixed( data, outcome_variable_level1, outcome_variable_level2, grouping_variable, outcome_variable_name = \"My outcome variable\", repeated_measures_name = \"Time\", conf_level = 0.95, save_raw_data = TRUE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_mixed.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a 2x2 mixed factorial design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_mixed","text":"data raw data - dataframe tibble outcome_variable_level1 column name outcome variable level 1 repeated-measures factor outcome_variable_level2 column name outcome variable level 2 repeated-measures factor grouping_variable column name grouping variable; 2 levels allowed; must factor outcome_variable_name Optional friendly name outcome variable. Defaults 'outcome variable' outcome variable column name data frame passed. repeated_measures_name Optional friendly name repeated measures factor. Defaults 'Time' conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. save_raw_data raw data; defaults TRUE; set FALSE save memory returning raw data estimate object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_mixed.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a 2x2 mixed factorial design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_mixed","text":"Returnsobject class esci_estimate es_mean_difference type - outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - SE - df - ta_LL - ta_UL - effect_type - effects_complex - t - p - es_smd outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - numerator - denominator - SE - df - d_biased - effect_type - effects_complex - overview outcome_variable_name - grouping_variable_name - grouping_variable_level - mean - mean_LL - mean_UL - median - median_LL - median_UL - sd - min - max - q1 - q3 - n - missing - df - mean_SE - median_SE - raw_data grouping_variable - outcome_variable - grouping_variable_A - grouping_variable_B - paired -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_mixed.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a 2x2 mixed factorial design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_mixed","text":"Reach function place 2x2 mixed-factorial ANOVA. generate estimate function, can visualize plot_mdiff() can visualize interaction specifically plot_interaction(). can test hypotheses test_mdiff(). estimated mean differences statpsych::ci.2x2.mean.mixed().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_2x2_mixed.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates for a 2x2 mixed factorial design with a continuous outcome\r\nvariable — estimate_mdiff_2x2_mixed","text":"","code":"# From raw data (summary data mode not available for this function) example_data <- data.frame( pretest = c( 19, 18, 19, 20, 17, 16, 16, 10, 12, 9, 13, 15 ), posttest = c( 18, 19, 20, 17, 20, 16, 19, 16, 16, 14, 16, 18 ), condition = as.factor( c( rep(\"Control\", times = 6), rep(\"Treated\", times = 6) ) ) ) estimates <- esci::estimate_mdiff_2x2_mixed( data = example_data, outcome_variable_level1 = pretest, outcome_variable_level2 = posttest, grouping_variable = condition, repeated_measures_name = \"Time\" ) if (FALSE) { # To visualize the estimated mean difference for the interaction plot_mdiff(estimate$interaction, effect_size = \"mean\") }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_ind_contrast.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a multi-group design with a continuous outcome\r\nvariable — estimate_mdiff_ind_contrast","title":"Estimates for a multi-group design with a continuous outcome\r\nvariable — estimate_mdiff_ind_contrast","text":"estimate_mdiff_ind_contrast suitable multi-group design (subjects) continuous outcome variable. accepts user-defined set contrast weights allows estimation 1-df contrast. can express estimates mean differences, standardized mean differences (Cohen's d) median differences (raw data ). can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_ind_contrast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a multi-group design with a continuous outcome\r\nvariable — estimate_mdiff_ind_contrast","text":"","code":"estimate_mdiff_ind_contrast( data = NULL, outcome_variable = NULL, grouping_variable = NULL, means = NULL, sds = NULL, ns = NULL, contrast = NULL, grouping_variable_levels = NULL, outcome_variable_name = \"My outcome variable\", grouping_variable_name = \"My grouping variable\", conf_level = 0.95, assume_equal_variance = FALSE, save_raw_data = TRUE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_ind_contrast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a multi-group design with a continuous outcome\r\nvariable — estimate_mdiff_ind_contrast","text":"data raw data - data frame tibble outcome_variable raw data - column name outcome variable, vector numeric data grouping_variable raw data - column name grouping variable, vector group names means summary data - vector 2 means sds summary data - vector standard deviations, length means ns summary data - vector sample sizes, length means contrast vector group weights, length number groups. grouping_variable_levels summary data - optional vector group labels, length means outcome_variable_name Optional friendly name outcome variable. Defaults 'outcome variable' outcome variable column name data frame passed. grouping_variable_name Optional friendly name grouping variable. Defaults 'grouping variable' grouping variable column name data.frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. assume_equal_variance Defaults FALSE save_raw_data raw data; defaults TRUE; set FALSE save memory returning raw data estimate object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_ind_contrast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a multi-group design with a continuous outcome\r\nvariable — estimate_mdiff_ind_contrast","text":"Returnsobject class esci_estimate es_mean_difference type - outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - SE - df - ta_LL - ta_UL - es_median_difference type - outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - SE - ta_LL - ta_UL - es_smd outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - numerator - denominator - SE - df - d_biased - overview outcome_variable_name - grouping_variable_name - grouping_variable_level - mean - mean_LL - mean_UL - median - median_LL - median_UL - sd - min - max - q1 - q3 - n - missing - df - mean_SE - median_SE - raw_data grouping_variable - outcome_variable -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_ind_contrast.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a multi-group design with a continuous outcome\r\nvariable — estimate_mdiff_ind_contrast","text":"Reach function place one-way ANOVA. generate estimate function, can visualize plot_mdiff() can test hypotheses test_mdiff(). estimated mean differences statpsych::ci.lc.mean.bs(). estimated SMDs CI_smd_ind_contrast() relies statpsych::ci.lc.stdmean.bs() unless 2 groups. estimated median differences statpsych::ci.lc.median.bs()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_ind_contrast.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates for a multi-group design with a continuous outcome\r\nvariable — estimate_mdiff_ind_contrast","text":"","code":"# From summary data data(\"data_halagappa\") estimate <- estimate_mdiff_ind_contrast( means = data_halagappa$Mean, sds = data_halagappa$SD, ns = data_halagappa$n, grouping_variable_levels = as.character(data_halagappa$Groups), assume_equal_variance = TRUE, contrast = c( \"NFree10\" = 1/3, \"AFree10\" = 1/3, \"ADiet10\" = -1/3, \"NFree17\" = -1/3, \"AFree17\" = 1/3, \"ADiet17\" = -1/3 ), grouping_variable_name = \"Diet\", outcome_variable_name = \"% time near target\" ) estimate #> Analysis of raw data: #> Outcome variable(s) = % time near target #> Grouping variable(s) = Diet #> #> ---Overview--- #> outcome_variable_name grouping_variable_name grouping_variable_level mean #> 1 % time near target Diet NFree10 37.5 #> 2 % time near target Diet AFree10 31.9 #> 3 % time near target Diet ADiet10 41.2 #> 4 % time near target Diet NFree17 33.4 #> 5 % time near target Diet AFree17 29.9 #> 6 % time near target Diet ADiet17 38.3 #> mean_LL mean_UL sd n df mean_SE #> 1 32.32519 42.67481 10.0 19 108 2.610673 #> 2 26.72519 37.07481 13.5 19 108 2.610673 #> 3 36.02519 46.37481 14.8 19 108 2.610673 #> 4 28.22519 38.57481 10.0 19 108 2.610673 #> 5 24.72519 35.07481 8.7 19 108 2.610673 #> 6 33.12519 43.47481 10.0 19 108 2.610673 #> #> -- es_mean_difference -- #> type outcome_variable_name grouping_variable_name #> 1 Comparison % time near target Diet #> 2 Reference % time near target Diet #> 3 Difference % time near target Diet #> effect #> 1 (NFree10 and AFree10 and AFree17) #> 2 (ADiet10 and NFree17 and ADiet17) #> 3 (NFree10 and AFree10 and AFree17) ‒ (ADiet10 and NFree17 and ADiet17) #> effect_size LL UL SE df ta_LL ta_UL #> 1 33.100000 30.112324 36.0876762 1.507273 108 30.599306 35.6006939 #> 2 37.633333 34.645657 40.6210095 1.507273 108 35.132639 40.1340273 #> 3 -4.533333 -8.758546 -0.3081211 2.131606 108 -8.069849 -0.9968181 #> #> -- es_smd -- #> outcome_variable_name grouping_variable_name #> 1 % time near target Diet #> effect #> 1 (NFree10 and AFree10 and AFree17) ‒ (ADiet10 and NFree17 and ADiet17) #> effect_size LL UL numerator denominator SE df #> 1 -0.3955976 -0.7655978 -0.02380328 -4.533333 11.37966 0.1892368 108 #> d_biased #> 1 -0.3983716 #> This standardized mean difference is called d_s because the standardizer used was s_p. d_s has been corrected for bias. Correction for bias can be important when df < 50. See the rightmost column for the biased value. #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimated mean difference plot_mdiff(estimate, effect_size = \"mean\") }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_one.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a single-group design with a continuous outcome\r\nvariable compared to a reference or population value — estimate_mdiff_one","title":"Estimates for a single-group design with a continuous outcome\r\nvariable compared to a reference or population value — estimate_mdiff_one","text":"estimate_mdiff_one suitable single-group design continuous outcome variable compared reference population value. can express estimates mean differences, standardized mean differences (Cohen's d) median differences (raw data ). can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_one.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a single-group design with a continuous outcome\r\nvariable compared to a reference or population value — estimate_mdiff_one","text":"","code":"estimate_mdiff_one( data = NULL, outcome_variable = NULL, comparison_mean = NULL, comparison_sd = NULL, comparison_n = NULL, reference_mean = 0, outcome_variable_name = \"My outcome variable\", conf_level = 0.95, save_raw_data = TRUE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_one.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a single-group design with a continuous outcome\r\nvariable compared to a reference or population value — estimate_mdiff_one","text":"data raw data - data frame tibble outcome_variable raw data - column name outcome variable, vector numeric data comparison_mean summary data, numeric comparison_sd summary data, numeric > 0 comparison_n summary data, numeric integer > 0 reference_mean Reference value, defaults 0 outcome_variable_name Optional friendly name outcome variable. Defaults 'outcome variable' outcome variable column name data frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. save_raw_data raw data; defaults TRUE; set FALSE save memory returning raw data estimate object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_one.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a single-group design with a continuous outcome\r\nvariable compared to a reference or population value — estimate_mdiff_one","text":"Returns object class esci_estimate overview outcome_variable_name - mean - mean_LL - mean_UL - median - median_LL - median_UL - sd - min - max - q1 - q3 - n - missing - df - mean_SE - median_SE - es_mean outcome_variable_name - effect - effect_size - LL - UL - SE - df - ta_LL - ta_UL - es_median outcome_variable_name - effect - effect_size - LL - UL - SE - df - ta_LL - ta_UL - raw_data grouping_variable - outcome_variable - es_mean_difference outcome_variable_name - effect - effect_size - LL - UL - SE - df - ta_LL - ta_UL - type - es_median_difference outcome_variable_name - effect - effect_size - LL - UL - SE - df - ta_LL - ta_UL - type - es_smd outcome_variable_name - effect - effect_size - LL - UL - numerator - denominator - SE - df - d_biased -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_one.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a single-group design with a continuous outcome\r\nvariable compared to a reference or population value — estimate_mdiff_one","text":"Reach function place z-test one-sample t-test. generate estimate function, can visualize plot_mdiff() can test hypotheses test_mdiff(). estimated mean differences statpsych::ci.mean1(). estimated SMDs CI_smd_one(). estimated median differences statpsych::ci.median1()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_one.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates for a single-group design with a continuous outcome\r\nvariable compared to a reference or population value — estimate_mdiff_one","text":"","code":"# From raw data data(\"data_penlaptop1\") estimate <- esci::estimate_mdiff_one( data = data_penlaptop1[data_penlaptop1$condition == \"Pen\", ], outcome_variable = transcription, reference_mean = 10 ) estimate #> Analysis of raw data: #> Data frame = data #> Outcome variable(s) = transcription #> #> ---Overview--- #> outcome_variable_name mean mean_LL mean_UL median median_LL median_UL #> 1 transcription 8.811765 7.154642 10.46889 8.6 6.5 11.1 #> sd min max q1 q3 n missing df mean_SE median_SE #> 1 4.749339 1 20.1 5.2 11.275 34 0 33 0.8145049 1.021201 #> #> -- es_mean -- #> outcome_variable_name effect effect_size LL UL SE #> 1 transcription transcription 8.811765 7.154642 10.46889 0.8145049 #> df ta_LL ta_UL #> 1 33 7.746606 9.876923 #> #> -- es_median -- #> outcome_variable_name effect effect_size LL UL SE df ta_LL #> 1 transcription transcription 8.6 6.5 11.1 1.021201 33 7.1 #> ta_UL #> 1 10.8 #> #> -- es_mean_difference -- #> outcome_variable_name effect effect_size LL #> 1 transcription transcription 8.811765 7.154642 #> 2 transcription Reference value 10.000000 NA #> 3 transcription transcription ‒ Reference value -1.188235 -2.845358 #> UL SE df ta_LL ta_UL type #> 1 10.4688874 0.8145049 33 7.746606 9.876923 Comparison #> 2 NA NA NA 7.746606 9.876923 Reference #> 3 0.4688874 0.8145049 33 -2.253394 -0.123077 Difference #> #> -- es_median_difference -- #> outcome_variable_name effect effect_size LL UL #> 1 transcription transcription 8.6 6.5 11.1 #> 2 transcription Reference value 10.0 NA NA #> 3 transcription transcription ‒ Reference value -1.4 -3.5 1.1 #> SE df ta_LL ta_UL type #> 1 1.021201 33 7.1 10.8 Comparison #> 2 NA NA NA NA Reference #> 3 1.021201 33 -2.9 0.8 Difference #> #> -- es_smd -- #> outcome_variable_name effect effect_size LL #> 1 transcription transcription ‒ Reference value -0.2444527 -0.5838873 #> UL numerator denominator SE df d_biased #> 1 0.09856549 -1.188235 4.749339 0.1740983 33 -0.2501896 #> This standardized mean difference is called d_1 because the standardizer used was s. d_1 has been corrected for bias. Correction for bias can be important when df < 50. See the rightmost column for the biased value. #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimate plot_mdiff(estimate) } if (FALSE) { # To conduct a hypothesis test test_mdiff(estimate, effect_size = \"mean\", rope = c(-2, 2)) } # From summary data mymean <- 12.09 mysd <- 5.52 myn <- 103 estimate <- esci::estimate_mdiff_one( comparison_mean = mymean, comparison_sd = mysd, comparison_n = myn, reference_mean = 12 ) estimate #> Analysis of raw data: #> Outcome variable(s) = My outcome variable #> #> ---Overview--- #> outcome_variable_name mean mean_LL mean_UL sd n df mean_SE #> 1 My outcome variable 12.09 11.01117 13.16883 5.52 103 102 0.5439018 #> #> -- es_mean -- #> outcome_variable_name effect effect_size LL UL #> 1 My outcome variable My outcome variable 12.09 11.01117 13.16883 #> SE df ta_LL ta_UL #> 1 0.5439018 102 11.38842 12.79158 #> #> -- es_mean_difference -- #> outcome_variable_name effect effect_size #> 1 My outcome variable My outcome variable 12.09 #> 2 My outcome variable Reference value 12.00 #> 3 My outcome variable My outcome variable ‒ Reference value 0.09 #> LL UL SE df ta_LL ta_UL type #> 1 11.0111734 13.168827 0.5439018 102 11.3884176 12.7915824 Comparison #> 2 NA NA NA NA 11.3884176 12.7915824 Reference #> 3 -0.9888266 1.168827 0.5439018 102 -0.6115824 0.7915824 Difference #> #> -- es_smd -- #> outcome_variable_name effect effect_size #> 1 My outcome variable My outcome variable ‒ Reference value 0.01618412 #> LL UL numerator denominator SE df d_biased #> 1 -0.1769892 0.2092782 0.09 5.52 0.09853943 102 0.01630435 #> This standardized mean difference is called d_1 because the standardizer used was s. d_1 has been corrected for bias. Correction for bias can be important when df < 50. See the rightmost column for the biased value. #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimate plot_mdiff(estimate) } if (FALSE) { # To conduct a hypothesis test test_mdiff(estimate, effect_size = \"mean\", rope = c(-2, 2)) }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_paired.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a repeated-measures study with two measures of a continuous\r\nvariable — estimate_mdiff_paired","title":"Estimates for a repeated-measures study with two measures of a continuous\r\nvariable — estimate_mdiff_paired","text":"estimate_mdiff_paired suitable simple paired design continuous outcome variable. provides estimates CIs population mean difference repeated measures, standardized mean difference (SMD; Cohen's d) repeated measures, median difference repeated measures (raw data ). can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_paired.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a repeated-measures study with two measures of a continuous\r\nvariable — estimate_mdiff_paired","text":"","code":"estimate_mdiff_paired( data = NULL, comparison_measure = NULL, reference_measure = NULL, comparison_mean = NULL, comparison_sd = NULL, reference_mean = NULL, reference_sd = NULL, n = NULL, correlation = NULL, comparison_measure_name = \"Comparison measure\", reference_measure_name = \"Reference measure\", conf_level = 0.95, save_raw_data = TRUE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_paired.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a repeated-measures study with two measures of a continuous\r\nvariable — estimate_mdiff_paired","text":"data raw data - data frame tibble comparison_measure raw data - column name comparison measure outcome variable, vector numeric data reference_measure raw data - column name reference measure outcome variable, vector numeric data comparison_mean summary data, numeric comparison_sd summary data, numeric > 0 reference_mean summary data, numeric reference_sd summary data, numeric > 0 n summary data, numeric integer > 0 correlation summary data, correlation measures, numeric > -1 < 1 comparison_measure_name summary data - optional character label comparison measure. Defaults 'Comparison measure' reference_measure_name summary data - optional character label reference measure. Defaults 'Reference measure' conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. save_raw_data raw data; defaults TRUE; set FALSE save memory returning raw data estimate object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_paired.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a repeated-measures study with two measures of a continuous\r\nvariable — estimate_mdiff_paired","text":"Returns object class esci_estimate overview outcome_variable_name - mean - mean_LL - mean_UL - median - median_LL - median_UL - sd - min - max - q1 - q3 - n - missing - df - mean_SE - median_SE - es_mean_difference type - comparison_measure_name - reference_measure_name - effect - effect_size - LL - UL - SE - df - ta_LL - ta_UL - es_smd comparison_measure_name - reference_measure_name - effect - effect_size - LL - UL - numerator - denominator - SE - d_biased - df - es_r x_variable_name - y_variable_name - effect - effect_size - LL - UL - SE - n - df - ta_LL - ta_UL - es_median_difference type - comparison_measure_name - reference_measure_name - effect - effect_size - LL - UL - SE - ta_LL - ta_UL - es_mean_ratio comparison_measure_name - reference_measure_name - effect - effect_size - LL - UL - comparison_mean - reference_mean - es_median_ratio comparison_measure_name - reference_measure_name - effect - effect_size - LL - UL - comparison_median - reference_median - raw_data comparison_measure - reference_measure -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_paired.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a repeated-measures study with two measures of a continuous\r\nvariable — estimate_mdiff_paired","text":"Reach function place paired-samples t-test. generate estimate function, can visualize plot_mdiff() can test hypotheses test_mdiff(). estimated mean differences statpsych::ci.mean.ps(). estimated SMDs CI_smd_ind_contrast(). estimated median differences statpsych::ci.median.ps().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_paired.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates for a repeated-measures study with two measures of a continuous\r\nvariable — estimate_mdiff_paired","text":"","code":"# From raw data data(\"data_thomason_1\") estimate <- esci::estimate_mdiff_paired( data = data_thomason_1, comparison_measure = Posttest, reference_measure = Pretest ) estimate #> Analysis of raw data: #> Data frame = structure(list(\"Participant ID\" = structure(1:12, levels = c(\"1\", #> Analysis of raw data: #> Data frame = \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\"), class = \"factor\"), #> Analysis of raw data: #> Data frame = Pretest = c(13, 12, 12, 9, 14, 17, 14, 9, 6, 7, 11, 15), #> Analysis of raw data: #> Data frame = Posttest = c(14, 13, 16, 12, 15, 18, 13, 10, 10, 8, 14, 16 #> Analysis of raw data: #> Data frame = )), removedRows = list(), addedRows = list(list(start = 0L, #> Analysis of raw data: #> Data frame = end = 11L)), transforms = list(), row.names = c(NA, 12L), class = \"data.frame\") #> #> ---Overview--- #> outcome_variable_name mean mean_LL mean_UL median median_LL median_UL #> 1 Pretest 11.58333 9.476776 13.68989 12.0 9 14 #> 2 Posttest 13.25000 11.410019 15.08998 13.5 10 16 #> sd min max q1 q3 n missing df mean_SE median_SE #> 1 3.315483 6 17 9.0 14.00 12 0 11 0.9570974 1.208488 #> 2 2.895922 8 18 11.5 15.25 12 0 11 0.8359806 1.450186 #> #> -- es_mean_difference -- #> type comparison_measure_name reference_measure_name effect #> 1 Comparison Posttest Pretest Posttest #> 2 Reference Posttest Pretest Pretest #> 11 Difference Posttest Pretest Posttest ‒ Pretest #> effect_size LL UL SE df ta_LL ta_UL #> 1 13.250000 11.410019 15.089981 0.8359806 11 11.748675 14.751325 #> 2 11.583333 9.476776 13.689890 0.9570974 11 9.864497 13.302170 #> 11 1.666667 0.715218 2.618115 0.4322831 11 0.890336 2.442997 #> #> -- es_smd -- #> comparison_measure_name reference_measure_name effect effect_size #> 1 Posttest Pretest Posttest - Pretest 0.5105098 #> LL UL numerator denominator SE d_biased df #> 1 0.1806393 0.8902152 1.666667 3.112779 0.1810176 0.5105098 11 #> This standardized mean difference is called d_average because the standardizer used was s_average. d_average has been corrected for bias. Correction for bias can be important when df < 50. #> #> -- es_r -- #> x_variable_name y_variable_name effect effect_size LL #> 1 Pretest Posttest Pretest and Posttest 0.8923908 0.62894 #> UL SE n df ta_LL ta_UL #> 1 0.967157 0.06139936 12 10 0.6882891 0.9596343 #> #> -- es_median_difference -- #> type comparison_measure_name reference_measure_name effect #> Comparison Posttest Pretest Posttest #> 1 Reference Posttest Pretest Pretest #> 2 Difference Posttest Pretest Posttest ‒ Pretest #> effect_size LL UL SE ta_LL ta_UL #> 13.5 10.000000 16.000000 1.450186 10.000000 16.000000 #> 1 12.0 9.000000 14.000000 1.208488 9.000000 14.000000 #> 2 1.5 -1.589665 4.589665 1.576389 -1.589665 4.589665 #> #> -- es_mean_ratio -- #> comparison_measure_name reference_measure_name effect effect_size #> 1 Posttest Pretest Posttest / Pretest 1.143885 #> LL UL comparison_mean reference_mean #> 1 1.05031 1.245797 13.25 11.58333 #> [1] \"This effect-size measure is appropriate only for true ratio scales.\" #> #> -- es_median_ratio -- #> comparison_measure_name reference_measure_name effect effect_size #> 1 Posttest Pretest Posttest / Pretest 1.125 #> LL UL comparison_median reference_median #> 1 0.8723319 1.450853 13.5 12 #> [1] \"This effect-size measure is appropriate only for true ratio scales.\" #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimated median difference (raw data only) plot_mdiff(estimate, effect_size = \"median\") } sd1 <- 4.28 sd2 <- 3.4 sdiff <- 2.13 cor <- (sd1^2 + sd2^2 - sdiff^2) / (2*sd1*sd2) estimate <- estimate_mdiff_paired( comparison_mean = 14.25, comparison_sd = 4.28, reference_mean = 12.88, reference_sd = 3.4, n = 16, correlation = 0.87072223749, comparison_measure_name = \"After\", reference_measure_name = \"Before\" ) estimate #> Analysis of raw data: #> #> ---Overview--- #> outcome_variable_name mean mean_LL mean_UL sd n df mean_SE #> 1 Before 12.88 11.06827 14.69173 3.40 16 15 0.85 #> 2 After 14.25 11.96935 16.53065 4.28 16 15 1.07 #> #> -- es_mean_difference -- #> type comparison_measure_name reference_measure_name effect #> 1 Comparison After Before After #> 2 Reference After Before Before #> 11 Difference After Before After ‒ Before #> effect_size LL UL SE df ta_LL ta_UL #> 1 14.25 11.9693490 16.530651 1.0700 15 12.3742361 16.125764 #> 2 12.88 11.0682679 14.691732 0.8500 15 11.3899072 14.370093 #> 11 1.37 0.2350031 2.504997 0.5325 15 0.4365007 2.303499 #> #> -- es_smd -- #> comparison_measure_name reference_measure_name effect effect_size #> 1 After Before After - Before 0.3424327 #> LL UL numerator denominator SE d_biased df #> 1 0.05110995 0.6577932 1.37 3.865126 0.154769 0.3424327 15 #> This standardized mean difference is called d_average because the standardizer used was s_average. d_average has been corrected for bias. Correction for bias can be important when df < 50. #> #> -- es_r -- #> x_variable_name y_variable_name effect effect_size LL #> 1 Before After Before and After 0.8707222 0.6430976 #> UL SE n df ta_LL ta_UL #> 1 0.9518053 0.06244354 16 14 0.6915049 0.9428632 #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimated mean difference # plot_mdiff(estimate, effect_size = \"mean\") }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_two.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a two-group study with a continuous outcome variable — estimate_mdiff_two","title":"Estimates for a two-group study with a continuous outcome variable — estimate_mdiff_two","text":"estimate_mdiff_two suitable simple two-group design continuous outcome variable. provides estimates CIs population mean difference repeated measures, standardized mean difference (SMD; Cohen's d) repeated measures, median difference repeated measures (raw data ). can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_two.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a two-group study with a continuous outcome variable — estimate_mdiff_two","text":"","code":"estimate_mdiff_two( data = NULL, outcome_variable = NULL, grouping_variable = NULL, comparison_mean = NULL, comparison_sd = NULL, comparison_n = NULL, reference_mean = NULL, reference_sd = NULL, reference_n = NULL, grouping_variable_levels = NULL, outcome_variable_name = \"My outcome variable\", grouping_variable_name = \"My grouping variable\", conf_level = 0.95, assume_equal_variance = FALSE, save_raw_data = TRUE, switch_comparison_order = FALSE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_two.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a two-group study with a continuous outcome variable — estimate_mdiff_two","text":"data raw data - datavframe tibble outcome_variable raw data - column name outcome variable, vector numeric data grouping_variable raw data - column name grouping variable, vector group names comparison_mean summary data, numeric comparison_sd summary data, numeric > 0 comparison_n summary data, numeric integer > 0 reference_mean summary data, numeric reference_sd summary data, numeric > 0 reference_n summary data, numeric integer > 0 grouping_variable_levels summary data - optional vector 2 group labels outcome_variable_name Optional friendly name outcome variable. Defaults 'outcome variable' outcome variable column name data frame passed. grouping_variable_name Optional friendly name grouping variable. Defaults 'grouping variable' grouping variable column name data.frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. assume_equal_variance Defaults FALSE save_raw_data raw data; defaults TRUE; set FALSE save memory returning raw data estimate object switch_comparison_order Defaults FALSE","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_two.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a two-group study with a continuous outcome variable — estimate_mdiff_two","text":"Returnsobject class esci_estimate es_mean_difference type - outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - SE - df - ta_LL - ta_UL - es_median_difference type - outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - SE - ta_LL - ta_UL - es_smd outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - numerator - denominator - SE - df - d_biased - es_mean_ratio outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - comparison_mean - reference_mean - es_median_ratio outcome_variable_name - grouping_variable_name - effect - effect_size - LL - UL - comparison_median - reference_median - overview outcome_variable_name - grouping_variable_name - grouping_variable_level - mean - mean_LL - mean_UL - median - median_LL - median_UL - sd - min - max - q1 - q3 - n - missing - df - mean_SE - median_SE - raw_data grouping_variable - outcome_variable -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_two.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a two-group study with a continuous outcome variable — estimate_mdiff_two","text":"Reach function place independent-samples t-test. generate estimate function, can visualize plot_mdiff() can test hypotheses test_mdiff(). estimated mean differences statpsych::ci.mean2(). estimated SMDs CI_smd_ind_contrast(). estimated median differences statpsych::ci.median2().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_mdiff_two.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates for a two-group study with a continuous outcome variable — estimate_mdiff_two","text":"","code":"# From summary data estimate <- estimate_mdiff_two( comparison_mean = 12.09, comparison_sd = 5.52, comparison_n = 103, reference_mean = 6.88, reference_sd = 4.22, reference_n = 48, grouping_variable_levels = c(\"Ref-Laptop\", \"Comp-Pen\"), outcome_variable_name = \"% Transcription\", grouping_variable_name = \"Note-taking type\", assume_equal_variance = TRUE ) if (FALSE) { # To visualize the estimated mean difference plot_mdiff(estimate, effect_size = \"mean\") } # From raw data data(\"data_penlaptop1\") estimate <- esci::estimate_mdiff_two( data = data_penlaptop1, outcome_variable = transcription, grouping_variable = condition, switch_comparison_order = TRUE, assume_equal_variance = TRUE ) if (FALSE) { # To visualize the estimated median difference (raw data only) plot_mdiff(estimate, effect_size = \"median\") }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_ind_contrast.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a multi-group study with a categorical outcome\r\nvariable — estimate_pdiff_ind_contrast","title":"Estimates for a multi-group study with a categorical outcome\r\nvariable — estimate_pdiff_ind_contrast","text":"estimate_pdiff_ind_contrast suitable multi-group design (subjects) categorical outcome variable. accepts user-defined set contrast weights allows estimation 1-df contrast. can express estimates difference proportions odds ratio (2-group designs ). can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_ind_contrast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a multi-group study with a categorical outcome\r\nvariable — estimate_pdiff_ind_contrast","text":"","code":"estimate_pdiff_ind_contrast( data = NULL, outcome_variable = NULL, grouping_variable = NULL, cases = NULL, ns = NULL, contrast = NULL, case_label = 1, grouping_variable_levels = NULL, outcome_variable_name = \"My outcome variable\", grouping_variable_name = \"My grouping variable\", conf_level = 0.95, count_NA = FALSE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_ind_contrast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a multi-group study with a categorical outcome\r\nvariable — estimate_pdiff_ind_contrast","text":"data raw data - data frame tibble outcome_variable raw data - column name outcome variable factor, vector factor grouping_variable raw data - column name grouping variable factor, vector factor cases summary data - numeric vector 2 event counts, integer >= 0 ns summary data - numeric vector sample sizes, length counts, integer >= corresponding event count contrast vector group weights, length number groups. case_label optional numeric character label summary data, used label defaults 'Affected'. raw data, used specify level used proportion. grouping_variable_levels summary data - optional vector group labels, length cases outcome_variable_name Optional friendly name outcome variable. Defaults 'outcome variable' outcome variable column name data frame passed. grouping_variable_name Optional friendly name grouping variable. Defaults 'grouping variable' grouping variable column name data.frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. count_NA Logical count NAs (TRUE) total N (FALSE)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_ind_contrast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a multi-group study with a categorical outcome\r\nvariable — estimate_pdiff_ind_contrast","text":"Returns object class esci_estimate es_proportion_difference type - outcome_variable_name - case_label - grouping_variable_name - effect - effect_size - LL - UL - SE - effect_size_adjusted - ta_LL - ta_UL - es_odds_ratio outcome_variable_name - case_label - grouping_variable_name - effect - effect_size - SE - LL - UL - ta_LL - ta_UL - overview grouping_variable_name - grouping_variable_level - outcome_variable_name - outcome_variable_level - cases - n - P - P_LL - P_UL - P_SE - P_adjusted - ta_LL - ta_UL - es_phi grouping_variable_name - outcome_variable_name - effect - effect_size - SE - LL - UL -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_ind_contrast.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a multi-group study with a categorical outcome\r\nvariable — estimate_pdiff_ind_contrast","text":"generate estimate function, can visualize plot_mdiff() can test hypotheses test_mdiff(). estimated proportion differences statpsych::ci.lc.prop.bs(). estimated odds ratios (returned) statpsych::ci.oddsratio().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_ind_contrast.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates for a multi-group study with a categorical outcome\r\nvariable — estimate_pdiff_ind_contrast","text":"","code":"estimate <- estimate_pdiff_ind_contrast( cases = c(78, 10), ns = c(252, 20), case_label = \"egocentric\", grouping_variable_levels = c(\"Original\", \"Replication\"), contrast = c(-1, 1), conf_level = 0.95 ) if (FALSE) { # To visualize the estimate plot_proportion(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_one.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a single-group design with a categorical outcome\r\nvariable compared to a reference or population value. — estimate_pdiff_one","title":"Estimates for a single-group design with a categorical outcome\r\nvariable compared to a reference or population value. — estimate_pdiff_one","text":"estimate_pdiff_one suitable single-group design (subjects) categorical outcome variable. calculates effect sizes respect reference population proportion (default value 0). returns estimated difference proportion reference/population value. can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_one.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a single-group design with a categorical outcome\r\nvariable compared to a reference or population value. — estimate_pdiff_one","text":"","code":"estimate_pdiff_one( data = NULL, outcome_variable = NULL, comparison_cases = NULL, comparison_n = NULL, reference_p = 0, case_label = 1, outcome_variable_name = \"My outcome variable\", conf_level = 0.95, count_NA = FALSE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_one.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a single-group design with a categorical outcome\r\nvariable compared to a reference or population value. — estimate_pdiff_one","text":"data raw data - dataframe tibble outcome_variable raw data - column name outcome variable, must factor, vector factor comparison_cases summary data, numeric integer > 0 comparison_n summary data, numeric integer >= count reference_p Reference proportion, numeric >=0 <=1 case_label optional numeric character label count level. outcome_variable_name Optional friendly name outcome variable. Defaults 'outcome variable' outcome variable column name data frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. count_NA Logical count NAs (TRUE) total N (FALSE)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_one.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a single-group design with a categorical outcome\r\nvariable compared to a reference or population value. — estimate_pdiff_one","text":"Returns object class esci_estimate overview outcome_variable_name - outcome_variable_level - cases - n - P - P_LL - P_UL - P_SE - P_adjusted - ta_LL - ta_UL - es_proportion_difference outcome_variable_name - case_label - effect - effect_size - LL - UL - SE - effect_size_adjusted - ta_LL - ta_UL - cases - n - type -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_one.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a single-group design with a categorical outcome\r\nvariable compared to a reference or population value. — estimate_pdiff_one","text":"generate estimate function, can visualize plot_pdiff() can test hypotheses test_pdiff(). estimated proportion differences statpsych::ci.prop1().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_one.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates for a single-group design with a categorical outcome\r\nvariable compared to a reference or population value. — estimate_pdiff_one","text":"","code":"estimate <- estimate_pdiff_one( comparison_cases = 8, comparison_n = 22, reference_p = 0.5 ) if (FALSE) { # To visualize the estimate plot_proportion(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_paired.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a repeated-measures study with two measures of a categorical\r\nvariable — estimate_pdiff_paired","title":"Estimates for a repeated-measures study with two measures of a categorical\r\nvariable — estimate_pdiff_paired","text":"estimate_pdiff_paired suitable simple paired design categorical outcome variable. provides estimates CIs population proportion difference repeated measures. can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_paired.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a repeated-measures study with two measures of a categorical\r\nvariable — estimate_pdiff_paired","text":"","code":"estimate_pdiff_paired( data = NULL, comparison_measure = NULL, reference_measure = NULL, cases_consistent = NULL, cases_inconsistent = NULL, not_cases_consistent = NULL, not_cases_inconsistent = NULL, case_label = 1, not_case_label = NULL, comparison_measure_name = \"Comparison measure\", reference_measure_name = \"Reference measure\", conf_level = 0.95, count_NA = FALSE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_paired.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a repeated-measures study with two measures of a categorical\r\nvariable — estimate_pdiff_paired","text":"data raw data - datavframe tibble comparison_measure raw data - comparison measure, factor. Can column name data frame vector. reference_measure raw data - reference measure, factor. Can column name data frame vector. cases_consistent Count cases measure 1 also cases measure 2; measure 1 = 0, measure 2 = 0; cell 0_0 cases_inconsistent Count cases measure 1 cases measure 2; measure 1 = 0, measure 2 = 1; cell 0_1 not_cases_consistent Count cases measure 1 also cases measure 2; measure 1 = 1, measure 2 = 1, cell 1_1 not_cases_inconsistent Count cases measure 1 cases measure 2; measure 1 = 1, measure 2 = 0, cell 1_0 case_label optional numeric character label case level. not_case_label optional numeric character label case level. comparison_measure_name summary data - optional character label comparison measure. Defaults 'Comparison measure' reference_measure_name summary data - optional character label reference measure. Defaults 'Reference measure' conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. count_NA Logical count NAs (TRUE) total N (FALSE)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_paired.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a repeated-measures study with two measures of a categorical\r\nvariable — estimate_pdiff_paired","text":"Returns object class esci_estimate","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_paired.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a repeated-measures study with two measures of a categorical\r\nvariable — estimate_pdiff_paired","text":"generate estimate function, can visualize plot_pdiff() can test hypotheses test_pdiff(). estimated proportion differences statpsych::ci.prop.ps().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_two.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a two-group study with a categorical outcome variable — estimate_pdiff_two","title":"Estimates for a two-group study with a categorical outcome variable — estimate_pdiff_two","text":"estimate_pdiff_two suitable simple two-group design categorical outcome variable. provides estimates CIs difference proportions two groups, odds ratio, phi. can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_two.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a two-group study with a categorical outcome variable — estimate_pdiff_two","text":"","code":"estimate_pdiff_two( data = NULL, outcome_variable = NULL, grouping_variable = NULL, comparison_cases = NULL, comparison_n = NULL, reference_cases = NULL, reference_n = NULL, case_label = 1, not_case_label = NULL, grouping_variable_levels = NULL, outcome_variable_name = \"My outcome variable\", grouping_variable_name = \"My grouping variable\", conf_level = 0.95, count_NA = FALSE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_two.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a two-group study with a categorical outcome variable — estimate_pdiff_two","text":"data raw data - data frame tibble outcome_variable raw data - column name outcome variable factor, vector factor grouping_variable raw data - column name grouping variable factor, vector factor comparison_cases summary data, numeric integer >= 0 comparison_n summary data, numeric integer >= comparison_events reference_cases summary data, numeric integer >= 0 reference_n summary data, numeric integer >= reference_events case_label optional numeric character label case level. not_case_label optional numeric character label case level. grouping_variable_levels summary data - optional vector 2 group labels outcome_variable_name Optional friendly name outcome variable. Defaults 'outcome variable' outcome variable column name data frame passed. grouping_variable_name Optional friendly name grouping variable. Defaults 'grouping variable' grouping variable column name data.frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. count_NA Logical count NAs (TRUE) total N (FALSE)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_two.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a two-group study with a categorical outcome variable — estimate_pdiff_two","text":"Returnsobject class esci_estimate es_proportion_difference type - outcome_variable_name - case_label - grouping_variable_name - effect - effect_size - LL - UL - SE - effect_size_adjusted - ta_LL - ta_UL - es_odds_ratio outcome_variable_name - case_label - grouping_variable_name - effect - effect_size - SE - LL - UL - ta_LL - ta_UL - overview grouping_variable_name - grouping_variable_level - outcome_variable_name - outcome_variable_level - cases - n - P - P_LL - P_UL - P_SE - P_adjusted - ta_LL - ta_UL - es_phi grouping_variable_name - outcome_variable_name - effect - effect_size - SE - LL - UL -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_two.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a two-group study with a categorical outcome variable — estimate_pdiff_two","text":"generate estimate function, can visualize plot_mdiff() can test hypotheses test_mdiff(). estimated mean differences statpsych::ci.prop2(). estimated odds ratio statpsych::ci.oddsratio(). estimated correlation (phi) statpsych::ci.phi().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_pdiff_two.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates for a two-group study with a categorical outcome variable — estimate_pdiff_two","text":"","code":"estimate <- estimate_pdiff_two( comparison_cases = 10, comparison_n = 20, reference_cases = 78, reference_n = 252, grouping_variable_levels = c(\"Original\", \"Replication\"), conf_level = 0.95 ) if (FALSE) { # To visualize the estimate plot_pdiff(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates for a categorical variable with no grouping (single-group design) — estimate_proportion","title":"Estimates for a categorical variable with no grouping (single-group design) — estimate_proportion","text":"estimate_proportion suitable single group design categorical outcome variable. estimates population proportion frequency level outcome variable, confidence intervals. can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates for a categorical variable with no grouping (single-group design) — estimate_proportion","text":"","code":"estimate_proportion( data = NULL, outcome_variable = NULL, cases = NULL, case_label = 1, outcome_variable_levels = NULL, outcome_variable_name = \"My outcome variable\", conf_level = 0.95, count_NA = FALSE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_proportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates for a categorical variable with no grouping (single-group design) — estimate_proportion","text":"data raw data - data frame tibble outcome_variable raw data - column name outcome variable, must factor, vector factor cases summary data - vector cases case_label numeric string indicating level factor estimate. Defaults 1, meaning first level analyzed outcome_variable_levels summary data - optional vector 2 characters indicating name count level name count level. Defaults \"Affected\" \"Affected\" outcome_variable_name Optional friendly name outcome variable. Defaults 'outcome variable' outcome variable column name data frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. count_NA Logical count NAs (TRUE) total N (FALSE)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates for a categorical variable with no grouping (single-group design) — estimate_proportion","text":"Returns object class esci_estimate overview outcome_variable_name - outcome_variable_level - cases - n - P - P_LL - P_UL - P_SE - P_adjusted - ta_LL - ta_UL - es_proportion outcome_variable_name - case_label - effect - effect_size - LL - UL - SE - effect_size_adjusted - ta_LL - ta_UL - cases - n -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_proportion.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates for a categorical variable with no grouping (single-group design) — estimate_proportion","text":"generate estimate function, can visualize plot_proportion(). want compare estimate known value reference, use estimate_pdiff_one(). estimated proportions statpsych::ci.prop1().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_proportion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates for a categorical variable with no grouping (single-group design) — estimate_proportion","text":"","code":"estimate <- esci::estimate_proportion( cases = c(8, 22-8), outcome_variable_levels = c(\"Affected\", \"Not Affected\") ) if (FALSE) { # To visualize the estimate plot_proportion(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_r.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_r","title":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_r","text":"estimate_r suitable design two continuous variables. estimates linear correlation two variables (Pearson's r) confidence interval. can pass raw data summary data.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_r.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_r","text":"","code":"estimate_r( data = NULL, x = NULL, y = NULL, r = NULL, n = NULL, x_variable_name = \"My x variable\", y_variable_name = \"My y variable\", conf_level = 0.95, save_raw_data = TRUE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_r.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_r","text":"data raw data - data frame tibble x raw data - column name outcome variable, vector numeric data y raw data - column name outcome variable, vector numeric data r summary data - pearson's r correlation coefficient n summary data - Sample size, integer > 0 x_variable_name Optional friendly name x variable. Defaults 'x variable' outcome variable column name data frame passed. y_variable_name Optional friendly name y variable. Defaults 'y variable' outcome variable column name data frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. save_raw_data raw data; defaults TRUE; set FALSE save memory returning raw data estimate object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_r.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_r","text":"Returns object class esci_estimate overview outcome_variable_name - mean - mean_LL - mean_UL - median - median_LL - median_UL - sd - min - max - q1 - q3 - n - missing - df - mean_SE - median_SE - es_r x_variable_name - y_variable_name - effect - effect_size - LL - UL - SE - n - df - ta_LL - ta_UL - regression component - values - LL - UL - raw_data x - y - fit - lwr - upr -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_r.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_r","text":"Reach function conduct simple linear correlation simple linear regression. generate estimate function, can visualize plot_correlation() can test hypotheses test_correlation(). addition, can use plot_scatter() visualize raw data conduct regression analysis r returns predicted Y' values given X value. estimated correlation statpsych::ci.cor(), uses Fisher r--z approach.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_r.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates the linear correlation (Pearson's r) between two continuous\r\nvariables — estimate_r","text":"","code":"# From raw data thomason1 <- data.frame( ls_pre = c( 13, 12, 12, 9, 14, 17, 14, 9, 6, 7, 11, 15 ), ls_post = c( 14, 13, 16, 12, 15, 18, 13, 10, 10, 8, 14, 16 ) ) estimate <- esci::estimate_r( thomason1, ls_pre, ls_post ) estimate #> Analysis of raw data: #> Data frame = data #> #> ---Overview--- #> outcome_variable_name mean mean_LL mean_UL median median_LL median_UL #> 1 ls_pre 11.58333 9.476776 13.68989 12.0 9 14 #> 2 ls_post 13.25000 11.410019 15.08998 13.5 10 16 #> sd min max q1 q3 n missing df mean_SE median_SE #> 1 3.315483 6 17 9.0 14.00 12 0 11 0.9570974 1.208488 #> 2 2.895922 8 18 11.5 15.25 12 0 11 0.8359806 1.450186 #> #> -- es_r -- #> x_variable_name y_variable_name effect effect_size LL #> 1 ls_pre ls_post ls_pre and ls_post 0.8923908 0.62894 #> UL SE n df ta_LL ta_UL #> 1 0.967157 0.06139936 12 10 0.6882891 0.9596343 #> #> -- regression -- #> component values LL UL #> 1 Intercept (a) 4.2212267 0.8856544 7.556799 #> 2 Slope (b) 0.7794624 0.5017391 1.057186 #> [1] \"Ŷ = 4.221 + 0.7795*X\" #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. # To evaluate a hypothesis (interval null from -0.1 to 0.1): test_correlation(estimate, rope = c(-0.1, 0.1)) #> $properties #> $properties$effect_size_name #> [1] \"r\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] TRUE #> #> $properties$rope #> [1] -0.1 0.1 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name effect null_words #> 1 Nil Hypothesis Test ls_pre and ls_post ls_pre and ls_post 0.00 #> confidence LL UL CI #> 1 95 0.62894 0.967157 95% CI [0.62894, 0.967157] #> CI_compare t df p p_result #> 1 The 95% CI does not contain H_0 4.300635 10 1.703091e-05 p < 0.05 #> null_decision conclusion significant #> 1 Reject H_0 At α = 0.05, 0.00 is not a plausible value of Ͱ TRUE #> #> $interval_null #> test_type outcome_variable_name effect #> 1 Practical significance test ls_pre and ls_post ls_pre and ls_post #> rope confidence #> 1 (-0.10, 0.10) 95 #> CI #> 1 95% CI [0.62894, 0.967157]\\n90% CI [0.6882891, 0.9596343] #> rope_compare p_result conclusion #> 1 95% CI fully outside H_0 p < 0.05 At α = 0.05, conclude Ͱ is substantive #> significant #> 1 TRUE #> if (FALSE) { # To visualize the value of r plot_correlation(estimate) # To visualize the data (scatterplot) plot_scatter(estimate) # To visualize the data (scatterplot) and use regression to obtain Y' from X plot_scatter(estimate, predict_from_x = 10) } # From summary data estimate <- esci::estimate_r(r = 0.536, n = 50) estimate #> Analysis of summary data: #> #> -- es_r -- #> x_variable_name y_variable_name effect effect_size #> 1 My x variable My y variable My x variable and My y variable 0.536 #> LL UL SE n df ta_LL ta_UL #> 1 0.2978573 0.7058914 0.1018149 50 48 0.3391487 0.6820747 #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimate plot_correlation(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_rdiff_two.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimates the difference in correlation for a design with two groups and\r\ntwo continuous outcome variables — estimate_rdiff_two","title":"Estimates the difference in correlation for a design with two groups and\r\ntwo continuous outcome variables — estimate_rdiff_two","text":"estimate_rdiff_two suitable simple two-group design two continuous outcome variables want estimate difference strength relationship two groups. estimate linear correlation (Pearson's r) group difference r, along confidence intervals. can pass raw data summary data. Returns effect sizes appropriate estimating linear relationship two quantitative variables","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_rdiff_two.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimates the difference in correlation for a design with two groups and\r\ntwo continuous outcome variables — estimate_rdiff_two","text":"","code":"estimate_rdiff_two( data = NULL, x = NULL, y = NULL, grouping_variable = NULL, comparison_r = NULL, comparison_n = NULL, reference_r = NULL, reference_n = NULL, grouping_variable_levels = NULL, x_variable_name = \"My x variable\", y_variable_name = \"My y variable\", grouping_variable_name = \"My grouping variable\", conf_level = 0.95, save_raw_data = TRUE )"},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_rdiff_two.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimates the difference in correlation for a design with two groups and\r\ntwo continuous outcome variables — estimate_rdiff_two","text":"data raw data - dataframe tibble x raw data - column name outcome variable, vector numeric data y raw data - column name outcome variable, vector numeric data grouping_variable raw data, vector factor name factor column data comparison_r summary data, pearson's r correlation coefficient comparison_n summary data - integer > 0 reference_r summary data, pearson's r correlation coefficient reference_n summary data - integer > 0 grouping_variable_levels summary data - optional vector 2 group labels x_variable_name Optional friendly name x variable. Defaults 'x variable' outcome variable column name data frame passed. y_variable_name Optional friendly name y variable. Defaults 'y variable' outcome variable column name data frame passed. grouping_variable_name Optional friendly name grouping variable. Defaults 'grouping variable' grouping variable column name data.frame passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. save_raw_data raw data; defaults TRUE; set FALSE save memory returning raw data estimate object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_rdiff_two.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimates the difference in correlation for a design with two groups and\r\ntwo continuous outcome variables — estimate_rdiff_two","text":"Returnsobject class esci_estimate overview outcome_variable_name - grouping_variable_name - grouping_variable_level - mean - mean_LL - mean_UL - median - median_LL - median_UL - sd - min - max - q1 - q3 - n - missing - df - mean_SE - median_SE - es_r_difference type - grouping_variable_name - grouping_variable_level - x_variable_name - y_variable_name - effect - effect_size - LL - UL - SE - n - df - ta_LL - ta_UL - rz - sem - z - p - es_r grouping_variable_name - grouping_variable_level - x_variable_name - y_variable_name - effect - effect_size - LL - UL - SE - n - df - ta_LL - ta_UL - raw_data x - y - grouping_variable -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_rdiff_two.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimates the difference in correlation for a design with two groups and\r\ntwo continuous outcome variables — estimate_rdiff_two","text":"generate estimate function, can visualize plot_rdiff() can test hypotheses test_rdiff(). addition, can use plot_scatter() visualize raw data. estimated single-group r values statpsych::ci.cor(). difference r values statpsych::ci.cor2().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/estimate_rdiff_two.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimates the difference in correlation for a design with two groups and\r\ntwo continuous outcome variables — estimate_rdiff_two","text":"","code":"# From summary data estimate <- esci::estimate_rdiff_two( comparison_r = .53, comparison_n = 45, reference_r = .41, reference_n = 59, grouping_variable_levels = c(\"Females\", \"Males\"), x_variable_name = \"Satisfaction with life\", y_variable_name = \"Body satisfaction\", grouping_variable_name = \"Gender\", conf_level = .95 ) estimate #> Analysis of summary data: #> #> -- es_r -- #> grouping_variable_name grouping_variable_level x_variable_name #> 1 Gender Females Satisfaction with life #> 2 Gender Males Satisfaction with life #> y_variable_name effect effect_size LL UL SE n df #> 1 Body satisfaction Females 0.41 0.1685419 0.6005378 0.1092338 59 57 #> 2 Body satisfaction Males 0.53 0.2744717 0.7096862 0.1084084 45 43 #> ta_LL ta_UL #> 1 0.2091420 0.5729339 #> 2 0.3188047 0.6847105 #> #> -- es_r_difference -- #> type grouping_variable_name grouping_variable_level #> 1 Comparison Gender Males #> 2 Reference Gender Females #> 3 Difference Gender Males - Females #> x_variable_name y_variable_name effect effect_size #> 1 Satisfaction with life Body satisfaction Males 0.53 #> 2 Satisfaction with life Body satisfaction Females 0.41 #> 3 Satisfaction with life Body satisfaction Males - Females 0.12 #> LL UL SE n df ta_LL ta_UL rz sem #> 1 0.2744717 0.7096862 0.1084084 45 43 0.3188047 0.6847105 0.5901452 0.1543033 #> 2 0.1685419 0.6005378 0.1092338 59 57 0.2091420 0.5729339 0.4356112 0.1336306 #> 3 -0.1987466 0.4209803 NA NA NA -0.1467413 0.3735336 0.1545339 0.2041241 #> z p #> 1 3.8245778 0.0001309964 #> 2 3.2598159 0.0011148455 #> 3 0.7570586 0.4490147644 #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. # To evaluate a hypothesis (interval null from -0.1 to 0.1): esci::test_rdiff(estimate) #> $properties #> $properties$effect_size_name #> [1] \"r\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] FALSE #> #> $properties$rope #> [1] 0 0 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name #> 1 Nil Hypothesis Test Satisfaction with life and Body satisfaction #> effect null_words confidence LL UL #> 1 Males - Females 0.00 95 -0.1987466 0.4209803 #> CI CI_compare t df p #> 1 95% CI [-0.1987466, 0.4209803] The 95% CI contains H_0 0.7570586 NA 0.4490148 #> p_result null_decision #> 1 p ≥ 0.05 Fail to reject H_0 #> conclusion significant #> 1 At α = 0.05, 0.00 remains a plausible value of Ͱ_diff FALSE #> if (FALSE) { # To visualize the values of r and their difference esci::plot_rdiff(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/geom_meta_diamond_h.html","id":null,"dir":"Reference","previous_headings":"","what":"Meta-analysis diamond — geom_meta_diamond_h","title":"Meta-analysis diamond — geom_meta_diamond_h","text":"geom_meta_diamond_h creates horizontal meta-analytic diamond defined x value (horizontal center diamond), xmin xmax values (horizontal ends diamond), y value (vertical placement diamond) height (vertical height diamond). Note use xmin xmax allows representation asymmetric confidence intervals geom.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/geom_meta_diamond_h.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Meta-analysis diamond — geom_meta_diamond_h","text":"","code":"geom_meta_diamond_h( mapping = NULL, data = NULL, stat = \"identity\", position = \"identity\", ..., na.rm = FALSE, show.legend = NA, inherit.aes = TRUE )"},{"path":"https://rcalinjageman.github.io/esci/reference/geom_meta_diamond_h.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Meta-analysis diamond — geom_meta_diamond_h","text":"mapping Set aesthetic mappings created aes(). specified inherit.aes = TRUE (default), combined default mapping top level plot. must supply mapping plot mapping. data data displayed layer. three options: NULL, default, data inherited plot data specified call ggplot(). data.frame, object, override plot data. objects fortified produce data frame. See fortify() variables created. function called single argument, plot data. return value must data.frame, used layer data. function can created formula (e.g. ~ head(.x, 10)). stat statistical transformation use data layer, either ggproto Geom subclass string naming stat stripped stat_ prefix (e.g. \"count\" rather \"stat_count\") position Position adjustment, either string naming adjustment (e.g. \"jitter\" use position_jitter), result call position adjustment function. Use latter need change settings adjustment. ... arguments passed geom. often aesthetics, used set aesthetic fixed value, like colour = \"red\" linewidth = 3 (see Aesthetics, ). ##Aesthetics ## geom_meta_diamond_h understands following aesthetics (required bold): x - horizontal center diamond y - vertical placement diamond xmin - left-side start diamond xmax - right-side start diamond height - vertical span diamond na.rm FALSE, default, missing values removed warning. TRUE, missing values silently removed. show.legend logical. layer included legends? NA, default, includes aesthetics mapped. FALSE never includes, TRUE always includes. can also named logical vector finely select aesthetics display. inherit.aes FALSE, overrides default aesthetics, rather combining . useful helper functions define data aesthetics inherit behaviour default plot specification, e.g. borders().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovicorrelation.html","id":null,"dir":"Reference","previous_headings":"","what":"Correlations: Single Group — jamovicorrelation","title":"Correlations: Single Group — jamovicorrelation","text":"Correlations: Single Group","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovicorrelation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Correlations: Single Group — jamovicorrelation","text":"","code":"jamovicorrelation( switch = \"from_raw\", data, x, y, r = \" \", n = \" \", x_variable_name = \"X variable\", y_variable_name = \"Y variable\", conf_level = 95, show_details = FALSE, do_regression = FALSE, show_line = FALSE, show_line_CI = FALSE, show_residuals = FALSE, show_PI = FALSE, show_mean_lines = FALSE, show_r = FALSE, plot_as_z = FALSE, predict_from_x = \" \", evaluate_hypotheses = FALSE, null_value = \"0\", null_boundary = \"0\", alpha = 0.05, null_color = \"#A40122\", es_plot_width = \"300\", es_plot_height = \"400\", sp_plot_width = \"650\", sp_plot_height = \"650\", ymin = \"-1\", ymax = \"1\", ybreaks = \"auto\", ylab = \"auto\", xlab = \"auto\", axis.text.y = \"14\", axis.title.y = \"15\", axis.text.x = \"14\", axis.title.x = \"15\", error_layout = \"none\", sp_ymin = \"auto\", sp_ymax = \"auto\", sp_ybreaks = \"auto\", sp_xmin = \"auto\", sp_xmax = \"auto\", sp_xbreaks = \"auto\", sp_ylab = \"auto\", sp_xlab = \"auto\", sp_axis.text.y = \"14\", sp_axis.title.y = \"15\", sp_axis.text.x = \"14\", sp_axis.title.x = \"15\", shape_summary = \"circle filled\", color_summary = \"#008DF9\", fill_summary = \"#008DF9\", size_summary = \"4\", alpha_summary = \"1\", linetype_summary = \"solid\", color_interval = \"black\", size_interval = \"3\", alpha_interval = \"1\", alpha_error = \"1\", fill_error = \"gray75\", sp_shape_raw_reference = \"circle filled\", sp_color_raw_reference = \"black\", sp_fill_raw_reference = \"#008DF9\", sp_size_raw_reference = \"3\", sp_alpha_raw_reference = \".25\", sp_linetype_summary_reference = \"solid\", sp_color_summary_reference = \"#008DF9\", sp_size_summary_reference = \"3\", sp_alpha_summary_reference = \".25\", sp_linetype_PI_reference = \"dotted\", sp_color_PI_reference = \"#E20134\", sp_size_PI_reference = \"2\", sp_alpha_PI_reference = \"1\", sp_linetype_residual_reference = \"solid\", sp_color_residual_reference = \"#E20134\", sp_size_residual_reference = \"1\", sp_alpha_residual_reference = \"1\", sp_prediction_label = \"5\", sp_prediction_color = \"#E20134\", sp_linetype_CI = \"solid\", sp_color_CI = \"#008DF9\", sp_size_CI = \"4\", sp_alpha_CI = \"1\", sp_linetype_ref = \"dotted\", sp_color_ref = \"gray60\", sp_size_ref = \"1\", sp_alpha_ref = \"1\", sp_linetype_PI = \"solid\", sp_color_PI = \"#E20134\", sp_size_PI = \"2\", sp_alpha_PI = \"1\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovicorrelation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Correlations: Single Group — jamovicorrelation","text":"switch . data . x . y . r . n . x_variable_name . y_variable_name . conf_level . show_details . do_regression . show_line . show_line_CI . show_residuals . show_PI . show_mean_lines . show_r . plot_as_z . predict_from_x . evaluate_hypotheses . null_value . null_boundary . alpha . null_color . es_plot_width . es_plot_height . sp_plot_width . sp_plot_height . ymin . ymax . ybreaks . ylab . xlab . axis.text.y . axis.title.y . axis.text.x . axis.title.x . error_layout . sp_ymin . sp_ymax . sp_ybreaks . sp_xmin . sp_xmax . sp_xbreaks . sp_ylab . sp_xlab . sp_axis.text.y . sp_axis.title.y . sp_axis.text.x . sp_axis.title.x . shape_summary . color_summary . fill_summary . size_summary . alpha_summary . linetype_summary . color_interval . size_interval . alpha_interval . alpha_error . fill_error . sp_shape_raw_reference . sp_color_raw_reference . sp_fill_raw_reference . sp_size_raw_reference . sp_alpha_raw_reference . sp_linetype_summary_reference . sp_color_summary_reference . sp_size_summary_reference . sp_alpha_summary_reference . sp_linetype_PI_reference . sp_color_PI_reference . sp_size_PI_reference . sp_alpha_PI_reference . sp_linetype_residual_reference . sp_color_residual_reference . sp_size_residual_reference . sp_alpha_residual_reference . sp_prediction_label . sp_prediction_color . sp_linetype_CI . sp_color_CI . sp_size_CI . sp_alpha_CI . sp_linetype_ref . sp_color_ref . sp_size_ref . sp_alpha_ref . sp_linetype_PI . sp_color_PI . sp_size_PI . sp_alpha_PI .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovicorrelation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Correlations: Single Group — jamovicorrelation","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$overview$asDF .data.frame(results$overview)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovidescribe.html","id":null,"dir":"Reference","previous_headings":"","what":"Describe — jamovidescribe","title":"Describe — jamovidescribe","text":"Describe","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovidescribe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Describe — jamovidescribe","text":"","code":"jamovidescribe( data, outcome_variable, show_details = FALSE, mark_mean = FALSE, mark_median = FALSE, mark_sd = FALSE, mark_quartiles = FALSE, mark_z_lines = FALSE, mark_percentile = \"0\", histogram_bins = \"12\", es_plot_width = \"500\", es_plot_height = \"400\", ymin = \"auto\", ymax = \"auto\", breaks = \"auto\", xmin = \"auto\", xmax = \"auto\", xbreaks = \"auto\", ylab = \"auto\", xlab = \"auto\", axis.text.y = \"14\", axis.title.y = \"15\", axis.text.x = \"14\", axis.title.x = \"15\", fill_regular = \"#008DF9\", fill_highlighted = \"#E20134\", color = \"black\", marker_size = \"4\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovidescribe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Describe — jamovidescribe","text":"data . outcome_variable . show_details . mark_mean . mark_median . mark_sd . mark_quartiles . mark_z_lines . mark_percentile . histogram_bins . es_plot_width . es_plot_height . ymin . ymax . breaks . xmin . xmax . xbreaks . ylab . xlab . axis.text.y . axis.title.y . axis.text.x . axis.title.x . fill_regular . fill_highlighted . color . marker_size .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovidescribe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Describe — jamovidescribe","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$overview$asDF .data.frame(results$overview)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimagnitude.html","id":null,"dir":"Reference","previous_headings":"","what":"Means and Medians: Single Group — jamovimagnitude","title":"Means and Medians: Single Group — jamovimagnitude","text":"Means Medians: Single Group","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimagnitude.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Means and Medians: Single Group — jamovimagnitude","text":"","code":"jamovimagnitude( switch = \"from_raw\", data, outcome_variable, mean = \"\", sd = \"\", n = \"\", outcome_variable_name = \"Outcome variable\", conf_level = 95, effect_size = \"mean\", show_details = FALSE, show_calculations = FALSE, evaluate_hypotheses = FALSE, null_value = \"0\", null_boundary = \"0\", alpha = 0.05, null_color = \"#A40122\", es_plot_width = \"300\", es_plot_height = \"400\", ymin = \"auto\", ymax = \"auto\", breaks = \"auto\", ylab = \"auto\", xlab = \"auto\", axis.text.y = \"14\", axis.title.y = \"15\", axis.text.x = \"14\", axis.title.x = \"15\", error_layout = \"halfeye\", error_scale = \"0.20\", error_nudge = \"0.3\", data_layout = \"random\", data_spread = \"0.25\", shape_raw = \"circle filled\", shape_summary = \"circle filled\", color_raw = \"#008DF9\", color_summary = \"#008DF9\", fill_raw = \"NA\", fill_summary = \"#008DF9\", size_raw = \"2\", size_summary = \"4\", alpha_raw = \"1\", alpha_summary = \"1\", linetype_summary = \"solid\", color_interval = \"black\", size_interval = \"3\", alpha_interval = \"1\", alpha_error = \"1\", fill_error = \"gray75\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimagnitude.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Means and Medians: Single Group — jamovimagnitude","text":"switch . data . outcome_variable . mean . sd . n . outcome_variable_name . conf_level . effect_size . show_details . show_calculations . evaluate_hypotheses . null_value . null_boundary . alpha . null_color . es_plot_width . es_plot_height . ymin . ymax . breaks . ylab . xlab . axis.text.y . axis.title.y . axis.text.x . axis.title.x . error_layout . error_scale . error_nudge . data_layout . data_spread . shape_raw . shape_summary . color_raw . color_summary . fill_raw . fill_summary . size_raw . size_summary . alpha_raw . alpha_summary . linetype_summary . color_interval . size_interval . alpha_interval . alpha_error . fill_error .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimagnitude.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Means and Medians: Single Group — jamovimagnitude","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$overview$asDF .data.frame(results$overview)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiff2x2.html","id":null,"dir":"Reference","previous_headings":"","what":"Means and Medians: 2x2 Factorial — jamovimdiff2x2","title":"Means and Medians: 2x2 Factorial — jamovimdiff2x2","text":"Means Medians: 2x2 Factorial","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiff2x2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Means and Medians: 2x2 Factorial — jamovimdiff2x2","text":"","code":"jamovimdiff2x2( design = \"fully_between\", switch = \"from_raw\", data, outcome_variable, grouping_variable_A, grouping_variable_B, outcome_variable_level1, outcome_variable_level2, outcome_variable_name_bs = \"My outcome variable\", grouping_variable, repeated_measures_name = \"Time\", outcome_variable_name = \"My outcome variable\", A1_label = \"A1 level\", A2_label = \"A2 level\", B1_label = \"B1 level\", B2_label = \"B2 level\", A_label = \"Variable A\", B_label = \"Variable B\", A1B1_mean = \" \", A1B1_sd = \" \", A1B1_n = \" \", A1B2_mean = \" \", A1B2_sd = \" \", A1B2_n = \" \", A2B1_mean = \" \", A2B1_sd = \" \", A2B1_n = \" \", A2B2_mean = \" \", A2B2_sd = \" \", A2B2_n = \" \", conf_level = 95, effect_size = \"mean_difference\", assume_equal_variance = TRUE, show_details = FALSE, show_interaction_plot = FALSE, show_CI = FALSE, evaluate_hypotheses = FALSE, null_value = \"0\", null_boundary = \"0\", rope_units = \"raw\", alpha = 0.05, null_color = \"#A40122\", es_plot_width = \"700\", es_plot_height = \"400\", ymin = \"auto\", ymax = \"auto\", ybreaks = \"auto\", ylab = \"auto\", xlab = \"auto\", axis.text.y = \"14\", axis.title.y = \"15\", axis.text.x = \"14\", axis.title.x = \"15\", simple_contrast_labels = FALSE, error_layout = \"halfeye\", error_scale = \"0.25\", error_nudge = \"0.5\", data_layout = \"random\", data_spread = \"0.20\", difference_axis_units = \"raw\", difference_axis_breaks = \"auto\", shape_raw_reference = \"circle filled\", shape_raw_comparison = \"circle filled\", shape_raw_unused = \"circle filled\", shape_summary_reference = \"circle filled\", shape_summary_comparison = \"circle filled\", shape_summary_unused = \"circle filled\", shape_summary_difference = \"triangle filled\", color_raw_reference = \"#008DF9\", color_raw_comparison = \"#009F81\", color_raw_unused = \"gray65\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#009F81\", color_summary_unused = \"gray65\", color_summary_difference = \"black\", fill_raw_reference = \"NA\", fill_raw_comparison = \"NA\", fill_raw_unused = \"NA\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#009F81\", fill_summary_unused = \"gray65\", fill_summary_difference = \"black\", size_raw_reference = \"2\", size_raw_comparison = \"2\", size_raw_unused = \"1\", size_summary_reference = \"4\", size_summary_comparison = \"4\", size_summary_unused = \"4\", size_summary_difference = \"4\", alpha_raw_reference = \"1\", alpha_raw_comparison = \"1\", alpha_raw_unused = \"1\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_unused = \"1\", alpha_summary_difference = \"1\", linetype_summary_reference = \"solid\", linetype_summary_comparison = \"solid\", linetype_summary_unused = \"solid\", linetype_summary_difference = \"solid\", color_interval_reference = \"black\", color_interval_comparison = \"black\", color_interval_unused = \"gray65\", color_interval_difference = \"black\", size_interval_reference = \"3\", size_interval_comparison = \"3\", size_interval_unused = \"3\", size_interval_difference = \"3\", alpha_interval_reference = \"1\", alpha_interval_comparison = \"1\", alpha_interval_unused = \"1\", alpha_interval_difference = \"1\", alpha_error_reference = \"1\", alpha_error_comparison = \"1\", alpha_error_unused = \"1\", alpha_error_difference = \"1\", fill_error_reference = \"gray75\", fill_error_comparison = \"gray75\", fill_error_unused = \"gray75\", fill_error_difference = \"gray75\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiff2x2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Means and Medians: 2x2 Factorial — jamovimdiff2x2","text":"design . switch . data . outcome_variable . grouping_variable_A . grouping_variable_B . outcome_variable_level1 . outcome_variable_level2 . outcome_variable_name_bs . grouping_variable . repeated_measures_name . outcome_variable_name . A1_label . A2_label . B1_label . B2_label . A_label . B_label . A1B1_mean . A1B1_sd . A1B1_n . A1B2_mean . A1B2_sd . A1B2_n . A2B1_mean . A2B1_sd . A2B1_n . A2B2_mean . A2B2_sd . A2B2_n . conf_level . effect_size . assume_equal_variance . show_details . show_interaction_plot . show_CI . evaluate_hypotheses . null_value . null_boundary . rope_units . alpha . null_color . es_plot_width . es_plot_height . ymin . ymax . ybreaks . ylab . xlab . axis.text.y . axis.title.y . axis.text.x . axis.title.x . simple_contrast_labels . error_layout . error_scale . error_nudge . data_layout . data_spread . difference_axis_units . difference_axis_breaks . shape_raw_reference . shape_raw_comparison . shape_raw_unused . shape_summary_reference . shape_summary_comparison . shape_summary_unused . shape_summary_difference . color_raw_reference . color_raw_comparison . color_raw_unused . color_summary_reference . color_summary_comparison . color_summary_unused . color_summary_difference . fill_raw_reference . fill_raw_comparison . fill_raw_unused . fill_summary_reference . fill_summary_comparison . fill_summary_unused . fill_summary_difference . size_raw_reference . size_raw_comparison . size_raw_unused . size_summary_reference . size_summary_comparison . size_summary_unused . size_summary_difference . alpha_raw_reference . alpha_raw_comparison . alpha_raw_unused . alpha_summary_reference . alpha_summary_comparison . alpha_summary_unused . alpha_summary_difference . linetype_summary_reference . linetype_summary_comparison . linetype_summary_unused . linetype_summary_difference . color_interval_reference . color_interval_comparison . color_interval_unused . color_interval_difference . size_interval_reference . size_interval_comparison . size_interval_unused . size_interval_difference . alpha_interval_reference . alpha_interval_comparison . alpha_interval_unused . alpha_interval_difference . alpha_error_reference . alpha_error_comparison . alpha_error_unused . alpha_error_difference . fill_error_reference . fill_error_comparison . fill_error_unused . fill_error_difference .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiff2x2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Means and Medians: 2x2 Factorial — jamovimdiff2x2","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$overview$asDF .data.frame(results$overview)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiffindcontrast.html","id":null,"dir":"Reference","previous_headings":"","what":"Means and Medians: Independent Groups Contrast — jamovimdiffindcontrast","title":"Means and Medians: Independent Groups Contrast — jamovimdiffindcontrast","text":"Means Medians: Independent Groups Contrast","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiffindcontrast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Means and Medians: Independent Groups Contrast — jamovimdiffindcontrast","text":"","code":"jamovimdiffindcontrast( switch = \"from_raw\", data, outcome_variable, grouping_variable, means, sds, ns, grouping_variable_levels, outcome_variable_name = \"Outcome variable\", grouping_variable_name = \"Grouping variable\", comparison_labels = \" \", reference_labels = \" \", conf_level = 95, effect_size = \"mean_difference\", assume_equal_variance = TRUE, show_details = FALSE, evaluate_hypotheses = FALSE, null_value = \"0\", null_boundary = \"0\", rope_units = \"raw\", alpha = 0.05, null_color = \"#A40122\", es_plot_width = \"550\", es_plot_height = \"450\", ymin = \"auto\", ymax = \"auto\", ybreaks = \"auto\", ylab = \"auto\", xlab = \"auto\", axis.text.y = \"14\", axis.title.y = \"15\", axis.text.x = \"14\", axis.title.x = \"15\", simple_contrast_labels = TRUE, error_layout = \"halfeye\", error_scale = \"0.25\", error_nudge = \"0.5\", data_layout = \"random\", data_spread = \"0.20\", difference_axis_units = \"raw\", difference_axis_breaks = \"auto\", shape_raw_reference = \"circle filled\", shape_raw_comparison = \"circle filled\", shape_raw_unused = \"circle filled\", shape_summary_reference = \"circle filled\", shape_summary_comparison = \"circle filled\", shape_summary_unused = \"circle filled\", shape_summary_difference = \"triangle filled\", color_raw_reference = \"#008DF9\", color_raw_comparison = \"#009F81\", color_raw_unused = \"gray65\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#009F81\", color_summary_unused = \"gray65\", color_summary_difference = \"black\", fill_raw_reference = \"NA\", fill_raw_comparison = \"NA\", fill_raw_unused = \"NA\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#009F81\", fill_summary_unused = \"gray65\", fill_summary_difference = \"black\", size_raw_reference = \"2\", size_raw_comparison = \"2\", size_raw_unused = \"1\", size_summary_reference = \"4\", size_summary_comparison = \"4\", size_summary_unused = \"4\", size_summary_difference = \"4\", alpha_raw_reference = \"1\", alpha_raw_comparison = \"1\", alpha_raw_unused = \"1\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_unused = \"1\", alpha_summary_difference = \"1\", linetype_summary_reference = \"solid\", linetype_summary_comparison = \"solid\", linetype_summary_unused = \"solid\", linetype_summary_difference = \"solid\", color_interval_reference = \"black\", color_interval_comparison = \"black\", color_interval_unused = \"gray65\", color_interval_difference = \"black\", size_interval_reference = \"3\", size_interval_comparison = \"3\", size_interval_unused = \"3\", size_interval_difference = \"3\", alpha_interval_reference = \"1\", alpha_interval_comparison = \"1\", alpha_interval_unused = \"1\", alpha_interval_difference = \"1\", alpha_error_reference = \"1\", alpha_error_comparison = \"1\", alpha_error_unused = \"1\", alpha_error_difference = \"1\", fill_error_reference = \"gray75\", fill_error_comparison = \"gray75\", fill_error_unused = \"gray75\", fill_error_difference = \"gray75\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiffindcontrast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Means and Medians: Independent Groups Contrast — jamovimdiffindcontrast","text":"switch . data . outcome_variable . grouping_variable . means . sds . ns . grouping_variable_levels . outcome_variable_name . grouping_variable_name . comparison_labels . reference_labels . conf_level . effect_size . assume_equal_variance . show_details . evaluate_hypotheses . null_value . null_boundary . rope_units . alpha . null_color . es_plot_width . es_plot_height . ymin . ymax . ybreaks . ylab . xlab . axis.text.y . axis.title.y . axis.text.x . axis.title.x . simple_contrast_labels . error_layout . error_scale . error_nudge . data_layout . data_spread . difference_axis_units . difference_axis_breaks . shape_raw_reference . shape_raw_comparison . shape_raw_unused . shape_summary_reference . shape_summary_comparison . shape_summary_unused . shape_summary_difference . color_raw_reference . color_raw_comparison . color_raw_unused . color_summary_reference . color_summary_comparison . color_summary_unused . color_summary_difference . fill_raw_reference . fill_raw_comparison . fill_raw_unused . fill_summary_reference . fill_summary_comparison . fill_summary_unused . fill_summary_difference . size_raw_reference . size_raw_comparison . size_raw_unused . size_summary_reference . size_summary_comparison . size_summary_unused . size_summary_difference . alpha_raw_reference . alpha_raw_comparison . alpha_raw_unused . alpha_summary_reference . alpha_summary_comparison . alpha_summary_unused . alpha_summary_difference . linetype_summary_reference . linetype_summary_comparison . linetype_summary_unused . linetype_summary_difference . color_interval_reference . color_interval_comparison . color_interval_unused . color_interval_difference . size_interval_reference . size_interval_comparison . size_interval_unused . size_interval_difference . alpha_interval_reference . alpha_interval_comparison . alpha_interval_unused . alpha_interval_difference . alpha_error_reference . alpha_error_comparison . alpha_error_unused . alpha_error_difference . fill_error_reference . fill_error_comparison . fill_error_unused . fill_error_difference .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiffindcontrast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Means and Medians: Independent Groups Contrast — jamovimdiffindcontrast","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$overview$asDF .data.frame(results$overview)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiffpaired.html","id":null,"dir":"Reference","previous_headings":"","what":"Means and Medians: Paired — jamovimdiffpaired","title":"Means and Medians: Paired — jamovimdiffpaired","text":"Means Medians: Paired","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiffpaired.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Means and Medians: Paired — jamovimdiffpaired","text":"","code":"jamovimdiffpaired( switch = \"from_raw\", data, reference_measure, comparison_measure, comparison_mean = \" \", comparison_sd = \" \", reference_mean = \" \", reference_sd = \" \", n = \" \", enter_r_or_sdiff = \"enter_r\", correlation = \" \", sdiff = \" \", comparison_measure_name = \"Comparison measure\", reference_measure_name = \"Reference measure\", conf_level = 95, effect_size = \"mean_difference\", show_ratio = FALSE, show_details = FALSE, show_calculations = FALSE, evaluate_hypotheses = FALSE, null_value = \"0\", null_boundary = \"0\", rope_units = \"raw\", alpha = 0.05, null_color = \"#A40122\", es_plot_width = \"600\", es_plot_height = \"400\", ymin = \"auto\", ymax = \"auto\", ybreaks = \"auto\", ylab = \"auto\", xlab = \"auto\", axis.text.y = \"14\", axis.title.y = \"15\", axis.text.x = \"14\", axis.title.x = \"15\", simple_contrast_labels = TRUE, error_layout = \"halfeye\", error_scale = \"0.25\", error_nudge = \"0.5\", data_layout = \"random\", data_spread = \"0.20\", difference_axis_units = \"raw\", difference_axis_breaks = \"auto\", shape_raw_reference = \"circle filled\", shape_raw_comparison = \"circle filled\", shape_raw_difference = \"triangle filled\", shape_summary_reference = \"circle filled\", shape_summary_comparison = \"circle filled\", shape_summary_difference = \"triangle filled\", color_raw_reference = \"#008DF9\", color_raw_comparison = \"#008DF9\", color_raw_difference = \"#E20134\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#008DF9\", color_summary_difference = \"black\", fill_raw_reference = \"NA\", fill_raw_comparison = \"NA\", fill_raw_difference = \"NA\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#008DF9\", fill_summary_difference = \"black\", size_raw_reference = \"2\", size_raw_comparison = \"2\", size_raw_difference = \"2\", size_summary_reference = \"4\", size_summary_comparison = \"4\", size_summary_difference = \"4\", alpha_raw_reference = \"1\", alpha_raw_comparison = \"1\", alpha_raw_difference = \"1\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_difference = \"1\", linetype_summary_reference = \"solid\", linetype_summary_comparison = \"solid\", linetype_summary_difference = \"solid\", color_interval_reference = \"black\", color_interval_comparison = \"black\", color_interval_difference = \"black\", size_interval_reference = \"3\", size_interval_comparison = \"3\", size_interval_difference = \"3\", alpha_interval_reference = \"1\", alpha_interval_comparison = \"1\", alpha_interval_difference = \"1\", alpha_error_reference = \"1\", alpha_error_comparison = \"1\", alpha_error_difference = \"1\", fill_error_reference = \"gray75\", fill_error_comparison = \"gray75\", fill_error_difference = \"gray75\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiffpaired.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Means and Medians: Paired — jamovimdiffpaired","text":"switch . data . reference_measure . comparison_measure . comparison_mean . comparison_sd . reference_mean . reference_sd . n . enter_r_or_sdiff . correlation . sdiff . comparison_measure_name . reference_measure_name . conf_level . effect_size . show_ratio . show_details . show_calculations . evaluate_hypotheses . null_value . null_boundary . rope_units . alpha . null_color . es_plot_width . es_plot_height . ymin . ymax . ybreaks . ylab . xlab . axis.text.y . axis.title.y . axis.text.x . axis.title.x . simple_contrast_labels . error_layout . error_scale . error_nudge . data_layout . data_spread . difference_axis_units . difference_axis_breaks . shape_raw_reference . shape_raw_comparison . shape_raw_difference . shape_summary_reference . shape_summary_comparison . shape_summary_difference . color_raw_reference . color_raw_comparison . color_raw_difference . color_summary_reference . color_summary_comparison . color_summary_difference . fill_raw_reference . fill_raw_comparison . fill_raw_difference . fill_summary_reference . fill_summary_comparison . fill_summary_difference . size_raw_reference . size_raw_comparison . size_raw_difference . size_summary_reference . size_summary_comparison . size_summary_difference . alpha_raw_reference . alpha_raw_comparison . alpha_raw_difference . alpha_summary_reference . alpha_summary_comparison . alpha_summary_difference . linetype_summary_reference . linetype_summary_comparison . linetype_summary_difference . color_interval_reference . color_interval_comparison . color_interval_difference . size_interval_reference . size_interval_comparison . size_interval_difference . alpha_interval_reference . alpha_interval_comparison . alpha_interval_difference . alpha_error_reference . alpha_error_comparison . alpha_error_difference . fill_error_reference . fill_error_comparison . fill_error_difference .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdiffpaired.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Means and Medians: Paired — jamovimdiffpaired","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$overview$asDF .data.frame(results$overview)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdifftwo.html","id":null,"dir":"Reference","previous_headings":"","what":"Means and Medians: Two Groups — jamovimdifftwo","title":"Means and Medians: Two Groups — jamovimdifftwo","text":"Means Medians: Two Groups","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdifftwo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Means and Medians: Two Groups — jamovimdifftwo","text":"","code":"jamovimdifftwo( switch = \"from_raw\", data, outcome_variable, grouping_variable, reference_level_name = \"Reference group\", reference_mean = \" \", reference_sd = \" \", reference_n = \" \", comparison_level_name = \"Comparison group\", comparison_mean = \" \", comparison_sd = \" \", comparison_n = \" \", outcome_variable_name = \"Outcome variable\", grouping_variable_name = \"Grouping variable\", conf_level = 95, assume_equal_variance = TRUE, effect_size = \"mean_difference\", show_ratio = FALSE, switch_comparison_order = FALSE, show_details = FALSE, show_calculations = FALSE, evaluate_hypotheses = FALSE, null_value = \"0\", null_boundary = \"0\", rope_units = \"raw\", alpha = 0.05, null_color = \"#A40122\", es_plot_width = \"600\", es_plot_height = \"400\", ymin = \"auto\", ymax = \"auto\", ybreaks = \"auto\", ylab = \"auto\", xlab = \"auto\", axis.text.y = \"14\", axis.title.y = \"15\", axis.text.x = \"14\", axis.title.x = \"15\", simple_contrast_labels = TRUE, error_layout = \"halfeye\", error_scale = \"0.25\", error_nudge = \"0.5\", data_layout = \"random\", data_spread = \"0.20\", difference_axis_units = \"raw\", difference_axis_breaks = \"auto\", shape_raw_reference = \"circle filled\", shape_raw_comparison = \"circle filled\", shape_summary_reference = \"circle filled\", shape_summary_comparison = \"circle filled\", shape_summary_difference = \"triangle filled\", color_raw_reference = \"#008DF9\", color_raw_comparison = \"#009F81\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#009F81\", color_summary_difference = \"black\", fill_raw_reference = \"NA\", fill_raw_comparison = \"NA\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#009F81\", fill_summary_difference = \"black\", size_raw_reference = \"2\", size_raw_comparison = \"2\", size_summary_reference = \"4\", size_summary_comparison = \"4\", size_summary_difference = \"4\", alpha_raw_reference = \"1\", alpha_raw_comparison = \"1\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_difference = \"1\", linetype_summary_reference = \"solid\", linetype_summary_comparison = \"solid\", linetype_summary_difference = \"solid\", color_interval_reference = \"black\", color_interval_comparison = \"black\", color_interval_difference = \"black\", size_interval_reference = \"3\", size_interval_comparison = \"3\", size_interval_difference = \"3\", alpha_interval_reference = \"1\", alpha_interval_comparison = \"1\", alpha_interval_difference = \"1\", alpha_error_reference = \"1\", alpha_error_comparison = \"1\", alpha_error_difference = \"1\", fill_error_reference = \"gray75\", fill_error_comparison = \"gray75\", fill_error_difference = \"gray75\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdifftwo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Means and Medians: Two Groups — jamovimdifftwo","text":"switch . data . outcome_variable . grouping_variable . reference_level_name . reference_mean . reference_sd . reference_n . comparison_level_name . comparison_mean . comparison_sd . comparison_n . outcome_variable_name . grouping_variable_name . conf_level . assume_equal_variance . effect_size . show_ratio . switch_comparison_order . show_details . show_calculations . evaluate_hypotheses . null_value . null_boundary . rope_units . alpha . null_color . es_plot_width . es_plot_height . ymin . ymax . ybreaks . ylab . xlab . axis.text.y . axis.title.y . axis.text.x . axis.title.x . simple_contrast_labels . error_layout . error_scale . error_nudge . data_layout . data_spread . difference_axis_units . difference_axis_breaks . shape_raw_reference . shape_raw_comparison . shape_summary_reference . shape_summary_comparison . shape_summary_difference . color_raw_reference . color_raw_comparison . color_summary_reference . color_summary_comparison . color_summary_difference . fill_raw_reference . fill_raw_comparison . fill_summary_reference . fill_summary_comparison . fill_summary_difference . size_raw_reference . size_raw_comparison . size_summary_reference . size_summary_comparison . size_summary_difference . alpha_raw_reference . alpha_raw_comparison . alpha_summary_reference . alpha_summary_comparison . alpha_summary_difference . linetype_summary_reference . linetype_summary_comparison . linetype_summary_difference . color_interval_reference . color_interval_comparison . color_interval_difference . size_interval_reference . size_interval_comparison . size_interval_difference . alpha_interval_reference . alpha_interval_comparison . alpha_interval_difference . alpha_error_reference . alpha_error_comparison . alpha_error_difference . fill_error_reference . fill_error_comparison . fill_error_difference .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimdifftwo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Means and Medians: Two Groups — jamovimdifftwo","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$overview$asDF .data.frame(results$overview)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetamdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Meta-Analysis: Difference in Means — jamovimetamdiff","title":"Meta-Analysis: Difference in Means — jamovimetamdiff","text":"Meta-Analysis: Difference Means","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetamdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Meta-Analysis: Difference in Means — jamovimetamdiff","text":"","code":"jamovimetamdiff( switch = \"from_raw\", data, comparison_means, comparison_sds, comparison_ns, reference_means, reference_sds, reference_ns, r, labels, moderator, d, dcomparison_ns, dreference_ns, dr, dlabels, dmoderator, conf_level = 95, effect_label = \"My effect\", reported_effect_size = \"mean_difference\", assume_equal_variance = TRUE, random_effects = \"random_effects\", include_PIs = FALSE, show_details = FALSE, es_plot_width = \"600\", es_plot_height = \"750\", size_base = \"2\", size_multiplier = \"3\", axis.text.y = \"14\", report_CIs = FALSE, meta_diamond_height = \".25\", xlab = \"auto\", xmin = \"auto\", xmax = \"auto\", xbreaks = \"auto\", mark_zero = TRUE, axis.text.x = \"14\", axis.title.x = \"15\", dlab = \"auto\", dmin = \"auto\", dmax = \"auto\", dbreaks = \"auto\", shape_raw_reference = \"square filled\", shape_raw_comparison = \"square filled\", shape_summary_difference = \"triangle filled\", shape_raw_unused = \"square filled\", color_raw_reference = \"#008DF9\", color_raw_comparison = \"#009F81\", color_raw_unused = \"gray65\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#009F81\", color_summary_unused = \"gray75\", color_summary_difference = \"black\", color_summary_overall = \"black\", fill_raw_reference = \"#008DF9\", fill_raw_comparison = \"#009F81\", fill_raw_unused = \"gray65\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#009F81\", fill_summary_unused = \"gray75\", fill_summary_difference = \"black\", fill_summary_overall = \"black\", alpha_raw_reference = \"1\", alpha_raw_comparison = \"1\", alpha_raw_unused = \"1\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_unused = \"1\", alpha_summary_difference = \"1\", alpha_summary_overall = \"1\", linetype_raw_reference = \"solid\", linetype_raw_comparison = \"solid\", linetype_summary_difference = \"solid\", linetype_raw_unused = \"solid\", color_interval_reference = \"black\", color_interval_comparison = \"black\", color_interval_difference = \"black\", color_interval_unused = \"black\", size_interval_reference = \".50\", size_interval_comparison = \".50\", size_interval_difference = \".50\", size_interval_unused = \".50\", alpha_interval_reference = \"1\", alpha_interval_comparison = \"1\", alpha_interval_difference = \"1\", alpha_interval_unused = \"1\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetamdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Meta-Analysis: Difference in Means — jamovimetamdiff","text":"switch . data . comparison_means . comparison_sds . comparison_ns . reference_means . reference_sds . reference_ns . r . labels . moderator . d . dcomparison_ns . dreference_ns . dr . dlabels . dmoderator . conf_level . effect_label . reported_effect_size . assume_equal_variance . random_effects . include_PIs . show_details . es_plot_width . es_plot_height . size_base . size_multiplier . axis.text.y . report_CIs . meta_diamond_height . xlab . xmin . xmax . xbreaks . mark_zero . axis.text.x . axis.title.x . dlab . dmin . dmax . dbreaks . shape_raw_reference . shape_raw_comparison . shape_summary_difference . shape_raw_unused . color_raw_reference . color_raw_comparison . color_raw_unused . color_summary_reference . color_summary_comparison . color_summary_unused . color_summary_difference . color_summary_overall . fill_raw_reference . fill_raw_comparison . fill_raw_unused . fill_summary_reference . fill_summary_comparison . fill_summary_unused . fill_summary_difference . fill_summary_overall . alpha_raw_reference . alpha_raw_comparison . alpha_raw_unused . alpha_summary_reference . alpha_summary_comparison . alpha_summary_unused . alpha_summary_difference . alpha_summary_overall . linetype_raw_reference . linetype_raw_comparison . linetype_summary_difference . linetype_raw_unused . color_interval_reference . color_interval_comparison . color_interval_difference . color_interval_unused . size_interval_reference . size_interval_comparison . size_interval_difference . size_interval_unused . alpha_interval_reference . alpha_interval_comparison . alpha_interval_difference . alpha_interval_unused .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetamdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Meta-Analysis: Difference in Means — jamovimetamdiff","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$raw_data$asDF .data.frame(results$raw_data)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetamean.html","id":null,"dir":"Reference","previous_headings":"","what":"Meta-Analysis: Means — jamovimetamean","title":"Meta-Analysis: Means — jamovimetamean","text":"Meta-Analysis: Means","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetamean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Meta-Analysis: Means — jamovimetamean","text":"","code":"jamovimetamean( switch = \"from_raw\", data, means, sds, ns, labels, moderator, ds, dns, dlabels, dmoderator, conf_level = 95, effect_label = \"My effect\", reference_mean = \"\", reported_effect_size = \"mean_difference\", random_effects = \"random_effects\", include_PIs = FALSE, show_details = FALSE, es_plot_width = \"600\", es_plot_height = \"750\", size_base = \"2\", size_multiplier = \"3\", axis.text.y = \"14\", report_CIs = FALSE, meta_diamond_height = \".25\", xlab = \"auto\", xmin = \"auto\", xmax = \"auto\", xbreaks = \"auto\", mark_zero = TRUE, axis.text.x = \"14\", axis.title.x = \"15\", dlab = \"auto\", dmin = \"auto\", dmax = \"auto\", dbreaks = \"auto\", shape_raw_reference = \"square filled\", shape_raw_comparison = \"square filled\", shape_summary_difference = \"triangle filled\", shape_raw_unused = \"square filled\", color_raw_reference = \"#008DF9\", color_raw_comparison = \"#009F81\", color_raw_unused = \"gray65\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#009F81\", color_summary_unused = \"gray75\", color_summary_difference = \"black\", color_summary_overall = \"black\", fill_raw_reference = \"#008DF9\", fill_raw_comparison = \"#009F81\", fill_raw_unused = \"gray65\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#009F81\", fill_summary_unused = \"gray75\", fill_summary_difference = \"black\", fill_summary_overall = \"black\", alpha_raw_reference = \"1\", alpha_raw_comparison = \"1\", alpha_raw_unused = \"1\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_unused = \"1\", alpha_summary_difference = \"1\", alpha_summary_overall = \"1\", linetype_raw_reference = \"solid\", linetype_raw_comparison = \"solid\", linetype_summary_difference = \"solid\", linetype_raw_unused = \"solid\", color_interval_reference = \"black\", color_interval_comparison = \"black\", color_interval_difference = \"black\", color_interval_unused = \"black\", size_interval_reference = \".50\", size_interval_comparison = \".50\", size_interval_difference = \".50\", size_interval_unused = \".50\", alpha_interval_reference = \"1\", alpha_interval_comparison = \"1\", alpha_interval_difference = \"1\", alpha_interval_unused = \"1\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetamean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Meta-Analysis: Means — jamovimetamean","text":"switch . data . means . sds . ns . labels . moderator . ds . dns . dlabels . dmoderator . conf_level . effect_label . reference_mean . reported_effect_size . random_effects . include_PIs . show_details . es_plot_width . es_plot_height . size_base . size_multiplier . axis.text.y . report_CIs . meta_diamond_height . xlab . xmin . xmax . xbreaks . mark_zero . axis.text.x . axis.title.x . dlab . dmin . dmax . dbreaks . shape_raw_reference . shape_raw_comparison . shape_summary_difference . shape_raw_unused . color_raw_reference . color_raw_comparison . color_raw_unused . color_summary_reference . color_summary_comparison . color_summary_unused . color_summary_difference . color_summary_overall . fill_raw_reference . fill_raw_comparison . fill_raw_unused . fill_summary_reference . fill_summary_comparison . fill_summary_unused . fill_summary_difference . fill_summary_overall . alpha_raw_reference . alpha_raw_comparison . alpha_raw_unused . alpha_summary_reference . alpha_summary_comparison . alpha_summary_unused . alpha_summary_difference . alpha_summary_overall . linetype_raw_reference . linetype_raw_comparison . linetype_summary_difference . linetype_raw_unused . color_interval_reference . color_interval_comparison . color_interval_difference . color_interval_unused . size_interval_reference . size_interval_comparison . size_interval_difference . size_interval_unused . alpha_interval_reference . alpha_interval_comparison . alpha_interval_difference . alpha_interval_unused .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetamean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Meta-Analysis: Means — jamovimetamean","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$raw_data$asDF .data.frame(results$raw_data)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetapdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Meta-Analysis: Difference in Proportions — jamovimetapdiff","title":"Meta-Analysis: Difference in Proportions — jamovimetapdiff","text":"Meta-Analysis: Difference Proportions","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetapdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Meta-Analysis: Difference in Proportions — jamovimetapdiff","text":"","code":"jamovimetapdiff( data, reference_cases, reference_ns, comparison_cases, comparison_ns, labels, moderator, effect_label = \"My effect\", reported_effect_size = \"RD\", conf_level = 95, random_effects = \"random_effects\", include_PIs = FALSE, show_details = FALSE, es_plot_width = \"600\", es_plot_height = \"750\", size_base = \"2\", size_multiplier = \"3\", axis.text.y = \"14\", report_CIs = FALSE, meta_diamond_height = \".25\", xlab = \"auto\", xmin = \"auto\", xmax = \"auto\", xbreaks = \"auto\", mark_zero = FALSE, axis.text.x = \"14\", axis.title.x = \"15\", dlab = \"auto\", dmin = \"auto\", dmax = \"auto\", dbreaks = \"auto\", shape_raw_reference = \"square filled\", shape_raw_comparison = \"square filled\", shape_summary_difference = \"triangle filled\", shape_raw_unused = \"square filled\", color_raw_reference = \"#008DF9\", color_raw_comparison = \"#009F81\", color_raw_unused = \"gray65\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#009F81\", color_summary_unused = \"gray75\", color_summary_difference = \"black\", color_summary_overall = \"black\", fill_raw_reference = \"#008DF9\", fill_raw_comparison = \"#009F81\", fill_raw_unused = \"gray65\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#009F81\", fill_summary_unused = \"gray75\", fill_summary_difference = \"black\", fill_summary_overall = \"black\", alpha_raw_reference = \"1\", alpha_raw_comparison = \"1\", alpha_raw_unused = \"1\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_unused = \"1\", alpha_summary_difference = \"1\", alpha_summary_overall = \"1\", linetype_raw_reference = \"solid\", linetype_raw_comparison = \"solid\", linetype_summary_difference = \"solid\", linetype_raw_unused = \"solid\", color_interval_reference = \"black\", color_interval_comparison = \"black\", color_interval_difference = \"black\", color_interval_unused = \"black\", size_interval_reference = \".50\", size_interval_comparison = \".50\", size_interval_difference = \".50\", size_interval_unused = \".50\", alpha_interval_reference = \"1\", alpha_interval_comparison = \"1\", alpha_interval_difference = \"1\", alpha_interval_unused = \"1\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetapdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Meta-Analysis: Difference in Proportions — jamovimetapdiff","text":"data . reference_cases . reference_ns . comparison_cases . comparison_ns . labels . moderator . effect_label . reported_effect_size . conf_level . random_effects . include_PIs . show_details . es_plot_width . es_plot_height . size_base . size_multiplier . axis.text.y . report_CIs . meta_diamond_height . xlab . xmin . xmax . xbreaks . mark_zero . axis.text.x . axis.title.x . dlab . dmin . dmax . dbreaks . shape_raw_reference . shape_raw_comparison . shape_summary_difference . shape_raw_unused . color_raw_reference . color_raw_comparison . color_raw_unused . color_summary_reference . color_summary_comparison . color_summary_unused . color_summary_difference . color_summary_overall . fill_raw_reference . fill_raw_comparison . fill_raw_unused . fill_summary_reference . fill_summary_comparison . fill_summary_unused . fill_summary_difference . fill_summary_overall . alpha_raw_reference . alpha_raw_comparison . alpha_raw_unused . alpha_summary_reference . alpha_summary_comparison . alpha_summary_unused . alpha_summary_difference . alpha_summary_overall . linetype_raw_reference . linetype_raw_comparison . linetype_summary_difference . linetype_raw_unused . color_interval_reference . color_interval_comparison . color_interval_difference . color_interval_unused . size_interval_reference . size_interval_comparison . size_interval_difference . size_interval_unused . alpha_interval_reference . alpha_interval_comparison . alpha_interval_difference . alpha_interval_unused .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetapdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Meta-Analysis: Difference in Proportions — jamovimetapdiff","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$raw_data$asDF .data.frame(results$raw_data)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetaproportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Meta-Analysis: Proportions — jamovimetaproportion","title":"Meta-Analysis: Proportions — jamovimetaproportion","text":"Meta-Analysis: Proportions","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetaproportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Meta-Analysis: Proportions — jamovimetaproportion","text":"","code":"jamovimetaproportion( data, cases, ns, labels, moderator, effect_label = \"My effect\", conf_level = 95, random_effects = \"random_effects\", include_PIs = FALSE, show_details = FALSE, es_plot_width = \"600\", es_plot_height = \"750\", size_base = \"2\", size_multiplier = \"3\", axis.text.y = \"14\", report_CIs = FALSE, meta_diamond_height = \".25\", xlab = \"auto\", xmin = \"auto\", xmax = \"auto\", xbreaks = \"auto\", mark_zero = FALSE, axis.text.x = \"14\", axis.title.x = \"15\", dlab = \"auto\", dmin = \"auto\", dmax = \"auto\", dbreaks = \"auto\", shape_raw_reference = \"square filled\", shape_raw_comparison = \"square filled\", shape_summary_difference = \"triangle filled\", shape_raw_unused = \"square filled\", color_raw_reference = \"#008DF9\", color_raw_comparison = \"#009F81\", color_raw_unused = \"gray65\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#009F81\", color_summary_unused = \"gray75\", color_summary_difference = \"black\", color_summary_overall = \"black\", fill_raw_reference = \"#008DF9\", fill_raw_comparison = \"#009F81\", fill_raw_unused = \"gray65\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#009F81\", fill_summary_unused = \"gray75\", fill_summary_difference = \"black\", fill_summary_overall = \"black\", alpha_raw_reference = \"1\", alpha_raw_comparison = \"1\", alpha_raw_unused = \"1\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_unused = \"1\", alpha_summary_difference = \"1\", alpha_summary_overall = \"1\", linetype_raw_reference = \"solid\", linetype_raw_comparison = \"solid\", linetype_summary_difference = \"solid\", linetype_raw_unused = \"solid\", color_interval_reference = \"black\", color_interval_comparison = \"black\", color_interval_difference = \"black\", color_interval_unused = \"black\", size_interval_reference = \".50\", size_interval_comparison = \".50\", size_interval_difference = \".50\", size_interval_unused = \".50\", alpha_interval_reference = \"1\", alpha_interval_comparison = \"1\", alpha_interval_difference = \"1\", alpha_interval_unused = \"1\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetaproportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Meta-Analysis: Proportions — jamovimetaproportion","text":"data . cases . ns . labels . moderator . effect_label . conf_level . random_effects . include_PIs . show_details . es_plot_width . es_plot_height . size_base . size_multiplier . axis.text.y . report_CIs . meta_diamond_height . xlab . xmin . xmax . xbreaks . mark_zero . axis.text.x . axis.title.x . dlab . dmin . dmax . dbreaks . shape_raw_reference . shape_raw_comparison . shape_summary_difference . shape_raw_unused . color_raw_reference . color_raw_comparison . color_raw_unused . color_summary_reference . color_summary_comparison . color_summary_unused . color_summary_difference . color_summary_overall . fill_raw_reference . fill_raw_comparison . fill_raw_unused . fill_summary_reference . fill_summary_comparison . fill_summary_unused . fill_summary_difference . fill_summary_overall . alpha_raw_reference . alpha_raw_comparison . alpha_raw_unused . alpha_summary_reference . alpha_summary_comparison . alpha_summary_unused . alpha_summary_difference . alpha_summary_overall . linetype_raw_reference . linetype_raw_comparison . linetype_summary_difference . linetype_raw_unused . color_interval_reference . color_interval_comparison . color_interval_difference . color_interval_unused . size_interval_reference . size_interval_comparison . size_interval_difference . size_interval_unused . alpha_interval_reference . alpha_interval_comparison . alpha_interval_difference . alpha_interval_unused .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetaproportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Meta-Analysis: Proportions — jamovimetaproportion","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$raw_data$asDF .data.frame(results$raw_data)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetar.html","id":null,"dir":"Reference","previous_headings":"","what":"Meta-Analysis: Correlations — jamovimetar","title":"Meta-Analysis: Correlations — jamovimetar","text":"Meta-Analysis: Correlations","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Meta-Analysis: Correlations — jamovimetar","text":"","code":"jamovimetar( data, rs, ns, labels, moderator, effect_label = \"My effect\", conf_level = 95, random_effects = \"random_effects\", include_PIs = FALSE, show_details = FALSE, es_plot_width = \"600\", es_plot_height = \"750\", size_base = \"2\", size_multiplier = \"3\", axis.text.y = \"14\", report_CIs = FALSE, meta_diamond_height = \".25\", xlab = \"auto\", xmin = \"-1\", xmax = \"1\", xbreaks = \"auto\", mark_zero = TRUE, axis.text.x = \"14\", axis.title.x = \"15\", dlab = \"auto\", dmin = \"auto\", dmax = \"auto\", dbreaks = \"auto\", shape_raw_reference = \"square filled\", shape_raw_comparison = \"square filled\", shape_summary_difference = \"triangle filled\", shape_raw_unused = \"square filled\", color_raw_reference = \"#008DF9\", color_raw_comparison = \"#009F81\", color_raw_unused = \"gray65\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#009F81\", color_summary_unused = \"gray75\", color_summary_difference = \"black\", color_summary_overall = \"black\", fill_raw_reference = \"#008DF9\", fill_raw_comparison = \"#009F81\", fill_raw_unused = \"gray65\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#009F81\", fill_summary_unused = \"gray75\", fill_summary_difference = \"black\", fill_summary_overall = \"black\", alpha_raw_reference = \"1\", alpha_raw_comparison = \"1\", alpha_raw_unused = \"1\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_unused = \"1\", alpha_summary_difference = \"1\", alpha_summary_overall = \"1\", linetype_raw_reference = \"solid\", linetype_raw_comparison = \"solid\", linetype_summary_difference = \"solid\", linetype_raw_unused = \"solid\", color_interval_reference = \"black\", color_interval_comparison = \"black\", color_interval_difference = \"black\", color_interval_unused = \"black\", size_interval_reference = \".50\", size_interval_comparison = \".50\", size_interval_difference = \".50\", size_interval_unused = \".50\", alpha_interval_reference = \"1\", alpha_interval_comparison = \"1\", alpha_interval_difference = \"1\", alpha_interval_unused = \"1\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Meta-Analysis: Correlations — jamovimetar","text":"data . rs . ns . labels . moderator . effect_label . conf_level . random_effects . include_PIs . show_details . es_plot_width . es_plot_height . size_base . size_multiplier . axis.text.y . report_CIs . meta_diamond_height . xlab . xmin . xmax . xbreaks . mark_zero . axis.text.x . axis.title.x . dlab . dmin . dmax . dbreaks . shape_raw_reference . shape_raw_comparison . shape_summary_difference . shape_raw_unused . color_raw_reference . color_raw_comparison . color_raw_unused . color_summary_reference . color_summary_comparison . color_summary_unused . color_summary_difference . color_summary_overall . fill_raw_reference . fill_raw_comparison . fill_raw_unused . fill_summary_reference . fill_summary_comparison . fill_summary_unused . fill_summary_difference . fill_summary_overall . alpha_raw_reference . alpha_raw_comparison . alpha_raw_unused . alpha_summary_reference . alpha_summary_comparison . alpha_summary_unused . alpha_summary_difference . alpha_summary_overall . linetype_raw_reference . linetype_raw_comparison . linetype_summary_difference . linetype_raw_unused . color_interval_reference . color_interval_comparison . color_interval_difference . color_interval_unused . size_interval_reference . size_interval_comparison . size_interval_difference . size_interval_unused . alpha_interval_reference . alpha_interval_comparison . alpha_interval_difference . alpha_interval_unused .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovimetar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Meta-Analysis: Correlations — jamovimetar","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$raw_data$asDF .data.frame(results$raw_data)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovipdiffpaired.html","id":null,"dir":"Reference","previous_headings":"","what":"Proportions: Paired — jamovipdiffpaired","title":"Proportions: Paired — jamovipdiffpaired","text":"Proportions: Paired","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovipdiffpaired.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Proportions: Paired — jamovipdiffpaired","text":"","code":"jamovipdiffpaired( switch = \"from_raw\", data, reference_measure, comparison_measure, cases_consistent = \" \", cases_inconsistent = \" \", not_cases_consistent = \" \", not_cases_inconsistent = \" \", case_label = \"Sick\", not_case_label = \"Well\", comparison_measure_name = \"Post-test\", reference_measure_name = \"Pre-test\", conf_level = 95, show_details = FALSE, evaluate_hypotheses = FALSE, null_value = \"0\", null_boundary = \"0\", alpha = 0.05, null_color = \"#A40122\", es_plot_width = \"400\", es_plot_height = \"450\", ymin = \"auto\", ymax = \"auto\", ybreaks = \"auto\", ylab = \"auto\", xlab = \"auto\", axis.text.y = \"14\", axis.title.y = \"15\", axis.text.x = \"14\", axis.title.x = \"15\", simple_contrast_labels = TRUE, error_layout = \"none\", difference_axis_breaks = \"auto\", shape_summary_reference = \"circle filled\", shape_summary_comparison = \"circle filled\", shape_summary_difference = \"triangle filled\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#009F81\", color_summary_difference = \"black\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#009F81\", fill_summary_difference = \"black\", size_summary_reference = \"4\", size_summary_comparison = \"4\", size_summary_difference = \"4\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_difference = \"1\", linetype_summary_reference = \"solid\", linetype_summary_comparison = \"solid\", linetype_summary_difference = \"solid\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovipdiffpaired.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Proportions: Paired — jamovipdiffpaired","text":"switch . data . reference_measure . comparison_measure . cases_consistent . cases_inconsistent . not_cases_consistent . not_cases_inconsistent . case_label . not_case_label . comparison_measure_name . reference_measure_name . conf_level . show_details . evaluate_hypotheses . null_value . null_boundary . alpha . null_color . es_plot_width . es_plot_height . ymin . ymax . ybreaks . ylab . xlab . axis.text.y . axis.title.y . axis.text.x . axis.title.x . simple_contrast_labels . error_layout . difference_axis_breaks . shape_summary_reference . shape_summary_comparison . shape_summary_difference . color_summary_reference . color_summary_comparison . color_summary_difference . fill_summary_reference . fill_summary_comparison . fill_summary_difference . size_summary_reference . size_summary_comparison . size_summary_difference . alpha_summary_reference . alpha_summary_comparison . alpha_summary_difference . linetype_summary_reference . linetype_summary_comparison . linetype_summary_difference .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovipdiffpaired.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Proportions: Paired — jamovipdiffpaired","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$overview$asDF .data.frame(results$overview)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovipdifftwo.html","id":null,"dir":"Reference","previous_headings":"","what":"Proportions: Two Groups — jamovipdifftwo","title":"Proportions: Two Groups — jamovipdifftwo","text":"Proportions: Two Groups","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovipdifftwo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Proportions: Two Groups — jamovipdifftwo","text":"","code":"jamovipdifftwo( switch = \"from_raw\", data, outcome_variable, grouping_variable, comparison_cases = \" \", comparison_not_cases = \" \", reference_cases = \" \", reference_not_cases = \" \", case_label = \"Sick\", not_case_label = \"Well\", grouping_variable_level1 = \"Treated\", grouping_variable_level2 = \"Control\", outcome_variable_name = \"Outcome variable\", grouping_variable_name = \"Grouping variable\", count_NA = FALSE, show_ratio = FALSE, show_chi_square = FALSE, chi_table_option = \"both\", show_phi = FALSE, conf_level = 95, show_details = FALSE, evaluate_hypotheses = FALSE, null_value = \"0\", null_boundary = \"0\", alpha = 0.05, null_color = \"#A40122\", es_plot_width = \"400\", es_plot_height = \"450\", ymin = \"auto\", ymax = \"auto\", ybreaks = \"auto\", ylab = \"auto\", xlab = \"auto\", axis.text.y = \"14\", axis.title.y = \"15\", axis.text.x = \"14\", axis.title.x = \"15\", simple_contrast_labels = TRUE, error_layout = \"none\", difference_axis_breaks = \"auto\", shape_summary_reference = \"circle filled\", shape_summary_comparison = \"circle filled\", shape_summary_difference = \"triangle filled\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#009F81\", color_summary_difference = \"black\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#009F81\", fill_summary_difference = \"black\", size_summary_reference = \"4\", size_summary_comparison = \"4\", size_summary_difference = \"4\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_difference = \"1\", linetype_summary_reference = \"solid\", linetype_summary_comparison = \"solid\", linetype_summary_difference = \"solid\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovipdifftwo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Proportions: Two Groups — jamovipdifftwo","text":"switch . data . outcome_variable . grouping_variable . comparison_cases . comparison_not_cases . reference_cases . reference_not_cases . case_label . not_case_label . grouping_variable_level1 . grouping_variable_level2 . outcome_variable_name . grouping_variable_name . count_NA . show_ratio . show_chi_square . chi_table_option . show_phi . conf_level . show_details . evaluate_hypotheses . null_value . null_boundary . alpha . null_color . es_plot_width . es_plot_height . ymin . ymax . ybreaks . ylab . xlab . axis.text.y . axis.title.y . axis.text.x . axis.title.x . simple_contrast_labels . error_layout . difference_axis_breaks . shape_summary_reference . shape_summary_comparison . shape_summary_difference . color_summary_reference . color_summary_comparison . color_summary_difference . fill_summary_reference . fill_summary_comparison . fill_summary_difference . size_summary_reference . size_summary_comparison . size_summary_difference . alpha_summary_reference . alpha_summary_comparison . alpha_summary_difference . linetype_summary_reference . linetype_summary_comparison . linetype_summary_difference .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovipdifftwo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Proportions: Two Groups — jamovipdifftwo","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$overview$asDF .data.frame(results$overview)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamoviproportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Proportions: Single Group — jamoviproportion","title":"Proportions: Single Group — jamoviproportion","text":"Proportions: Single Group","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamoviproportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Proportions: Single Group — jamoviproportion","text":"","code":"jamoviproportion( switch = \"from_raw\", data, outcome_variable, cases = \" \", not_cases = \" \", case_label = \"Affected\", not_case_label = \"Not Affected\", outcome_variable_name = \"My outcome variable\", count_NA = FALSE, conf_level = 95, show_details = FALSE, plot_possible = FALSE, evaluate_hypotheses = FALSE, null_value = \"0\", null_boundary = \"0\", alpha = 0.05, null_color = \"#A40122\", es_plot_width = \"300\", es_plot_height = \"400\", ymin = \"0\", ymax = \"1\", breaks = \"auto\", ylab = \"auto\", xlab = \"auto\", axis.text.y = \"14\", axis.title.y = \"15\", axis.text.x = \"14\", axis.title.x = \"15\", error_layout = \"none\", shape_summary = \"circle filled\", color_summary = \"#008DF9\", fill_summary = \"#008DF9\", size_summary = \"4\", alpha_summary = \"1\", linetype_summary = \"solid\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamoviproportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Proportions: Single Group — jamoviproportion","text":"switch . data . outcome_variable . cases . not_cases . case_label . not_case_label . outcome_variable_name . count_NA . conf_level . show_details . plot_possible . evaluate_hypotheses . null_value . null_boundary . alpha . null_color . es_plot_width . es_plot_height . ymin . ymax . breaks . ylab . xlab . axis.text.y . axis.title.y . axis.text.x . axis.title.x . error_layout . shape_summary . color_summary . fill_summary . size_summary . alpha_summary . linetype_summary .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamoviproportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Proportions: Single Group — jamoviproportion","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$overview$asDF .data.frame(results$overview)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovirdifftwo.html","id":null,"dir":"Reference","previous_headings":"","what":"Correlations: Two Groups — jamovirdifftwo","title":"Correlations: Two Groups — jamovirdifftwo","text":"Correlations: Two Groups","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovirdifftwo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Correlations: Two Groups — jamovirdifftwo","text":"","code":"jamovirdifftwo( switch = \"from_raw\", data, x, y, grouping_variable, comparison_r = \" \", comparison_n = \" \", reference_r = \" \", reference_n = \" \", x_variable_name = \"X variable\", y_variable_name = \"Y variable\", comparison_level_name = \"Comparison level\", reference_level_name = \"Reference level\", grouping_variable_name = \"Grouping variable\", conf_level = 95, show_details = FALSE, show_line = TRUE, show_line_CI = TRUE, evaluate_hypotheses = FALSE, null_value = \"0\", null_boundary = \"0\", alpha = 0.05, null_color = \"#A40122\", es_plot_width = \"600\", es_plot_height = \"400\", sp_plot_width = \"650\", sp_plot_height = \"650\", ymin = \"auto\", ymax = \"auto\", ybreaks = \"auto\", ylab = \"auto\", xlab = \"auto\", axis.text.y = \"14\", axis.title.y = \"15\", axis.text.x = \"14\", axis.title.x = \"15\", sp_ymin = \"auto\", sp_ymax = \"auto\", sp_ybreaks = \"auto\", sp_xmin = \"auto\", sp_xmax = \"auto\", sp_xbreaks = \"auto\", sp_ylab = \"auto\", sp_xlab = \"auto\", sp_axis.text.y = \"14\", sp_axis.title.y = \"15\", sp_axis.text.x = \"14\", sp_axis.title.x = \"15\", difference_axis_breaks = \"auto\", shape_summary_reference = \"circle filled\", shape_summary_comparison = \"circle filled\", shape_summary_difference = \"triangle filled\", color_summary_reference = \"#008DF9\", color_summary_comparison = \"#009F81\", color_summary_difference = \"black\", fill_summary_reference = \"#008DF9\", fill_summary_comparison = \"#009F81\", fill_summary_difference = \"black\", size_summary_reference = \"4\", size_summary_comparison = \"4\", size_summary_difference = \"4\", alpha_summary_reference = \"1\", alpha_summary_comparison = \"1\", alpha_summary_difference = \"1\", linetype_summary_reference = \"solid\", linetype_summary_comparison = \"solid\", linetype_summary_difference = \"solid\", sp_shape_raw_reference = \"circle filled\", sp_shape_raw_comparison = \"circle filled\", sp_shape_raw_unused = \"circle filled\", sp_color_raw_reference = \"black\", sp_color_raw_comparison = \"black\", sp_color_raw_unused = \"black\", sp_fill_raw_reference = \"#008DF9\", sp_fill_raw_comparison = \"#009F81\", sp_fill_raw_unused = \"NA\", sp_size_raw_reference = \"3\", sp_size_raw_comparison = \"3\", sp_size_raw_unused = \"2\", sp_alpha_raw_reference = \".25\", sp_alpha_raw_comparison = \".25\", sp_alpha_raw_unused = \".25\", sp_linetype_summary_reference = \"solid\", sp_linetype_summary_comparison = \"solid\", sp_color_summary_reference = \"#008DF9\", sp_color_summary_comparison = \"#009F81\", sp_size_summary_reference = \"2\", sp_size_summary_comparison = \"2\", sp_alpha_summary_reference = \".25\", sp_alpha_summary_comparison = \".25\" )"},{"path":"https://rcalinjageman.github.io/esci/reference/jamovirdifftwo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Correlations: Two Groups — jamovirdifftwo","text":"switch . data . x . y . grouping_variable . comparison_r . comparison_n . reference_r . reference_n . x_variable_name . y_variable_name . comparison_level_name . reference_level_name . grouping_variable_name . conf_level . show_details . show_line . show_line_CI . evaluate_hypotheses . null_value . null_boundary . alpha . null_color . es_plot_width . es_plot_height . sp_plot_width . sp_plot_height . ymin . ymax . ybreaks . ylab . xlab . axis.text.y . axis.title.y . axis.text.x . axis.title.x . sp_ymin . sp_ymax . sp_ybreaks . sp_xmin . sp_xmax . sp_xbreaks . sp_ylab . sp_xlab . sp_axis.text.y . sp_axis.title.y . sp_axis.text.x . sp_axis.title.x . difference_axis_breaks . shape_summary_reference . shape_summary_comparison . shape_summary_difference . color_summary_reference . color_summary_comparison . color_summary_difference . fill_summary_reference . fill_summary_comparison . fill_summary_difference . size_summary_reference . size_summary_comparison . size_summary_difference . alpha_summary_reference . alpha_summary_comparison . alpha_summary_difference . linetype_summary_reference . linetype_summary_comparison . linetype_summary_difference . sp_shape_raw_reference . sp_shape_raw_comparison . sp_shape_raw_unused . sp_color_raw_reference . sp_color_raw_comparison . sp_color_raw_unused . sp_fill_raw_reference . sp_fill_raw_comparison . sp_fill_raw_unused . sp_size_raw_reference . sp_size_raw_comparison . sp_size_raw_unused . sp_alpha_raw_reference . sp_alpha_raw_comparison . sp_alpha_raw_unused . sp_linetype_summary_reference . sp_linetype_summary_comparison . sp_color_summary_reference . sp_color_summary_comparison . sp_size_summary_reference . sp_size_summary_comparison . sp_alpha_summary_reference . sp_alpha_summary_comparison .","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jamovirdifftwo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Correlations: Two Groups — jamovirdifftwo","text":"results object containing: Tables can converted data frames asDF .data.frame. example: results$overview$asDF .data.frame(results$overview)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jesci_document_data.html","id":null,"dir":"Reference","previous_headings":"","what":"also promote kind, altruistic behavior. Brethel-Haurwitz and Marsh (2014)\r\nexamined this idea by collecting data on U.S. states. A Gallup poll in 2010\r\nwas used to measure each state's well-being index, a measure of mean\r\nhappiness for the state's residents on a scale from 0 to 100. Next, a kidney\r\ndonation database for 1999-2010 was used to figure out each state's rate\r\n(number of donations per 1 million people) of non-directed kidney\r\ndonations-that's giving one kidney to a stranger, an extremely generous and\r\naltruistic thing to do!\r\ntheir classic study, Jacowitz and Kahneman (1995) asked participants to\r\nestimate how many babies are born each day in the United States. Participants\r\nwere given either a low anchor (more than 100 babies/day) or a high anchor\r\n(less than 50,000 babies/day). Those who saw the low anchor estimated many\r\nfewer births/day than those who saw the high anchor, which suggests that the\r\nwording can have a profound influence. The correct answer, as it happens, is\r\n~11,000 births/day in 2014. To investigate the extent that these results are\r\nreplicable, the Many Labs project repeated this classic study at many\r\ndifferent labs around the world. You can find the summary data for 30 of\r\nthese labs in the Anchor Estimate ma data file\r\nnumerous other issues can be highly damaging, but are thriving in this social\r\nmedia age. Trying to debunk a conspiracy theory by presenting facts and\r\nevidence often doesn't work, alas. Psychological inoculation, also similar to\r\nprebunking, presents a mild form of misinformation, preferably with\r\nexplanation, in the hope of building resistance to real-life fake news-a sort\r\nof vaccine for fake news. The Bad News game is a spin-off from research on\r\npsychological inoculation. Basol et al. (2020) assessed the possible\r\neffectiveness of this game as a fake news vaccine. At getbadnews.com you can\r\nclick 'About' for information, or just start playing the game- it's easy and\r\nmaybe even fun. You encounter mock Twitter (now X) fake news messages that\r\nillustrate common strategies for making fake news memorable or believable.\r\nYou make choices between messages and decide which ones to 'forward' as you\r\ntry to spread fake news while building your credibility score and number of\r\n'followers'-rather like real life for a conspiracy theorist wanting to spread\r\nthe word. Compete with your friends for credibility and number of followers.\r\nBasol's online participants first saw 18 fictitious fake news tweets and\r\nrated each for reliability (accuracy, believability), and also rated their\r\nconfidence in that reliability rating. Both ratings were on a 1 to 7 scale.\r\nThose in the BadNews group then played the game for 15 minutes, whereas those\r\nin the Control group played Tetris. Then all once again gave reliability and\r\nconfidence ratings for the 18 tweets.\r\ndecade earlier, had been one of several outside experts invited to scrutinize\r\nthe laboratory and experimental procedures of parapsychology researcher\r\nCharles Honorton. Bem not only judged them adequate, but joined the research\r\neffort and became a coauthor with Honorton. Bem and Honorton (1994) first\r\nreviewed early ganzfeld studies and described how the experimental procedure\r\nhad been improved to reduce the chance that results could be influenced by\r\nvarious possible biases, or leakages of information from sender to receiver.\r\nFor example, the randomization procedure was carried out automatically by\r\ncomputer, and all stimuli were presented under computer control. Bem and\r\nHonorton then presented data from studies conducted with the improved\r\nprocedure. Table 13.1 presents basic data from 10 studies reported by Bem and\r\nHonorton (1994). Participants each made a single judgment, so in Pilot 1, for\r\nexample, 22 participants responded, with 8 of them giving a correct response.\r\nThree pilot studies helped refine the procedures, then four studies used\r\nnovice receivers. Study 5 used 20 students of music, drama, or dance as\r\nreceivers, in response to suggestions that creative people might be more\r\nlikely to show telepathy. Studies 6 and 7 used receivers who had participated\r\nin an earlier study. The proportion of hits expected by chance is .25, and\r\nTable 13.1 shows that all but Study 1 found proportions higher than .25.\r\nwho identified as female. Data is Subjective Wellbeing abd Body Satisfaction.\r\nwho identified as male. Data is Subjective Wellbeing abd Body Satisfaction.\r\nReported are measures of Subjective Wellbeing abd Body Satisfaction.\r\nFictituous data from an unrealistically small HEAT\r\nstudy comparing scores for a single group of students before and after a\r\nworkshop on climate change.\r\nSome researchers claim that moral judgments are based not only on rational\r\nconsiderations but also on one's current emotional state. To what extent can\r\nrecent emotional experiences influence moral judgments? Schnall et al. (2008)\r\nexamined this question by manipulating feelings of cleanliness and purity and\r\nthen observing the extent that this changes how harshly participants judge\r\nthe morality of others. Inscho Study 1, Schnall et al. asked participants to\r\ncomplete a word scramble task with either neutral words (neutral prime) or\r\nwords related to cleanliness (cleanliness prime). All students then completed\r\na set of moral judgments about controversial scenarios: Moral judgment is the\r\naverage of six items, each rated on a scale from 0 to 9, with high meaning\r\nharsh. The data from this study are in the Clean moral file, which also\r\ncontains data from a replication by Johnson et al. (2014) — jesci_document_data","title":"also promote kind, altruistic behavior. Brethel-Haurwitz and Marsh (2014)\r\nexamined this idea by collecting data on U.S. states. A Gallup poll in 2010\r\nwas used to measure each state's well-being index, a measure of mean\r\nhappiness for the state's residents on a scale from 0 to 100. Next, a kidney\r\ndonation database for 1999-2010 was used to figure out each state's rate\r\n(number of donations per 1 million people) of non-directed kidney\r\ndonations-that's giving one kidney to a stranger, an extremely generous and\r\naltruistic thing to do!\r\ntheir classic study, Jacowitz and Kahneman (1995) asked participants to\r\nestimate how many babies are born each day in the United States. Participants\r\nwere given either a low anchor (more than 100 babies/day) or a high anchor\r\n(less than 50,000 babies/day). Those who saw the low anchor estimated many\r\nfewer births/day than those who saw the high anchor, which suggests that the\r\nwording can have a profound influence. The correct answer, as it happens, is\r\n~11,000 births/day in 2014. To investigate the extent that these results are\r\nreplicable, the Many Labs project repeated this classic study at many\r\ndifferent labs around the world. You can find the summary data for 30 of\r\nthese labs in the Anchor Estimate ma data file\r\nnumerous other issues can be highly damaging, but are thriving in this social\r\nmedia age. Trying to debunk a conspiracy theory by presenting facts and\r\nevidence often doesn't work, alas. Psychological inoculation, also similar to\r\nprebunking, presents a mild form of misinformation, preferably with\r\nexplanation, in the hope of building resistance to real-life fake news-a sort\r\nof vaccine for fake news. The Bad News game is a spin-off from research on\r\npsychological inoculation. Basol et al. (2020) assessed the possible\r\neffectiveness of this game as a fake news vaccine. At getbadnews.com you can\r\nclick 'About' for information, or just start playing the game- it's easy and\r\nmaybe even fun. You encounter mock Twitter (now X) fake news messages that\r\nillustrate common strategies for making fake news memorable or believable.\r\nYou make choices between messages and decide which ones to 'forward' as you\r\ntry to spread fake news while building your credibility score and number of\r\n'followers'-rather like real life for a conspiracy theorist wanting to spread\r\nthe word. Compete with your friends for credibility and number of followers.\r\nBasol's online participants first saw 18 fictitious fake news tweets and\r\nrated each for reliability (accuracy, believability), and also rated their\r\nconfidence in that reliability rating. Both ratings were on a 1 to 7 scale.\r\nThose in the BadNews group then played the game for 15 minutes, whereas those\r\nin the Control group played Tetris. Then all once again gave reliability and\r\nconfidence ratings for the 18 tweets.\r\ndecade earlier, had been one of several outside experts invited to scrutinize\r\nthe laboratory and experimental procedures of parapsychology researcher\r\nCharles Honorton. Bem not only judged them adequate, but joined the research\r\neffort and became a coauthor with Honorton. Bem and Honorton (1994) first\r\nreviewed early ganzfeld studies and described how the experimental procedure\r\nhad been improved to reduce the chance that results could be influenced by\r\nvarious possible biases, or leakages of information from sender to receiver.\r\nFor example, the randomization procedure was carried out automatically by\r\ncomputer, and all stimuli were presented under computer control. Bem and\r\nHonorton then presented data from studies conducted with the improved\r\nprocedure. Table 13.1 presents basic data from 10 studies reported by Bem and\r\nHonorton (1994). Participants each made a single judgment, so in Pilot 1, for\r\nexample, 22 participants responded, with 8 of them giving a correct response.\r\nThree pilot studies helped refine the procedures, then four studies used\r\nnovice receivers. Study 5 used 20 students of music, drama, or dance as\r\nreceivers, in response to suggestions that creative people might be more\r\nlikely to show telepathy. Studies 6 and 7 used receivers who had participated\r\nin an earlier study. The proportion of hits expected by chance is .25, and\r\nTable 13.1 shows that all but Study 1 found proportions higher than .25.\r\nwho identified as female. Data is Subjective Wellbeing abd Body Satisfaction.\r\nwho identified as male. Data is Subjective Wellbeing abd Body Satisfaction.\r\nReported are measures of Subjective Wellbeing abd Body Satisfaction.\r\nFictituous data from an unrealistically small HEAT\r\nstudy comparing scores for a single group of students before and after a\r\nworkshop on climate change.\r\nSome researchers claim that moral judgments are based not only on rational\r\nconsiderations but also on one's current emotional state. To what extent can\r\nrecent emotional experiences influence moral judgments? Schnall et al. (2008)\r\nexamined this question by manipulating feelings of cleanliness and purity and\r\nthen observing the extent that this changes how harshly participants judge\r\nthe morality of others. Inscho Study 1, Schnall et al. asked participants to\r\ncomplete a word scramble task with either neutral words (neutral prime) or\r\nwords related to cleanliness (cleanliness prime). All students then completed\r\na set of moral judgments about controversial scenarios: Moral judgment is the\r\naverage of six items, each rated on a scale from 0 to 9, with high meaning\r\nharsh. The data from this study are in the Clean moral file, which also\r\ncontains data from a replication by Johnson et al. (2014) — jesci_document_data","text":"psychological behavioral measures. various psychological behavioral measures. performance. first study (Damisch 1) used golf putting task. Students experimental group told using lucky ball (Lucky group). Calin-Jageman Caldwell (2014) reported two studies designed replicate Damisch 1. first (RCJ 1) followed Damisch 1 closely practical, although participants American rather German college students. second (RCJ 2) made several changes designed increase effect superstition putting performance. 138 U.S. adults, recruited August 2018 Prolific Academic, worked online. First, saw six fake headlines four times, time asked rate interesting/engaging/ funny/well-written headline . rating task simply ensured participants paid attention headline. stimuli 12 actual fake-news headlines American politics, accompanying photographs. Half appealed Republicans half Democrats. Later, 12 fake headlines presented one time, random mix six Old headlines-seen -six New headlines seen previously. stated clearly independent, non-partisan fact-checking established headlines true. Participants first rated, 0 () 100 (extremely) scale, degree judged unethical publish headline. Unethicality DV. also rated likely share headline saw posted acquaintance social media; three similar ratings. Finally, rated accurate believed headline . heart rate? investigate, Lakens (2013) asked students record heart rate (beats per minute) rest (baseline) recalling time intense anger. conceptual replication classic study Ekman et al. (1983). Load Emotion heartrate data set book website. extent initial performance class relate performance final exam? First exam final exam scores nine students enrolled introductory psychology course. Exam scores percentages, 0 = answers correct 100 = answers correct. Data synthetic represent patterns found previous psych stats course. extent exposed American flag influence political attitudes? One seminal study (Carter et al., 2011) explored issue subtly exposing participants either images American flag control images. Next, participants asked political attitudes, using 1-7 rating scale high scores indicate conservative attitudes. Participants exposed flag found express substantially conservative attitudes. Many Labs project replicated finding 25 different locations United States. mathematics? investigate, Nosek et al. (2002) asked male female students complete Implicit Association Test (IAT)-task designed measure participant's implicit (non-conscious) feelings towards topic. (never heard IAT, try : tiny.cc/harvardiat) IAT, students tested negative feelings towards mathematics art. Scores reflect degree student negative implicit attitudes mathematics art (positive score: negative feelings mathematics; 0: level negativity ; negative score: negative feelings art). data_gender_math_iat data two labs participated large-scale replication original study (Klein et al., 2014a, 2014b) al. (2002), male female participants completed Implicit Association Test (IAT) measured extent negative attitudes towards mathematics, compared art. study found women, compared men, tended negative implicit attitudes towards mathematics. Many Labs project repeated study locations around world (Klein et al., 2014a, 2014b). Summary data 30 labs available Gender math IAT ma. Higher scores indicate implicit bias mathematics. See also data_gender_math_iat raw data two specific sites replication effort. eating much less delay Alzheimer's? , great news. Halagappa et al. (2007) investigated possibility using mouse model, meaning used Alzheimer-prone mice, genetically predisposed develop neural degeneration typical Alzheimer's. researchers used six independent groups mice, three tested mouse middle age 10 months old, three mouse old age 17 months. age control group normal mice ate freely (NFree10 NFree17 groups), group Alzheimer-prone mice also ate freely (AFree10 AFree17 groups), another Alzheimer-prone group restricted 40% less food normal (ADiet10 ADiet17 groups). Table 14.2 lists factors define groups, group labels. discuss one measure mouse cognition: percent time spent near target water maze, higher values indicating better learning memory. Table 14.2 reports means standard deviations measure, group sizes. Maybe thinking buying house college? Regression can help hunt bargain. Download Home Prices data set. file contains real estate listings 1997 2003 city California. explore extent size home (square meters) predicts sale price. Suppose want change battery phone, cook perfect souffle, perform three-ball juggle. Just numerous people every day, might search online find video shows . Suppose watch video just . First question: well predict perform task? Second question: well actually perform task, first time tried? Now suppose watch video many times: consider two questions. questions investigated series studies Kardas O'Brien (2018). first quick analyses Kardas Experiments 3 4-call Expt 3 Expt 4-focusing effect watching video many times rather . Expt 3, participants first watched brief video person performing moonwalk. Low Exposure group watched video , High Exposure group 20 times. participants predicted, 1 10 scale, well felt able perform moonwalk . Finally, attempted single performance moonwalk, videoed. videos rated, 1 10 scale, independent raters. souffle, perform three-ball juggle. Just numerous people every day, might search online find video shows . Suppose watch video just . First question: well predict perform task? Second question: well actually perform task, first time tried? Now suppose watch video many times: consider two questions. questions investigated series studies Kardas O'Brien (2018). first quick analyses Kardas Experiments 3 4-call Expt 3 Expt 4-focusing effect watching video many times rather . Expt 4 conducted online participants recruited Amazon's Mechanical Turk, typically diverse students. online task based mirror-drawing game developed Bob students (Cusack et al., 2015, tiny.cc/bobmirrortrace). Participants first read description game scoring procedure. play, use computer trackpad trace target line, accurately quickly can. task tricky can see mirror image path tracing finger trackpad. running score displayed. final score percentage match target line path traced, scores can range 0 100 investigate, participants asked participate taste test. participants actually given grape juice, one glass poured bottle labeled 'Organic' glass bottle labeled 'Generic'. tasting (counterbalanced order), participants asked rate much enjoyed juice scale 1 () 10 (much). Participants also asked say much willing pay large container juice scale $1 $10. Load Labels flavor data set book website. data collected part class project Floretta-Schiller et al. (2015), whose work inspired clever study looking effects fast-food wrappers children's enjoyment food (Robinson et al., 2007). researchers interested different study approaches might impact learning. Working France, created three independent groups, comprising 95 adults. Participants worked online seven learning modules DNA. Reread group worked module, worked second time going next module. Quiz group worked module, complete quiz going next module. Prequiz group work quiz seeing presentation module, went quiz presentation next module. Participants received feedback brief explanation answering question quiz, take long wished work module quiz. Seven days later, participants completed final test. data_latimier_3groups full data set. facilitate different student exercises, also seperate data entities group (data_latimier_prequiz, data_latimier_reread, etc.), every pair groups (data_latimier_quiz_prequiz, etc.). Just Prequiz group Latimier et al., 2019 See full details data_latimier_3_groups Just Quiz (RQ) Prequiz (QR) groups Latimier et al., 2019 See full details data_latimier_3_groups Just Quiz group Latimier et al., 2019 See full details data_latimier_3_groups Just Reread (RR) Prequiz (QR) groups Latimier et al., 2019 See full details data_latimier_3_groups Just Reread Quiz groups Latimier et al., 2019 See full details data_latimier_3_groups Just Reread group Latimier et al., 2019 See full details data_latimier_3_groups worked sufficiently hard develop requisite skills? Meta-analysis correlations can help answer questions. issue extent practice effort may sufficient achieving highest levels expertise. Ericsson et al. (1993) argued years effort matters : 'Many characteristics believed reflect innate talent actually result intense practice extended minimum 10 years' (p. 363). view enormously popularized Malcolm Gladwell (2008), argued book Outliers 10,000 hours focused practice key achieving expertise. However, view now challenged, one important contribution large meta-analysis correlations amount intense practice level achievement: Macnamara et al. (2014) combined 157 correlations reported wide range fields, sports music education, found correlation r = .35 .30, .39. Table 11.1 shows 16 main correlations music, Macnamara et al. (2014). brightly colored areas indicating brain regions active particular type cognition emotion. Search online fMRI (functional magnetic resonance imaging) brain scans see pictures learn made. can fascinating-last able see thinking works? 2008, McCabe Castel published studies investigated adding brain picture might alter judgments credibility scientific article. one group participants, article accompanied brain image irrelevant article. second, independent group, image. Participants read article, gave rating statement 'scientific reasoning article made sense'. response options 1 (strongly disagree), 2 (disagree), 3 (agree), 4 (strongly agree). researchers reported mean ratings higher brain picture without, difference small. seemed irrelevant brain picture may , small influence. authors drew appropriately cautious conclusions, result quickly attracted attention many media reports greatly overstated . least according popular media, seemed adding brain picture made story convincing. Search 'McCabe seeing believing', similar, find media reports blog posts. warned readers watch brain pictures, , said, can trick believing things true. result intrigued New Zealander colleagues mine discovered , despite wide recognition, finding replicated. ran replication studies using materials used original researchers, found generally small ESs. joined team data analysis stage research published (Michael et al., 2013). discuss meta-analysis two original studies eight replications team. studies sufficiently similar meta-analysis, especially considering Michael studies designed many features matched original studies. data set include two additional critique studies run Michael team. See also data_mccabemichael_brain2 run Michael team. (2011). People wanted reduce stress, experienced meditators, assigned Meditation (n = 16) Control (n = 17) group. Meditation group participated 8 weeks intensive training practice mindfulness meditation. researchers used questionnaire assess range emotional cognitive variables (Pretest) (Posttest) 8-week period. assessment conducted participants meditating. study notable including brain imaging assess possible changes participants' brains Pretest Posttest. researchers measured gray matter concentration, increases brain regions experience higher frequent activation. researchers expected hippocampus may especially responsive meditation implicated regulation emotion, arousal, general responsiveness. therefore included planned analysis assessment changes gray matter concentration hippocampus. extent might choosing organic foods make us morally smug? investigate, Eskine (2013) asked participants rate images organic food, neutral (control) food, comfort food. Next, guise different study, participants completed moral judgment scale read different controversial scenarios rated morally wrong judged (scale 1-7, high judgments mean wrong). Table 14.7 shows summary data, also available first four variables OrganicMoral file. file can see two variables, report full data-come shortly. use summary data. results Eskine (2013) published, Moery Calin-Jageman (2016) conducted series close replications. obtained original materials Eskine, piloted procedure, preregistered sampling analysis plan. OSF page, osf.io/atkn7, details. data one close replications last two variables OrganicMoral file. replication study, group names variable ReplicationGroup moral judgments MoralJudgment. (may need scroll right see variables.) skills? investigate, Burgmer Englich (2012) assigned German participants either power control conditions asked play golf (Experiment 1) darts (Experiment 2). found participants manipulated feel powerful performed substantially better control condition. study finding , Cusack et al. (2015) conducted five replications United States. Across replications tried different ways manipulating power, different types tasks (golf, mirror tracing, cognitive task), different levels difficulty, different types participant pools (undergraduates online). Summary data seven studies available PowerPerformance ma. reassurance, , instead, encouragement challenge? Carol Dweck colleagues investigated many questions people respond different types feedback. next example comes Dweck's research group illustrates data analysis starts full data, rather summary statistics. Rattan et al. (2012) asked college student participants imagine undertaking mathematics course just received low score (65%) first test year. Participants assigned randomly three groups, received different feedback along low score. Comfort group received positive encouragement also reassurance, Challenge group received positive encouragement also challenge, Control group received just positive encouragement. Participants responded range questions felt course professor. discuss data ratings motivation toward mathematics, made received feedback. childhood? investigate, large international sample children asked play game given 10 stickers asked give stickers away another child able tested day. number stickers donated considered measure altruistic sharing. addition, parents child reported family's religion. Summary data provided. data large online survey participants asked report, scale 0 100, belief existence God. Age also reported. explanations material studying. Self-explaining generally found effective standard studying, may also take time. raises question whether study strategy extra time benefits learning. explore issue, grade school children took pretest mathematics conceptual knowledge, studied mathematics problems, took similar posttest (McEldoon et al., 2013). Participants randomly assigned one two study conditions: normal study + practice (Practice group), self-explaining (Self-Explain group). first condition intended make time spent learning similar two groups. can find part data study SelfExplain, scores percent correct. program, now job develop ad campaign. choose BeforeAfter pair pictures, Figure 14.1, top panel? might Progressive sequence pictures person, bottom panel, effective? Pause, think, discuss. choose, ? might think BeforeAfter simpler dramatic. hand, Progressive highlights steady improvement claim program deliver. probably surprised learn BeforeAfter used often long favorite advertising industry, whereas Progressive used rarely. Luca Cian colleagues (Cian et al., 2020) curious know extent BeforeAfter actually effective, appealing, credible Progressive, , indeed, whether Progressive might score highly. reported seven studies various aspects question. focus Study 2, used three independent groups compare three conditions illustrated Figure 14.1. BeforeAfterInfo condition, middle panel, comprises three BeforeAfter pairs, thus providing extra information endpoints. researchers included condition case advantage Progressive might stem simply images, rather illustrates clear progressive sequence. randomly assigned 213 participants MTurk one three groups. Participants asked 'imagine decided lose weight', saw one three ads weight loss program called MRMDiets. answered question 'evaluate MRMDiets?' choosing 1-7 response several scales, including Unlikeable-Likable, Ineffective-Effective, credible-Credible. researchers averaged six scores give overall Credibility score, 1-7 scale, 7 credible. Simmons Nelson (2020) sufficiently intrigued carry two substantial close replications. cooperation original researchers, used materials procedure. used much larger groups preregistered research plan, including data analysis plan. focus first replication, 761 participants MTurk randomized three groups. decided examine extent sleep relates attractiveness. 70 college students self-reported amount sleep night . addition, photograph taken participant rated attractiveness scale 1 10 two judges opposite gender. average rating score used. can download data set (Sleep Beauty) book website. Stickgold et al. (2000) found , remarkably, performance visual discrimination task actually improved 48-96 hours initial training, even without practice time. However, participants sleep deprived period? trained 11 participants new skill, sleep deprived. data (-14.7, -10.7, -10.7, 2.2, 2.4, 4.5, 7.2, 9.6, 10, 21.3, 21.8)-download Stickgold data set book website. data changes performance scores immediately training night without sleep: 0 represents change, positive scores represent improvement, negative scores represent decline. Data set courtesy DataCrunch (tiny.cc/Stickgold) extent study strategy influence learning? investigate, psychology students randomly assigned three groups asked learn biology facts using one three different strategies: ) Self-Explain (explaining fact new knowledge gained relates already known), b) Elab Interrogation (elaborative interrogation: stating fact makes sense), c) Repetition Control (stating fact ). studying, students took 25-point fill--blank test (O'Reilly et al., 1998) interested ways enhance students' critical thinking. investigating argument mapping, promising way use diagrams represent structure arguments. Students study completed established test critical thinking (Pretest), critical thinking course based argument mapping, second version test (Posttest). might factors cause aggressive behavior? explore, Hilgard (2015) asked male participants play one four versions video game 15 minutes. game customized vary violence (shooting zombies helping aliens) difficulty (targets controlled tough AI dumb AI). game, players provoked given insulting evaluation confederate. Participants got decide long confederate hold hand painfully cold ice water (0 80 seconds), taken measure aggressive behavior. can find materials analysis plan study Open Science Framework: osf. io/cwenz. simplified version full data set.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/jesci_document_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"also promote kind, altruistic behavior. Brethel-Haurwitz and Marsh (2014)\r\nexamined this idea by collecting data on U.S. states. A Gallup poll in 2010\r\nwas used to measure each state's well-being index, a measure of mean\r\nhappiness for the state's residents on a scale from 0 to 100. Next, a kidney\r\ndonation database for 1999-2010 was used to figure out each state's rate\r\n(number of donations per 1 million people) of non-directed kidney\r\ndonations-that's giving one kidney to a stranger, an extremely generous and\r\naltruistic thing to do!\r\ntheir classic study, Jacowitz and Kahneman (1995) asked participants to\r\nestimate how many babies are born each day in the United States. Participants\r\nwere given either a low anchor (more than 100 babies/day) or a high anchor\r\n(less than 50,000 babies/day). Those who saw the low anchor estimated many\r\nfewer births/day than those who saw the high anchor, which suggests that the\r\nwording can have a profound influence. The correct answer, as it happens, is\r\n~11,000 births/day in 2014. To investigate the extent that these results are\r\nreplicable, the Many Labs project repeated this classic study at many\r\ndifferent labs around the world. You can find the summary data for 30 of\r\nthese labs in the Anchor Estimate ma data file\r\nnumerous other issues can be highly damaging, but are thriving in this social\r\nmedia age. Trying to debunk a conspiracy theory by presenting facts and\r\nevidence often doesn't work, alas. Psychological inoculation, also similar to\r\nprebunking, presents a mild form of misinformation, preferably with\r\nexplanation, in the hope of building resistance to real-life fake news-a sort\r\nof vaccine for fake news. The Bad News game is a spin-off from research on\r\npsychological inoculation. Basol et al. (2020) assessed the possible\r\neffectiveness of this game as a fake news vaccine. At getbadnews.com you can\r\nclick 'About' for information, or just start playing the game- it's easy and\r\nmaybe even fun. You encounter mock Twitter (now X) fake news messages that\r\nillustrate common strategies for making fake news memorable or believable.\r\nYou make choices between messages and decide which ones to 'forward' as you\r\ntry to spread fake news while building your credibility score and number of\r\n'followers'-rather like real life for a conspiracy theorist wanting to spread\r\nthe word. Compete with your friends for credibility and number of followers.\r\nBasol's online participants first saw 18 fictitious fake news tweets and\r\nrated each for reliability (accuracy, believability), and also rated their\r\nconfidence in that reliability rating. Both ratings were on a 1 to 7 scale.\r\nThose in the BadNews group then played the game for 15 minutes, whereas those\r\nin the Control group played Tetris. Then all once again gave reliability and\r\nconfidence ratings for the 18 tweets.\r\ndecade earlier, had been one of several outside experts invited to scrutinize\r\nthe laboratory and experimental procedures of parapsychology researcher\r\nCharles Honorton. Bem not only judged them adequate, but joined the research\r\neffort and became a coauthor with Honorton. Bem and Honorton (1994) first\r\nreviewed early ganzfeld studies and described how the experimental procedure\r\nhad been improved to reduce the chance that results could be influenced by\r\nvarious possible biases, or leakages of information from sender to receiver.\r\nFor example, the randomization procedure was carried out automatically by\r\ncomputer, and all stimuli were presented under computer control. Bem and\r\nHonorton then presented data from studies conducted with the improved\r\nprocedure. Table 13.1 presents basic data from 10 studies reported by Bem and\r\nHonorton (1994). Participants each made a single judgment, so in Pilot 1, for\r\nexample, 22 participants responded, with 8 of them giving a correct response.\r\nThree pilot studies helped refine the procedures, then four studies used\r\nnovice receivers. Study 5 used 20 students of music, drama, or dance as\r\nreceivers, in response to suggestions that creative people might be more\r\nlikely to show telepathy. Studies 6 and 7 used receivers who had participated\r\nin an earlier study. The proportion of hits expected by chance is .25, and\r\nTable 13.1 shows that all but Study 1 found proportions higher than .25.\r\nwho identified as female. Data is Subjective Wellbeing abd Body Satisfaction.\r\nwho identified as male. Data is Subjective Wellbeing abd Body Satisfaction.\r\nReported are measures of Subjective Wellbeing abd Body Satisfaction.\r\nFictituous data from an unrealistically small HEAT\r\nstudy comparing scores for a single group of students before and after a\r\nworkshop on climate change.\r\nSome researchers claim that moral judgments are based not only on rational\r\nconsiderations but also on one's current emotional state. To what extent can\r\nrecent emotional experiences influence moral judgments? Schnall et al. (2008)\r\nexamined this question by manipulating feelings of cleanliness and purity and\r\nthen observing the extent that this changes how harshly participants judge\r\nthe morality of others. Inscho Study 1, Schnall et al. asked participants to\r\ncomplete a word scramble task with either neutral words (neutral prime) or\r\nwords related to cleanliness (cleanliness prime). All students then completed\r\na set of moral judgments about controversial scenarios: Moral judgment is the\r\naverage of six items, each rated on a scale from 0 to 9, with high meaning\r\nharsh. The data from this study are in the Clean moral file, which also\r\ncontains data from a replication by Johnson et al. (2014) — jesci_document_data","text":"","code":"jesci_document_data(save_files = FALSE)"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_any.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate any meta effect. — meta_any","title":"Estimate any meta effect. — meta_any","text":"meta_any suitable synthesizing effect size across multiple studies. must provide effect size study predicted sampling variance study.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_any.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate any meta effect. — meta_any","text":"","code":"meta_any( data, yi, vi, labels = NULL, moderator = NULL, contrast = NULL, effect_label = \"My effect\", effect_size_name = \"Effect size\", moderator_variable_name = \"My moderator\", random_effects = TRUE, conf_level = 0.95 )"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_any.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate any meta effect. — meta_any","text":"data data frame tibble columns yi Name column data containing effect size study vi Name column data containing expected sampling variance study labels Name column data containing label study moderator Optional name column data containing factor categorical moderator contrast Optional vector specifying contrast analysis categorical moderator. define moderator defined; vector length match number levels moderator effect_label Optional human-friendly name effect synthesized; defaults 'effect' effect_size_name Optional human-friendly name effect size synthesized; defaults 'Effect size' moderator_variable_name Optional human-friendly name moderator, defined; passed moderator defined, set quoted name moderator column 'moderator' random_effects Use TRUE obtain random effect meta-anlaysis (usually reccomended); FALSE fixed effect. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_any.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate any meta effect. — meta_any","text":"esci-estimate object; list data frames properties. Returned tables include: es_meta - data frame meta-analytic effect sizes. moderator defined, additional row level moderator. effect_label - Study label effect_size - Effect size LL - Lower bound conf_level% confidence interval UL - Upper bound conf_level% confidence interval SE - Expected standard error k - Number studies diamond_ratio - ratio random fixed effects meta-analtics effect sizes diamond_ratio_LL - lower bound conf_level% confidence interval diamond ratio diamond_ratio_UL - upper bound conf_level% confidence interval diamond ratio I2 - I2 measure heterogeneity I2_LL - Lower bound conf_level% confidence interval I2 I2_UL - upper bound conf_level% confidence interval I2 PI_LL - lower bound conf_level% prediction interval PI_UL - upper bound conf_level% prediction interval p - p value meta-analytic effect size, based null exactly 0 *width - width effect-size confidence interval FE_effect_size - effect size fixed-effects model (regardless fixed effects selected RE_effect_size - effect size random-effects model (regardless random effects selected FE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio RE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio es_heterogeneity - data frame heterogeneity values conf_level% CIs meta-analytic effect size. moderator defined also reports heterogeneity estimates level moderator. effect_label - study label moderator_variable_name - moderator passed, gives name moderator moderator_level - 'Overall' level moderator, passed measure - Name measure heterogeneity estimate - Value heterogeneity estimate LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval raw_data - data one row study passed label - study label effect_size - effect size weight - study weight meta analysis sample_variance - expected level sampling variation SE - expected standard error LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval mean - used calculate study p value; d value entered study sd - use calculate study p value; set 1 study n - study sample size p - p value study, based null exactly 0","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_any.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate any meta effect. — meta_any","text":"#' generate estimate function, can visualize plot_meta(). meta-analytic effect size, confidence interval heterogeneity estimates come metafor::rma(). diamond ratio confidence interval come CI_diamond_ratio().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d1.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate ameta-analytic Cohen's d1 across multiple studies — meta_d1","title":"Estimate ameta-analytic Cohen's d1 across multiple studies — meta_d1","text":"meta_d1 suitable synthesizing across multiple single-group studies continuous outcome variable, outcome measured scale studies","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate ameta-analytic Cohen's d1 across multiple studies — meta_d1","text":"","code":"meta_d1( data, ds, ns, labels = NULL, moderator = NULL, contrast = NULL, effect_label = \"My effect\", random_effects = TRUE, conf_level = 0.95 )"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate ameta-analytic Cohen's d1 across multiple studies — meta_d1","text":"data data frame tibble ds Set bias-adjusted cohen's d1 values, 1 study ns Set sample sizes, positive integers, 1 study labels Optional set labels, 1 study moderator Optional factor categorical moderator; k > 2 per group contrast Optional vector specifying contrast moderator levels effect_label Optional character providing human-friendly label effect random_effects Boolean; TRUE random effects model; otherwise fixed effects conf_level confidence level confidence interval. Given decimal form. Defaults 0.95.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate ameta-analytic Cohen's d1 across multiple studies — meta_d1","text":"esci-estimate object; list data frames properties. Returned tables include: es_meta - data frame meta-analytic effect sizes. moderator defined, additional row level moderator. effect_label - Study label effect_size - Effect size LL - Lower bound conf_level% confidence interval UL - Upper bound conf_level% confidence interval SE - Expected standard error k - Number studies diamond_ratio - ratio random fixed effects meta-analtics effect sizes diamond_ratio_LL - lower bound conf_level% confidence interval diamond ratio diamond_ratio_UL - upper bound conf_level% confidence interval diamond ratio I2 - I2 measure heterogeneity I2_LL - Lower bound conf_level% confidence interval I2 I2_UL - upper bound conf_level% confidence interval I2 PI_LL - lower bound conf_level% prediction interval PI_UL - upper bound conf_level% prediction interval p - p value meta-analytic effect size, based null exactly 0 *width - width effect-size confidence interval FE_effect_size - effect size fixed-effects model (regardless fixed effects selected RE_effect_size - effect size random-effects model (regardless random effects selected FE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio RE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio es_heterogeneity - data frame heterogeneity values conf_level% CIs meta-analytic effect size. moderator defined also reports heterogeneity estimates level moderator. effect_label - study label moderator_variable_name - moderator passed, gives name moderator moderator_level - 'Overall' level moderator, passed measure - Name measure heterogeneity estimate - Value heterogeneity estimate LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval raw_data - data one row study passed label - study label effect_size - effect size weight - study weight meta analysis sample_variance - expected level sampling variation SE - expected standard error LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval mean - used calculate study p value; d value entered study sd - use calculate study p value; set 1 study n - study sample size p - p value study, based null exactly 0","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d1.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate ameta-analytic Cohen's d1 across multiple studies — meta_d1","text":"generate estimate function, can visualize plot_meta(). study's effect size expressed Cohen's d1: (mean - reference) / sd. d1 values corrected bias. function CI_smd_one() can assist converting raw data study d1_unbiased. meta-analytic effect size, confidence interval heterogeneity estimates come metafor::rma(). diamond ratio confidence interval come CI_diamond_ratio().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d1.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate ameta-analytic Cohen's d1 across multiple studies — meta_d1","text":"","code":"# example code original_7 <- data.frame( study_name = c( \"Aden (1993)\" , \"Buggs (1995)\" , \"Crazed (1999)\" , \"Dudley (2003)\" , \"Evers (2005)\" , \"Fox (2009)\", \"Mine (2011)\" ), rt_mean = c( 454 , 317 , 430 , 525 , 479 , 387, 531 ), rt_sd = c( 142 , 158 , 137 , 260 , 144 , 165, 233 ), rt_n = c( 24 , 7 , 20 , 8 , 14 , 13, 18 ), subset = as.factor( c( \"90s\", \"90s\", \"90s\", \"00s\", \"00s\", \"00s\", \"00s\" ) ), d1_unbiased = c( 3.091587, 1.742751, 3.012857, 1.793487, 3.130074, 2.195209, 2.17667 ) ) # Fixed effect, 95% CI estimate <- esci::meta_d1( original_7, d1_unbiased, rt_n, study_name, random_effects = FALSE ) estimate <- esci::meta_d1( data = original_7, ds = d1_unbiased, ns = rt_n, moderator = subset, labels = study_name, random_effects = FALSE ) if (FALSE) { # Forest plot esci::plot_meta(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d2.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate meta-analytic standardized mean difference across multiple\r\ntwo group studies (all paired, all independent, or a mix). — meta_d2","title":"Estimate meta-analytic standardized mean difference across multiple\r\ntwo group studies (all paired, all independent, or a mix). — meta_d2","text":"meta_d2 suitable synthesizing across multiple two-group studies (paired independent) continuous outcome measure studies measured scale, instead magnitude difference study expressed d_s d_avg.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate meta-analytic standardized mean difference across multiple\r\ntwo group studies (all paired, all independent, or a mix). — meta_d2","text":"","code":"meta_d2( data, ds, comparison_ns, reference_ns, r = NULL, labels = NULL, moderator = NULL, contrast = NULL, effect_label = \"My effect\", assume_equal_variance = FALSE, random_effects = TRUE, conf_level = 0.95 )"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate meta-analytic standardized mean difference across multiple\r\ntwo group studies (all paired, all independent, or a mix). — meta_d2","text":"data data frame tibble ds Set bias-adjusted cohen's d_s d_avg values, 1 study comparison_ns Set comparison_group sample sizes, positive integers, 1 study reference_ns Set reference_groups sample sizes, positive integers, 1 study r optional correlation measures w-s studies, NA otherwise labels Optional set labels, 1 study moderator Optional factor categorical moderator; k > 2 per group contrast Optional vector specifying contrast moderator levels effect_label Optional character providing human-friendly label effect assume_equal_variance Defaults FALSE random_effects Boolean; TRUE random effects model; otherwise fixed effects conf_level confidence level confidence interval. Given decimal form. Defaults 0.95.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate meta-analytic standardized mean difference across multiple\r\ntwo group studies (all paired, all independent, or a mix). — meta_d2","text":"esci-estimate object; list data frames properties. Returned tables include: es_meta - data frame meta-analytic effect sizes. moderator defined, additional row level moderator. effect_label - Study label effect_size - Effect size LL - Lower bound conf_level% confidence interval UL - Upper bound conf_level% confidence interval SE - Expected standard error k - Number studies diamond_ratio - ratio random fixed effects meta-analtics effect sizes diamond_ratio_LL - lower bound conf_level% confidence interval diamond ratio diamond_ratio_UL - upper bound conf_level% confidence interval diamond ratio I2 - I2 measure heterogeneity I2_LL - Lower bound conf_level% confidence interval I2 I2_UL - upper bound conf_level% confidence interval I2 PI_LL - lower bound conf_level% prediction interval PI_UL - upper bound conf_level% prediction interval p - p value meta-analytic effect size, based null exactly 0 *width - width effect-size confidence interval FE_effect_size - effect size fixed-effects model (regardless fixed effects selected RE_effect_size - effect size random-effects model (regardless random effects selected FE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio RE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio es_heterogeneity - data frame heterogeneity values conf_level% CIs meta-analytic effect size. moderator defined also reports heterogeneity estimates level moderator. effect_label - study label moderator_variable_name - moderator passed, gives name moderator moderator_level - 'Overall' level moderator, passed measure - Name measure heterogeneity estimate - Value heterogeneity estimate LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval raw_data - data one row study passed label - study label effect_size - effect size weight - study weight meta analysis sample_variance - expected level sampling variation SE - expected standard error LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval mean - used calculate study p value; d value entered study sd - use calculate study p value; set 1 study n - study sample size p - p value study, based null exactly 0","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate meta-analytic standardized mean difference across multiple\r\ntwo group studies (all paired, all independent, or a mix). — meta_d2","text":"generate estimate function, can visualize plot_meta(). study's effect size expressed : Cohen's d_s: (comparison_mean - reference_mean) / sd_pooled Cohen_'s d_avg: (comparison_mean - reference_mean) / sd_avg enter d_s, set assume_equal_variance TRUE enter d_avg, set assume_equal_variance FALSE d values corrected bias. function CI_smd_ind_contrast() can assist converting raw data study d_s d_avg bias correction. als details calculation forms d CIs. meta-analytic effect size, confidence interval heterogeneity estimates come metafor::rma(). diamond ratio confidence interval come CI_diamond_ratio().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_d2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate meta-analytic standardized mean difference across multiple\r\ntwo group studies (all paired, all independent, or a mix). — meta_d2","text":"","code":"# Data set -- see Introduction to the New Statistics, 1st edition lucky_golf <- data.frame( study = c(paste(\"Damisch\", seq(1:6)), \"Calin 1\", \"Calin 2\"), my_smd = c(0.83, 0.986, 0.66, 0.78, 0.979, 0.86, 0.05, 0.047), smd_corrected = c(0.806, 0.963, 0.647, 0.758, 0.950, 0.835, 0.050, 0.047), n1 = c(14, 17, 20, 15, 14, 14, 58, 54), n2 = c(14, 17, 21, 14, 14, 14, 66, 57), subset = as.factor(c(rep(\"Germany\", times = 6), rep(\"USA\", times = 2))) ) # Meta-analysis, random effects, assuming equal variance, no moderator estimate <- esci::meta_d2( data = lucky_golf, ds = smd_corrected, comparison_ns = n1, reference_ns = n2, labels = study, assume_equal_variance = TRUE, random_effects = TRUE ) if (FALSE) { # Forest plot esci::plot_meta(estimate) } # Meta-analysis, random effects, assuming equal variance estimate <- esci::meta_d2( data = lucky_golf, ds = smd_corrected, comparison_ns = n1, reference_ns = n2, moderator = subset, labels = study, assume_equal_variance = TRUE, random_effects = TRUE ) if (FALSE) { # Forest plot esci::plot_meta(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mdiff_two.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate meta-analytic difference in means across multiple two-group studies. — meta_mdiff_two","title":"Estimate meta-analytic difference in means across multiple two-group studies. — meta_mdiff_two","text":"meta_mdiff_two suitable synthesizing across multiple two-group studies (paired independent) continuous outcome measure. takes raw data study. studies used measurement scale, meta-analytic raw-score difference can returned. studies used different scales, standardized mean difference can returned. Studies can paired, independent, mix. Equal variance can assumed, . standardized mean difference output, d_s equal variance assumed d_avg equal variance assumed.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mdiff_two.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate meta-analytic difference in means across multiple two-group studies. — meta_mdiff_two","text":"","code":"meta_mdiff_two( data, comparison_means, comparison_sds, comparison_ns, reference_means, reference_sds, reference_ns, r = NULL, labels = NULL, moderator = NULL, contrast = NULL, effect_label = \"My effect\", reported_effect_size = c(\"mean_difference\", \"smd_unbiased\", \"smd\"), assume_equal_variance = FALSE, random_effects = TRUE, conf_level = 0.95 )"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mdiff_two.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate meta-analytic difference in means across multiple two-group studies. — meta_mdiff_two","text":"data data frame tibble comparison_means Set comparison_group means, 1 per study comparison_sds Set comparison_group standard deviations, 1 per study, > 0 comparison_ns Set comparison_group sample sizes, positive integers, 1 study reference_means Set reference_group means, 1 per study reference_sds Set comparison_group standard deviations, 1 per study, > 0 reference_ns Set reference_group sample sizes, positive integers, 1 study r Optional correlation measures w-s studies, NA otherwise labels optional collection study labels moderator optional factor analyze categorical moderator, must k > 2 per groups contrast optional contrast estimate moderator levels; express vector contrast weights 1 weight per moderator level. effect_label Optional character giving human-friendly name effect synthesized reported_effect_size Character specifying effect size return: Must one 'mean_difference', 'smd_unbiased' (return unbiased Cohen's d_s d_avg) 'smd' (return d_s d_avg without correction bias). Defaults mean_difference. assume_equal_variance Defaults FALSE random_effects TRUE random effect model; FALSE fixed effects conf_level confidence level confidence interval. Given decimal form. Defaults 0.95.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mdiff_two.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate meta-analytic difference in means across multiple two-group studies. — meta_mdiff_two","text":"esci-estimate object; list data frames properties. Returned tables include: es_meta - data frame meta-analytic effect sizes. moderator defined, additional row level moderator. effect_label - Study label effect_size - Effect size LL - Lower bound conf_level% confidence interval UL - Upper bound conf_level% confidence interval SE - Expected standard error k - Number studies diamond_ratio - ratio random fixed effects meta-analtics effect sizes diamond_ratio_LL - lower bound conf_level% confidence interval diamond ratio diamond_ratio_UL - upper bound conf_level% confidence interval diamond ratio I2 - I2 measure heterogeneity I2_LL - Lower bound conf_level% confidence interval I2 I2_UL - upper bound conf_level% confidence interval I2 PI_LL - lower bound conf_level% prediction interval PI_UL - upper bound conf_level% prediction interval p - p value meta-analytic effect size, based null exactly 0 *width - width effect-size confidence interval FE_effect_size - effect size fixed-effects model (regardless fixed effects selected RE_effect_size - effect size random-effects model (regardless random effects selected FE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio RE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio es_heterogeneity - data frame heterogeneity values conf_level% CIs meta-analytic effect size. moderator defined also reports heterogeneity estimates level moderator. effect_label - study label moderator_variable_name - moderator passed, gives name moderator moderator_level - 'Overall' level moderator, passed measure - Name measure heterogeneity estimate - Value heterogeneity estimate LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval raw_data - data one row study passed label - study label effect_size - effect size weight - study weight meta analysis sample_variance - expected level sampling variation SE - expected standard error LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval mean - used calculate study p value; d value entered study sd - use calculate study p value; set 1 study n - study sample size p - p value study, based null exactly 0","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mdiff_two.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate meta-analytic difference in means across multiple two-group studies. — meta_mdiff_two","text":"generate estimate function, can visualize plot_meta(). meta-analytic effect size, confidence interval heterogeneity estimates come metafor::rma(). diamond ratio confidence interval come CI_diamond_ratio(). reported_effect_size smd_unbiased smd conversion Cohen's d handled CI_smd_ind_contrast().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mdiff_two.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate meta-analytic difference in means across multiple two-group studies. — meta_mdiff_two","text":"","code":"# Data set -- see Introduction to the New Statistics, 1st edition brain_scan_persuasion <- data.frame( study_name = c(\"McCabe 1\", \"McCabe 2\", paste(\"Michael\", seq(1:10), sep = \" \")), nbM = c(2.89, 2.69, 2.90, 2.62, 2.96, 2.93, 2.86, 2.50, 2.41, 2.54, 2.73, 2.66), nbS = c(0.79, 0.55, 0.58, 0.54, 0.36, 0.60, 0.59, 0.84, 0.78, 0.66, 0.67, 0.65), nbN = c(28, 26, 98, 42, 24, 184, 274, 58, 34, 99, 98, 94), bM = c(3.12, 3.00, 2.86, 2.85, 3.07, 2.89, 2.91, 2.60, 2.74, 2.72, 2.68, 2.64), bS = c(0.65, 0.54, 0.61, 0.57, 0.55, 0.60, 0.52, 0.83, 0.51, 0.68, 0.69, 0.71), bN = c(26, 28, 99, 33, 21, 184, 255, 55, 34, 95, 93, 97), mod = as.factor( c(\"Simple\", \"Critique\", \"Simple\",\"Simple\",\"Simple\", \"Simple\",\"Simple\",\"Critique\",\"Critique\",\"Critique\", \"Critique\",\"Critique\") ), ds = c(0.31217944, 0.56073138, -0.06693802, 0.41136192, 0.23581389, -0.06652995, 0.08958082, 0.11892778, 0.49506069, 0.26765910, -0.07326, -0.02925) ) # Meta-analysis: random effects, no moderator estimate <- esci::meta_mdiff_two( data = brain_scan_persuasion, comparison_means = bM, comparison_sds = bS, comparison_ns = bN, reference_means = nbM, reference_sds = nbS, reference_ns = nbN, labels = study_name, effect_label = \"Brain Photo Rating - No Brain Photo Rating\", assume_equal_variance = TRUE, random_effects = TRUE ) if (FALSE) { # Forest plot esci::plot_meta(estimate) } # Meta-analysis: random effects, moderator if (FALSE) { # Forest plot esci::plot_meta(estimate) } # Meta-analysis: random effects, moderator, output d_s if (FALSE) { # Forest plot esci::plot_meta(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mean.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate a meta-analytic mean across multiple single-group studies. — meta_mean","title":"Estimate a meta-analytic mean across multiple single-group studies. — meta_mean","text":"meta_mean suitable synthesizing across multiple single-group studies continuous outcome variable studies measured scale.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate a meta-analytic mean across multiple single-group studies. — meta_mean","text":"","code":"meta_mean( data, means, sds, ns, labels = NULL, moderator = NULL, contrast = NULL, effect_label = \"My effect\", reference_mean = 0, reported_effect_size = c(\"mean_difference\", \"smd_unbiased\", \"smd\"), random_effects = TRUE, conf_level = 0.95 )"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mean.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate a meta-analytic mean across multiple single-group studies. — meta_mean","text":"data dataframe tibble means collection study means, 1 per study sds collection study standard deviations, 1 per study, >0 ns collection sample sizes, 1 per study, integers > 2 labels optional collection study labels moderator optional factor analyze categorical moderator, must k > 2 per groups contrast optional contrast estimate moderator levels; express vector contrast weights 1 weight per moderator level. effect_label Optional character giving human-friendly name effect synthesized reference_mean Optional reference mean, defaults 0 reported_effect_size Character specifying effect size return; Must one 'mean_difference', 'smd_unbiased' (return unbiased Cohen's d1) 'smd' (return Cohen's d1 without correction bias) random_effects TRUE random effect model; FALSE fixed effects conf_level confidence level confidence interval. Given decimal form. Defaults 0.95.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mean.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate a meta-analytic mean across multiple single-group studies. — meta_mean","text":"esci-estimate object; list data frames properties. Returned tables include: es_meta - data frame meta-analytic effect sizes. moderator defined, additional row level moderator. effect_label - Study label effect_size - Effect size LL - Lower bound conf_level% confidence interval UL - Upper bound conf_level% confidence interval SE - Expected standard error k - Number studies diamond_ratio - ratio random fixed effects meta-analtics effect sizes diamond_ratio_LL - lower bound conf_level% confidence interval diamond ratio diamond_ratio_UL - upper bound conf_level% confidence interval diamond ratio I2 - I2 measure heterogeneity I2_LL - Lower bound conf_level% confidence interval I2 I2_UL - upper bound conf_level% confidence interval I2 PI_LL - lower bound conf_level% prediction interval PI_UL - upper bound conf_level% prediction interval p - p value meta-analytic effect size, based null exactly 0 *width - width effect-size confidence interval FE_effect_size - effect size fixed-effects model (regardless fixed effects selected RE_effect_size - effect size random-effects model (regardless random effects selected FE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio RE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio es_heterogeneity - data frame heterogeneity values conf_level% CIs meta-analytic effect size. moderator defined also reports heterogeneity estimates level moderator. effect_label - study label moderator_variable_name - moderator passed, gives name moderator moderator_level - 'Overall' level moderator, passed measure - Name measure heterogeneity estimate - Value heterogeneity estimate LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval raw_data - data one row study passed label - study label effect_size - effect size weight - study weight meta analysis sample_variance - expected level sampling variation SE - expected standard error LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval mean - used calculate study p value; d value entered study sd - use calculate study p value; set 1 study n - study sample size p - p value study, based null exactly 0","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mean.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate a meta-analytic mean across multiple single-group studies. — meta_mean","text":"meta-analytic effect size, confidence interval heterogeneity estimates come metafor::rma(). diamond ratio confidence interval come CI_diamond_ratio(). reported_effect_size smd_unbiased smd conversion d1 handled CI_smd_one().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_mean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate a meta-analytic mean across multiple single-group studies. — meta_mean","text":"","code":"# example code original_7 <- data.frame( study_name = c( \"Aden (1993)\" , \"Buggs (1995)\" , \"Crazed (1999)\" , \"Dudley (2003)\" , \"Evers (2005)\" , \"Fox (2009)\", \"Mine (2011)\" ), rt_mean = c( 454 , 317 , 430 , 525 , 479 , 387, 531 ), rt_sd = c( 142 , 158 , 137 , 260 , 144 , 165, 233 ), rt_n = c( 24 , 7 , 20 , 8 , 14 , 13, 18 ), subset = as.factor( c( \"90s\", \"90s\", \"90s\", \"00s\", \"00s\", \"00s\", \"00s\" ) ), d1_unbiased = c( 3.091587, 1.742751, 3.012857, 1.793487, 3.130074, 2.195209, 2.17667 ) ) # Fixed effect, 95% CI estimate <- esci::meta_mean( original_7, rt_mean, rt_sd, rt_n, study_name, random_effects = FALSE ) # Random effects, categorical moderator, report cohen's d1 estimate <- esci::meta_mean( original_7, rt_mean, rt_sd, rt_n, study_name, subset, reported_effect_size = \"smd_unbiased\" ) if (FALSE) { # Forest plot esci::plot_meta(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_pdiff_two.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate meta-analytic difference in proportions over multiple studies\r\nwith two independent groups and a categorical outcome variable. — meta_pdiff_two","title":"Estimate meta-analytic difference in proportions over multiple studies\r\nwith two independent groups and a categorical outcome variable. — meta_pdiff_two","text":"meta_pdiff_two suitable synthesizing across multiple two-group studies categorical outcome variable. takes input number cases/events comparison reference groups well total number samples comparison reference groups.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_pdiff_two.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate meta-analytic difference in proportions over multiple studies\r\nwith two independent groups and a categorical outcome variable. — meta_pdiff_two","text":"","code":"meta_pdiff_two( data, comparison_cases, comparison_ns, reference_cases, reference_ns, labels = NULL, moderator = NULL, contrast = NULL, effect_label = \"My effect\", reported_effect_size = c(\"RD\", \"RR\", \"OR\", \"AS\", \"PETO\"), random_effects = TRUE, conf_level = 0.95 )"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_pdiff_two.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate meta-analytic difference in proportions over multiple studies\r\nwith two independent groups and a categorical outcome variable. — meta_pdiff_two","text":"data dataframe tibble comparison_cases collection case/event counts comparison groups, 1 per study, integers >= 0 comparison_ns collection sample sizes comparison groups, 1 per study, integers > 2 reference_cases collection case/event counts reference groups, 1 per study, integers >= 0 reference_ns collection sample sizes reference groups, 1 per study, integers > 2 labels optional collection study labels moderator optional factor analyze categorical moderator, must k > 2 per groups contrast optional contrast estimate moderator levels; express vector contrast weights 1 weight per moderator level. effect_label Optional character giving human-friendly name effect synthesized reported_effect_size Character specifying effect size return: Must one 'RD' (risk difference, default), 'RR' (log risk ratio), '' (log odds ratio), '' (arcsine square root transformed risk difference), 'PETO' (log odds ratio estimated using Peto's method). See metafor::escalc() details. random_effects TRUE random effect model; FALSE fixed effects conf_level confidence level confidence interval. Given decimal form. Defaults 0.95.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_pdiff_two.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate meta-analytic difference in proportions over multiple studies\r\nwith two independent groups and a categorical outcome variable. — meta_pdiff_two","text":"esci-estimate object; list data frames properties. Returned tables include: es_meta - data frame meta-analytic effect sizes. moderator defined, additional row level moderator. effect_label - Study label effect_size - Effect size LL - Lower bound conf_level% confidence interval UL - Upper bound conf_level% confidence interval SE - Expected standard error k - Number studies diamond_ratio - ratio random fixed effects meta-analtics effect sizes diamond_ratio_LL - lower bound conf_level% confidence interval diamond ratio diamond_ratio_UL - upper bound conf_level% confidence interval diamond ratio I2 - I2 measure heterogeneity I2_LL - Lower bound conf_level% confidence interval I2 I2_UL - upper bound conf_level% confidence interval I2 PI_LL - lower bound conf_level% prediction interval PI_UL - upper bound conf_level% prediction interval p - p value meta-analytic effect size, based null exactly 0 *width - width effect-size confidence interval FE_effect_size - effect size fixed-effects model (regardless fixed effects selected RE_effect_size - effect size random-effects model (regardless random effects selected FE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio RE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio es_heterogeneity - data frame heterogeneity values conf_level% CIs meta-analytic effect size. moderator defined also reports heterogeneity estimates level moderator. effect_label - study label moderator_variable_name - moderator passed, gives name moderator moderator_level - 'Overall' level moderator, passed measure - Name measure heterogeneity estimate - Value heterogeneity estimate LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval raw_data - data one row study passed label - study label effect_size - effect size weight - study weight meta analysis sample_variance - expected level sampling variation SE - expected standard error LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval mean - used calculate study p value; d value entered study sd - use calculate study p value; set 1 study n - study sample size p - p value study, based null exactly 0","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_pdiff_two.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate meta-analytic difference in proportions over multiple studies\r\nwith two independent groups and a categorical outcome variable. — meta_pdiff_two","text":"generate estimate function, can visualize plot_meta(). meta-analytic effect size, confidence interval heterogeneity estimates come metafor::rma(). conversion events suitable effect sizes handled metafor::escalc()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_pdiff_two.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate meta-analytic difference in proportions over multiple studies\r\nwith two independent groups and a categorical outcome variable. — meta_pdiff_two","text":"","code":"# Data set: Replications of power on egocentric behavior esci_meta_pdiff_two <- data.frame( studies = c( \"Online\", \"Original\", \"Online Pilot\", \"Exact replication\" ), control_egocentric = c( 33, 4, 4, 7 ), control_sample_size = c( 101, 33, 10, 53 ), power_egocentric = c( 48, 8, 4, 11 ), power_sample_size = c( 105, 24, 12, 56 ), setting = as.factor( c( \"Online\", \"In-Person\", \"Online\", \"In-Person\" ) ) ) # Meta-analysis, risk difference as effect size estimate <- esci::meta_pdiff_two( esci_meta_pdiff_two, power_egocentric, power_sample_size, control_egocentric, control_sample_size, studies, reported_effect_size = \"RD\" ) if (FALSE) { # Forest plot esci::plot_meta(estimate) } # Meta-analysis, risk difference as effect size, moderator (setting) estimate <- esci::meta_pdiff_two( esci_meta_pdiff_two, power_egocentric, power_sample_size, control_egocentric, control_sample_size, studies, moderator = setting, reported_effect_size = \"RD\" ) if (FALSE) { # Forest plot esci::plot_meta(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate a meta-analytic proportion of outcomes over multiple studies with\r\na categorical outcome variable. — meta_proportion","title":"Estimate a meta-analytic proportion of outcomes over multiple studies with\r\na categorical outcome variable. — meta_proportion","text":"meta_proportion suitable synthesizing across multiple studies categorical outcome variable. takes input number cases/events number samples study.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate a meta-analytic proportion of outcomes over multiple studies with\r\na categorical outcome variable. — meta_proportion","text":"","code":"meta_proportion( data, cases, ns, labels = NULL, moderator = NULL, contrast = NULL, effect_label = \"My effect\", random_effects = TRUE, conf_level = 0.95 )"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_proportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate a meta-analytic proportion of outcomes over multiple studies with\r\na categorical outcome variable. — meta_proportion","text":"data dataframe tibble cases collection cases/event counts, 1 per study, integers, > 0 ns collection sample sizes, 1 per study, integers > 2 labels optional collection study labels moderator optional factor analyze categorical moderator, must k > 2 per groups contrast optional contrast estimate moderator levels; express vector contrast weights 1 weight per moderator level. effect_label Optional character giving human-friendly name effect synthesized random_effects TRUE random effect model; FALSE fixed effects conf_level confidence level confidence interval. Given decimal form. Defaults 0.95.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate a meta-analytic proportion of outcomes over multiple studies with\r\na categorical outcome variable. — meta_proportion","text":"esci-estimate object; list data frames properties. Returned tables include: es_meta - data frame meta-analytic effect sizes. moderator defined, additional row level moderator. effect_label - Study label effect_size - Effect size LL - Lower bound conf_level% confidence interval UL - Upper bound conf_level% confidence interval SE - Expected standard error k - Number studies diamond_ratio - ratio random fixed effects meta-analtics effect sizes diamond_ratio_LL - lower bound conf_level% confidence interval diamond ratio diamond_ratio_UL - upper bound conf_level% confidence interval diamond ratio I2 - I2 measure heterogeneity I2_LL - Lower bound conf_level% confidence interval I2 I2_UL - upper bound conf_level% confidence interval I2 PI_LL - lower bound conf_level% prediction interval PI_UL - upper bound conf_level% prediction interval p - p value meta-analytic effect size, based null exactly 0 *width - width effect-size confidence interval FE_effect_size - effect size fixed-effects model (regardless fixed effects selected RE_effect_size - effect size random-effects model (regardless random effects selected FE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio RE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio es_heterogeneity - data frame heterogeneity values conf_level% CIs meta-analytic effect size. moderator defined also reports heterogeneity estimates level moderator. effect_label - study label moderator_variable_name - moderator passed, gives name moderator moderator_level - 'Overall' level moderator, passed measure - Name measure heterogeneity estimate - Value heterogeneity estimate LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval raw_data - data one row study passed label - study label effect_size - effect size weight - study weight meta analysis sample_variance - expected level sampling variation SE - expected standard error LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval mean - used calculate study p value; d value entered study sd - use calculate study p value; set 1 study n - study sample size p - p value study, based null exactly 0","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_proportion.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate a meta-analytic proportion of outcomes over multiple studies with\r\na categorical outcome variable. — meta_proportion","text":"generate estimate function, can visualize plot_meta(). meta-analytic effect size, confidence interval heterogeneity estimates come metafor::rma().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_proportion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate a meta-analytic proportion of outcomes over multiple studies with\r\na categorical outcome variable. — meta_proportion","text":"","code":"# Data set: Replications of power on egocentric behavior esci_meta_pdiff_two <- data.frame( studies = c( \"Online\", \"Original\", \"Online Pilot\", \"Exact replication\" ), control_egocentric = c( 33, 4, 4, 7 ), control_sample_size = c( 101, 33, 10, 53 ), power_egocentric = c( 48, 8, 4, 11 ), power_sample_size = c( 105, 24, 12, 56 ), setting = as.factor( c( \"Online\", \"In-Person\", \"Online\", \"In-Person\" ) ) ) # Meta-analysis, risk difference as effect size estimate <- esci::meta_proportion( esci_meta_pdiff_two, power_egocentric, power_sample_size, studies ) if (FALSE) { # Forest plot esci::plot_meta(estimate) } # Meta-analysis, risk difference as effect size, moderator (setting) estimate <- esci::meta_proportion( esci_meta_pdiff_two, power_egocentric, power_sample_size, studies, moderator = setting ) if (FALSE) { # Forest plot esci::plot_meta(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_r.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate meta-analytic Pearson's r across multiple studies with two\r\ncontinuous outcome variables. — meta_r","title":"Estimate meta-analytic Pearson's r across multiple studies with two\r\ncontinuous outcome variables. — meta_r","text":"meta_r suitable synthesizing across multiple studies measured linear correlation (Pearson's r) two continuyous variables.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_r.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate meta-analytic Pearson's r across multiple studies with two\r\ncontinuous outcome variables. — meta_r","text":"","code":"meta_r( data, rs, ns, labels = NULL, moderator = NULL, contrast = NULL, effect_label = \"My effect\", random_effects = TRUE, conf_level = 0.95 )"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_r.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate meta-analytic Pearson's r across multiple studies with two\r\ncontinuous outcome variables. — meta_r","text":"data dataframe tibble rs collection Pearson's r values, 1 per study, -1 1, inclusive ns collection study sample sizes, integers > 2 labels optional collection study labels moderator optional factor analyze categorical moderator, must k > 2 per groups contrast optional contrast estimate moderator levels; express vector contrast weights 1 weight per moderator level. effect_label Optional character giving human-friendly name effect synthesized random_effects TRUE random effect model; FALSE fixed effects conf_level confidence level confidence interval. Given decimal form. Defaults 0.95.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_r.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate meta-analytic Pearson's r across multiple studies with two\r\ncontinuous outcome variables. — meta_r","text":"esci-estimate object; list data frames properties. Returned tables include: es_meta - data frame meta-analytic effect sizes. moderator defined, additional row level moderator. effect_label - Study label effect_size - Effect size LL - Lower bound conf_level% confidence interval UL - Upper bound conf_level% confidence interval SE - Expected standard error k - Number studies diamond_ratio - ratio random fixed effects meta-analtics effect sizes diamond_ratio_LL - lower bound conf_level% confidence interval diamond ratio diamond_ratio_UL - upper bound conf_level% confidence interval diamond ratio I2 - I2 measure heterogeneity I2_LL - Lower bound conf_level% confidence interval I2 I2_UL - upper bound conf_level% confidence interval I2 PI_LL - lower bound conf_level% prediction interval PI_UL - upper bound conf_level% prediction interval p - p value meta-analytic effect size, based null exactly 0 *width - width effect-size confidence interval FE_effect_size - effect size fixed-effects model (regardless fixed effects selected RE_effect_size - effect size random-effects model (regardless random effects selected FE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio RE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio es_heterogeneity - data frame heterogeneity values conf_level% CIs meta-analytic effect size. moderator defined also reports heterogeneity estimates level moderator. effect_label - study label moderator_variable_name - moderator passed, gives name moderator moderator_level - 'Overall' level moderator, passed measure - Name measure heterogeneity estimate - Value heterogeneity estimate LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval raw_data - data one row study passed label - study label effect_size - effect size weight - study weight meta analysis sample_variance - expected level sampling variation SE - expected standard error LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval mean - used calculate study p value; d value entered study sd - use calculate study p value; set 1 study n - study sample size p - p value study, based null exactly 0","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_r.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate meta-analytic Pearson's r across multiple studies with two\r\ncontinuous outcome variables. — meta_r","text":"generate estimate function, can visualize plot_meta(). meta-analytic effect size, confidence interval heterogeneity estimates come metafor::rma().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_r.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate meta-analytic Pearson's r across multiple studies with two\r\ncontinuous outcome variables. — meta_r","text":"","code":"# Data: See Introduction to the New Statistics, first edition esci_single_r <- data.frame( studies = c( 'Violin, viola' , 'Strings' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'All' , 'Piano' , 'Piano' , 'Band' , 'Music majors' , 'Music majors' , 'All' ), rvalues = c( .67, .51, .4, .46, .47, .228, -.224, .104, .322, .231, .67, .41, .34, .31, .54, .583 ), sample_size = c( 109, 55, 19, 30, 19, 52, 24, 52, 16, 97, 57, 107, 178, 64, 19, 135 ), subsets = as.factor( c( 'Strings' , 'Strings' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Piano' , 'Strings' , 'Strings' , 'Strings' , 'Strings' ) ) ) # Meta-analysis, random effects, no moderator estimate <- esci::meta_r( esci_single_r, rvalues, sample_size, studies, random_effects = TRUE ) if (FALSE) { # Forest plot esci::plot_meta(estimate) } # Meta-analysis, random effects, moderator (subsets) estimate <- esci::meta_r( esci_single_r, rvalues, sample_size, studies, subsets, random_effects = TRUE ) if (FALSE) { # Forest plot esci::plot_meta(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_smd_two.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate meta-analytic d_s or d_avg across multiple two group studies. — meta_smd_two","title":"Estimate meta-analytic d_s or d_avg across multiple two group studies. — meta_smd_two","text":"meta_smd_two suitable synthesizing across multiple two-group studies outcome variable continuous studies measured scale.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_smd_two.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate meta-analytic d_s or d_avg across multiple two group studies. — meta_smd_two","text":"","code":"meta_smd_two( data, smds, comparison_ns, reference_ns, labels = NULL, moderator = NULL, contrast = NULL, effect_label = \"My effect\", assume_equal_variance = FALSE, correct_bias = TRUE, esci_vi = FALSE, random_effects = TRUE, conf_level = 0.95 )"},{"path":"https://rcalinjageman.github.io/esci/reference/meta_smd_two.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate meta-analytic d_s or d_avg across multiple two group studies. — meta_smd_two","text":"data data frame tibble smds Name column data containing Cohen's d study comparison_ns Name column data containing sample size comparison group study; integer > 0. reference_ns Name column data containing sample size reference group study; integer > 0. labels Name column data containing label study moderator Optional name column data containing factor categorical moderator contrast Optional vector specifying contrast analysis categorical moderator. define moderator defined; vector length match number levels moderator effect_label Optional human-friendly name effect synthesized; defaults 'effect' assume_equal_variance TRUE assume equal variance, meaning effect sizes provided d_s; FALSE assume equal variance, meaning effect sizes provided d_avg correct_bias TRUE apply bias correction effect sizes provided conducting meta-analysis esci_vi TRUE report sampling variance based Bonnett rather metafor random_effects Use TRUE obtain random effect meta-anlaysis (usually reccomended); FALSE fixed effect. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_smd_two.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate meta-analytic d_s or d_avg across multiple two group studies. — meta_smd_two","text":"esci-estimate object; list data frames properties. Returned tables include: es_meta - data frame one overall. moderator defined, additional row level moderator. effect_label - Study label effect_size - Effect size LL - Lower bound conf_level% confidence interval UL - Upper bound conf_level% confidence interval SE - Expected standard error k - Number studies diamond_ratio - ratio random fixed effects meta-analtics effect sizes diamond_ratio_LL - lower bound conf_level% confidence interval diamond ratio diamond_ratio_UL - upper bound conf_level% confidence interval diamond ratio I2 - I2 measure heterogeneity I2_LL - Lower bound conf_level% confidence interval I2 I2_UL - upper bound conf_level% confidence interval I2 PI_LL - lower bound conf_level% prediction interval PI_UL - upper bound conf_level% prediction interval p - p value meta-analytic effect size, based null exactly 0 width - width effect-size confidence interval FE_effect_size - effect size fixed-effects model (regardless fixed effects selected RE_effect_size - effect size random-effects model (regardless random effects selected FE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio RE_CI_width - width fixed-effects confidence interval, used calculate diamond ratio es_heterogeneity - data frame heterogeneity values conf_level% CIs meta-analytic effect size. moderator defined also reports heterogeneity estimates level moderator. effect_label - study label moderator_variable_name - moderator passed, gives name moderator moderator_variable_name - 'Overall' level moderator, passed measure - Name measure heterogeneity LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval raw_data - data one row study passed label - study label effect_size - effect size weight - study weight meta analysis sample_variance - expected level sampling variation SE - expected standard error LL - lower bound conf_level% confidence interval UL - upper bound conf_level% confidence interval mean - used calculate study p value; d value entered study n - study sample size sd - use calculate study p value; set 1 study p - p value study, based null exactly 0","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/meta_smd_two.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate meta-analytic d_s or d_avg across multiple two group studies. — meta_smd_two","text":"study's effect size expressed d_s d_avg. function estimate_mdiff_two() can provide effect sizes raw data. meta-analytic effect size, confidence interval heterogeneity estimates come metafor::rma(). diamond ratio confidence interval come CI_diamond_ratio().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/overview.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates descriptive statistics for a continuous variable — overview","title":"Calculates descriptive statistics for a continuous variable — overview","text":"function calculates basic descriptive statistics numerical variable. can calculate overall summary, broken levels grouping variable. Inputs can summary data, vectors, data frame.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/overview.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates descriptive statistics for a continuous variable — overview","text":"","code":"overview( data = NULL, outcome_variable = NULL, grouping_variable = NULL, means = NULL, sds = NULL, ns = NULL, grouping_variable_levels = NULL, outcome_variable_name = \"My Outcome Variable\", grouping_variable_name = NULL, conf_level = 0.95, assume_equal_variance = FALSE )"},{"path":"https://rcalinjageman.github.io/esci/reference/overview.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates descriptive statistics for a continuous variable — overview","text":"data raw data, data frame tibble outcome_variable raw data, either vector containing numerical data name data-frame column containing factor grouping_variable optional; raw data either vector containing factor name data frame column containing factor means summary data - vector 1 numerical means sds summary data - vector standard deviations, length means ns summary data - vector sample sizes, length means grouping_variable_levels summary data - optional vector group labels, length means. passed, auto-generated. outcome_variable_name Optional friendly name outcome variable. Defaults 'Outcome Variable'. Ignored data-frame passed, argument ignored. grouping_variable_name Optional friendly name grouping variable. data frame passed, argument ignored. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. assume_equal_variance Defaults FALSE","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/overview.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates descriptive statistics for a continuous variable — overview","text":"Returns table descriptive statistics overview outcome_variable_name - grouping_variable_name - grouping_variable_level - mean - mean_LL - mean_UL - median - median_LL - median_UL - sd - min - max - q1 - q3 - n - missing - df - mean_SE - median_SE -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/overview.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculates descriptive statistics for a continuous variable — overview","text":"equal variance assumed, group treated independently. case, estimated mean, CI, SE statpsych::ci.mean1(), estimated median, CI, SE statpsych::ci.median1(). equal variance assumed, group CI calculated respect group data, using statpsych::ci.lc.mean.bs() statpsych::ci.lc.median.bs()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/overview.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculates descriptive statistics for a continuous variable — overview","text":"","code":"# example code esci::overview(data_latimier_3groups, \"Test%\", \"Group\") #> outcome_variable_name grouping_variable_name grouping_variable_level mean #> 1 Test% Group Reread 37.29323 #> 2 Test% Group Quiz 42.75689 #> 3 Test% Group Prequiz 37.14286 #> mean_LL mean_UL median median_LL median_UL sd min max #> 1 34.17500 40.41146 33.33333 28.79785 37.86881 15.30716 9.52381 76.19048 #> 2 38.96548 46.54830 42.85714 36.05392 49.66036 18.61177 14.28571 95.23810 #> 3 33.88967 40.39604 33.33333 31.06559 35.60107 15.96965 9.52381 90.47619 #> q1 q3 n missing df mean_SE median_SE #> 1 28.57143 47.61905 95 0 94 1.570482 2.314063 #> 2 28.57143 57.14286 95 0 94 1.909527 3.471095 #> 3 23.80952 42.85714 95 0 94 1.638451 1.157033"},{"path":"https://rcalinjageman.github.io/esci/reference/overview_nominal.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates descriptive statistics for a numerical variable — overview_nominal","title":"Calculates descriptive statistics for a numerical variable — overview_nominal","text":"function calculated basic descriptive statistics categorical/ nominal variable. Inputs can summary data, vectors, data frame.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/overview_nominal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates descriptive statistics for a numerical variable — overview_nominal","text":"","code":"overview_nominal( data = NULL, outcome_variable = NULL, grouping_variable = NULL, cases = NULL, outcome_variable_levels = NULL, outcome_variable_name = \"My Outcome Variable\", grouping_variable_name = \"My Grouping Variable\", conf_level = 0.95, count_NA = FALSE )"},{"path":"https://rcalinjageman.github.io/esci/reference/overview_nominal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates descriptive statistics for a numerical variable — overview_nominal","text":"data raw data, data frame tibble outcome_variable raw data, either vector containing factor data name data-frame column containing factor grouping_variable raw data, either NULL (default), vector factor data-frame column containing factor cases summary data - vector 1 counts, integers>0 outcome_variable_levels summary data - optional vector group labels, length cases. passed, auto-generated. outcome_variable_name Optional friendly name outcome variable. Defaults 'Outcome Variable'. Ignored data-frame passed, argument ignored. grouping_variable_name Optional friendly name grouping variable. Defaults 'Grouping Variable'. Ignored summary data data frames -- used vectors data passed. conf_level confidence level confidence interval. Given decimal form. Defaults 0.95. count_NA Logical count NAs (TRUE) total N (FALSE)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/overview_nominal.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates descriptive statistics for a numerical variable — overview_nominal","text":"Returns table descriptive statistics overview_nominal outcome_variable_name - outcome_variable_level - cases - n - P - P_LL - P_UL - P_SE - P_adjusted - ta_LL - ta_UL -","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/overview_nominal.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculates descriptive statistics for a numerical variable — overview_nominal","text":"","code":"# example code esci::overview_nominal(esci::data_latimier_3groups, \"Group\") #> outcome_variable_name outcome_variable_level cases n P P_LL #> 1 Group Reread 95 285 0.3333333 0.2811977 #> 2 Group Quiz 95 285 0.3333333 0.2811977 #> 3 Group Prequiz 95 285 0.3333333 0.2811977 #> P_UL P_SE P_adjusted ta_LL ta_UL #> 1 0.3900826 0.02777728 0.3356401 0.2899506 0.3813297 #> 2 0.3900826 0.02777728 0.3356401 0.2899506 0.3813297 #> 3 0.3900826 0.02777728 0.3356401 0.2899506 0.3813297"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_correlation.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot an estimated Pearson's r value — plot_correlation","title":"Plot an estimated Pearson's r value — plot_correlation","text":"plot_correlation creates ggplot2 plot suitable visualizing estimate correlation two continuous variables (Pearson's r). function can passed esci_estimate object generated estimate_r()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_correlation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot an estimated Pearson's r value — plot_correlation","text":"","code":"plot_correlation( estimate, error_layout = c(\"halfeye\", \"eye\", \"gradient\", \"none\"), error_scale = 0.3, error_normalize = c(\"groups\", \"all\", \"panels\"), rope = c(NA, NA), ggtheme = NULL )"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_correlation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot an estimated Pearson's r value — plot_correlation","text":"estimate esci_estimate object generated estimate_r() error_layout Optional; One 'halfeye', 'eye', 'gradient' 'none' expected sampling error measure central tendency displayed. Caution - displayed error distributions seem correct yet error_scale Optional real number > 0 speciyin width expected sampling error visualization; default 0.3 error_normalize Optional; One 'groups' (default), '', 'panels' specifying width expected sampling error distributions calculated. rope Optional two-item vector specifying region practical equivalence (ROPE) highlighted plot. point null hypothesis, pass value (e.g. c(0, 0) test point null exaclty 0); interval null pass ascending values (e.g. c(-1, 1)) ggtheme Optional ggplot2 theme object control overall styling; defaults ggplot2::theme_classic()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_correlation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot an estimated Pearson's r value — plot_correlation","text":"Returns ggplot object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_correlation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot an estimated Pearson's r value — plot_correlation","text":"function developed primarily student use within jamovi learning along text book Introduction New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024). Expect breaking changes function improved general use. Work still done includes: Revise avoid deprecated ggplot features Revise consistent ability control aesthetics consistent layer names","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_correlation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot an estimated Pearson's r value — plot_correlation","text":"","code":"# From raw data thomason1 <- data.frame( ls_pre = c( 13, 12, 12, 9, 14, 17, 14, 9, 6, 7, 11, 15 ), ls_post = c( 14, 13, 16, 12, 15, 18, 13, 10, 10, 8, 14, 16 ) ) estimate <- esci::estimate_r( thomason1, ls_pre, ls_post ) estimate #> Analysis of raw data: #> Data frame = data #> #> ---Overview--- #> outcome_variable_name mean mean_LL mean_UL median median_LL median_UL #> 1 ls_pre 11.58333 9.476776 13.68989 12.0 9 14 #> 2 ls_post 13.25000 11.410019 15.08998 13.5 10 16 #> sd min max q1 q3 n missing df mean_SE median_SE #> 1 3.315483 6 17 9.0 14.00 12 0 11 0.9570974 1.208488 #> 2 2.895922 8 18 11.5 15.25 12 0 11 0.8359806 1.450186 #> #> -- es_r -- #> x_variable_name y_variable_name effect effect_size LL #> 1 ls_pre ls_post ls_pre and ls_post 0.8923908 0.62894 #> UL SE n df ta_LL ta_UL #> 1 0.967157 0.06139936 12 10 0.6882891 0.9596343 #> #> -- regression -- #> component values LL UL #> 1 Intercept (a) 4.2212267 0.8856544 7.556799 #> 2 Slope (b) 0.7794624 0.5017391 1.057186 #> [1] \"Ŷ = 4.221 + 0.7795*X\" #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. # To evaluate a hypothesis (interval null from -0.1 to 0.1): test_correlation(estimate, rope = c(-0.1, 0.1)) #> $properties #> $properties$effect_size_name #> [1] \"r\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] TRUE #> #> $properties$rope #> [1] -0.1 0.1 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name effect null_words #> 1 Nil Hypothesis Test ls_pre and ls_post ls_pre and ls_post 0.00 #> confidence LL UL CI #> 1 95 0.62894 0.967157 95% CI [0.62894, 0.967157] #> CI_compare t df p p_result #> 1 The 95% CI does not contain H_0 4.300635 10 1.703091e-05 p < 0.05 #> null_decision conclusion significant #> 1 Reject H_0 At α = 0.05, 0.00 is not a plausible value of Ͱ TRUE #> #> $interval_null #> test_type outcome_variable_name effect #> 1 Practical significance test ls_pre and ls_post ls_pre and ls_post #> rope confidence #> 1 (-0.10, 0.10) 95 #> CI #> 1 95% CI [0.62894, 0.967157]\\n90% CI [0.6882891, 0.9596343] #> rope_compare p_result conclusion #> 1 95% CI fully outside H_0 p < 0.05 At α = 0.05, conclude Ͱ is substantive #> significant #> 1 TRUE #> if (FALSE) { # To visualize the value of r plot_correlation(estimate) # To visualize the data (scatterplot) plot_scatter(estimate) # To visualize the data (scatterplot) and use regression to obtain Y' from X plot_scatter(estimate, predict_from_x = 10) } # From summary data estimate <- esci::estimate_r(r = 0.536, n = 50) estimate #> Analysis of summary data: #> #> -- es_r -- #> x_variable_name y_variable_name effect effect_size #> 1 My x variable My y variable My x variable and My y variable 0.536 #> LL UL SE n df ta_LL ta_UL #> 1 0.2978573 0.7058914 0.1018149 50 48 0.3391487 0.6820747 #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimate plot_correlation(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_describe.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a histogram or dotplot of an estimated magnitude with raw data — plot_describe","title":"Plot a histogram or dotplot of an estimated magnitude with raw data — plot_describe","text":"plot_describe Takes estimate produced estimate_magnitude produces dotplot histogram. can mark various descriptive statitics plot, including mean, median, sd, quartiles, z lines. percentile passed, color-codes data based percentile.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_describe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a histogram or dotplot of an estimated magnitude with raw data — plot_describe","text":"","code":"plot_describe( estimate, type = c(\"histogram\", \"dotplot\"), mark_mean = FALSE, mark_median = FALSE, mark_sd = FALSE, mark_quartiles = FALSE, mark_z_lines = FALSE, mark_percentile = NULL, histogram_bins = 12, ylim = c(0, NA), ybreaks = NULL, xlim = c(NA, NA), xbreaks = NULL, fill_regular = \"#008DF9\", fill_highlighted = \"#E20134\", color = \"black\", marker_size = 5, ggtheme = NULL )"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_describe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a histogram or dotplot of an estimated magnitude with raw data — plot_describe","text":"estimate esci_estimate object raw data es_mean type histogram dotplot mark_mean mean marked? mark_median median marked? mark_sd mean marked? mark_quartiles mean marked? mark_z_lines z lines marked? mark_percentile percentile (0 1) marked histogram_bins number bins histogram ylim 2-length numeric vector ybreaks numeric >= 1 xlim 2-length numeric vector xbreaks numeric >= 1 fill_regular color fill_highlighted color color outline color marker_size Size markers ggtheme theme apply, ","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_describe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a histogram or dotplot of an estimated magnitude with raw data — plot_describe","text":"Returns ggplot object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_describe.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot a histogram or dotplot of an estimated magnitude with raw data — plot_describe","text":"function developed primarily student use within jamovi learning along text book Introduction New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024). Expect breaking changes function improved general use. Work still done includes: Revise avoid deprecated ggplot features Revise consistent ability control aesthetics consistent layer names","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_describe.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot a histogram or dotplot of an estimated magnitude with raw data — plot_describe","text":"","code":"# example code # Generate an estimate on a single continuous variable estimate <- esci::estimate_magnitude(esci::data_latimier_3groups, `Test%`) # Now describe the result, with a histogram plot_describe(estimate) # Same, but as a dotplot and mark the mean plot_describe(estimate, type = \"dotplot\", mark_mean = TRUE) #> Warning: Ignoring unknown aesthetics: z #> Warning: `stat(z <= mark_percentile)` was deprecated in ggplot2 3.4.0. #> ℹ Please use `after_stat(z <= mark_percentile)` instead. #> ℹ The deprecated feature was likely used in the esci package. #> Please report the issue to the authors."},{"path":"https://rcalinjageman.github.io/esci/reference/plot_interaction.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the interaction from a 2x2 design — plot_interaction","title":"Plot the interaction from a 2x2 design — plot_interaction","text":"plot_interaction helps visualize interaction 2x2 design. plots 2 simple effects first factor can also help visualize CIs simple effects. comparison simple effects represents interaction (difference difference). can pass esci-estimate objects generated estimate_mdiff_2x2_between() estimate_mdiff_2x2_mixed(). function returns ggplot2 object.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_interaction.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the interaction from a 2x2 design — plot_interaction","text":"","code":"plot_interaction( estimate, effect_size = c(\"mean\", \"median\"), show_CI = FALSE, ggtheme = NULL, line_count = 100, line_alpha = 0.02 )"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_interaction.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the interaction from a 2x2 design — plot_interaction","text":"estimate esci_estimate object raw data es_mdiff_2x2_ function effect_size Optional; one 'mean' 'median' determine measure central tendency plotted. Note median available estimate generated raw data. Defauls 'mean' show_CI Optional logical; set TRUE visualize confidence intervals simple effect; defaults FALSE ggtheme Optional ggplot2 theme object specify visual style plot. Defaults ggplot2::theme_classic() line_count Optional integer > 0 specify number lines used visualize simple-effect confidence intervals; defaults 100 line_alpha Optional numeric 0 1 specify alpha (transparancy) confidence interval lines; defaults 0.02","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_interaction.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the interaction from a 2x2 design — plot_interaction","text":"Returns ggplot object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_interaction.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot the interaction from a 2x2 design — plot_interaction","text":"function developed primarily student use within jamovi learning along text book Introduction New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024). Expect breaking changes function improved general use. Work still done includes: Revise avoid deprecated ggplot features Revise consistent ability control aesthetics consistent layer names","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_interaction.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot the interaction from a 2x2 design — plot_interaction","text":"","code":"# From summary data means <- c(1.5, 1.14, 1.38, 2.22) sds <- c(1.38, .96,1.5, 1.68) ns <- c(26, 26, 25, 26) grouping_variable_A_levels <- c(\"Evening\", \"Morning\") grouping_variable_B_levels <- c(\"Sleep\", \"No Sleep\") estimates <- estimate_mdiff_2x2_between( means = means, sds = sds, ns = ns, grouping_variable_A_levels = grouping_variable_A_levels, grouping_variable_B_levels = grouping_variable_B_levels, grouping_variable_A_name = \"Testing Time\", grouping_variable_B_name = \"Rest\", outcome_variable_name = \"False Memory Score\", assume_equal_variance = TRUE ) if (FALSE) { # To visualize the estimated mean difference for the interaction plot_mdiff(estimate$interaction, effect_size = \"mean\") # To visualize the interaction as a line plot plot_interaction(estimates) # Same but with fan effect representing each simple-effect CI plot_interaction(estimates, show_CI = TRUE) }"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_magnitude.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the mean or median for a continuous variable — plot_magnitude","title":"Plot the mean or median for a continuous variable — plot_magnitude","text":"plot_magnitude creates ggplot2 plot suitable visualizing results study one group one continuous outcome variables. can highlight either mean median outcome variable. function can passed esci_estimate object generated estimate_magnitude()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_magnitude.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the mean or median for a continuous variable — plot_magnitude","text":"","code":"plot_magnitude( estimate, effect_size = c(\"mean\", \"median\"), data_layout = c(\"random\", \"swarm\", \"none\"), data_spread = 0.25, error_layout = c(\"halfeye\", \"eye\", \"gradient\", \"none\"), error_scale = 0.3, error_nudge = 0.35, error_normalize = c(\"groups\", \"all\", \"panels\"), rope = c(NA, NA), ggtheme = NULL )"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_magnitude.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the mean or median for a continuous variable — plot_magnitude","text":"estimate esci_estimate object generated estimate_magnitude() effect_size Optional; One 'mean' (default) 'median'; specifies measure central tendency highlight; note medians available esci_estimate object generated raw data data_layout Optional; One 'random' (default), 'swarm', 'none' raw data (available) displayed data_spread Optional real number > 0 specifying width raw data (available) take graph; default 0.25; default spacing two groups graph 1 error_layout Optional; One 'halfeye', 'eye', 'gradient' 'none' expected sampling error measure central tendency displayed. Currently, applies 'mean' selected measure central tendency error_scale Optional real number > 0 speciyin width expected sampling error visualization; default 0.3 error_nudge Optional amount error distribution offset; default 0.35 error_normalize Optional; One 'groups' (default), '', 'panels' specifying width expected sampling error distributions calculated. rope Optional two-item vector specifying region practical equivalence (ROPE) highlighted plot. point null hypothesis, pass value (e.g. c(0, 0) test point null exaclty 0); interval null pass ascending values (e.g. c(-1, 1)) ggtheme Optional ggplot2 theme object control overall styling; defaults ggplot2::theme_classic()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_magnitude.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the mean or median for a continuous variable — plot_magnitude","text":"Returns ggplot object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_magnitude.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot the mean or median for a continuous variable — plot_magnitude","text":"function developed primarily student use within jamovi learning along text book Introduction New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024). Expect breaking changes function improved general use. Work still done includes: Revise avoid deprecated ggplot features Revise consistent ability control aesthetics consistent layer names","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_magnitude.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot the mean or median for a continuous variable — plot_magnitude","text":"","code":"# From raw data data(\"data_penlaptop1\") estimate <- esci::estimate_magnitude( data = data_penlaptop1[data_penlaptop1$condition == \"Pen\", ], outcome_variable = transcription ) estimate #> Analysis of raw data: #> Data frame = data #> Outcome variable(s) = transcription #> #> ---Overview--- #> outcome_variable_name mean mean_LL mean_UL median median_LL median_UL #> 1 transcription 8.811765 7.154642 10.46889 8.6 6.5 11.1 #> sd min max q1 q3 n missing df mean_SE median_SE #> 1 4.749339 1 20.1 5.2 11.275 34 0 33 0.8145049 1.021201 #> #> -- es_mean -- #> outcome_variable_name effect effect_size LL UL SE #> 1 transcription transcription 8.811765 7.154642 10.46889 0.8145049 #> df ta_LL ta_UL #> 1 33 7.746606 9.876923 #> #> -- es_median -- #> outcome_variable_name effect effect_size LL UL SE df ta_LL #> 1 transcription transcription 8.6 6.5 11.1 1.021201 33 7.1 #> ta_UL #> 1 10.8 #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimate plot_magnitude(estimate) } # From summary data mymean <- 24.5 mysd <- 3.65 myn <- 40 estimate <- esci::estimate_magnitude( mean = mymean, sd = mysd, n = myn ) estimate #> Analysis of raw data: #> Outcome variable(s) = My outcome variable #> #> ---Overview--- #> outcome_variable_name mean mean_LL mean_UL sd n df mean_SE #> 1 My outcome variable 24.5 23.33267 25.66733 3.65 40 39 0.5771157 #> #> -- es_mean -- #> outcome_variable_name effect effect_size LL UL #> 1 My outcome variable My outcome variable 24.5 23.33267 25.66733 #> SE df ta_LL ta_UL #> 1 0.5771157 39 23.74765 25.25235 #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimate plot_mdiff(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_mdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots for comparing continuous outcome variables between conditions — plot_mdiff","title":"Plots for comparing continuous outcome variables between conditions — plot_mdiff","text":"plot_mdiff helps visualize comparisons continuous outcome variable conditions. can plot raw data (available) condition, mean median (raw data ) condition, emphasizes 1-df comparison among conditions, plotting estimated difference confidence interval difference axis. can pass esci-estimate objects generated estimate_mdiff_one(), estimate_mdiff_two(), estimate_mdiff_paired(), estimate_mdiff_ind_contrast(), estimate_mdiff_2x2_between(), estimate_mdiff_2x2_mixed(). function returns ggplot2 object.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_mdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots for comparing continuous outcome variables between conditions — plot_mdiff","text":"","code":"plot_mdiff( estimate, effect_size = c(\"mean\", \"median\"), data_layout = c(\"random\", \"swarm\", \"none\"), data_spread = 0.15, error_layout = c(\"halfeye\", \"eye\", \"gradient\", \"none\"), error_scale = 0.3, error_nudge = 0.4, error_normalize = c(\"groups\", \"all\", \"panels\"), difference_axis_units = c(\"raw\", \"sd\"), difference_axis_breaks = 5, difference_axis_space = 1, simple_contrast_labels = TRUE, ylim = c(NA, NA), ybreaks = 5, rope = c(NA, NA), rope_units = c(\"raw\", \"sd\"), ggtheme = NULL )"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_mdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots for comparing continuous outcome variables between conditions — plot_mdiff","text":"estimate esci-estimate object generated estimate_mdiff_ function effect_size Optional; one 'mean' 'median' determine measure central tendency plotted. Note median available estimate generated raw data. Defauls 'mean' data_layout Optional; one 'random', 'swarm', 'none' determine raw data (available) displayed. Defaults 'random' data_spread Optional numeric determining width raw data use condition. Defaults 0.15 (relative 1 unit per condition) error_layout Optional; one 'halfeye', 'eye', 'gradient' 'none' determine expected error distribution displayed estimated parameter. Defaults 'halfeye'. Currently apply 'median' selected effect size, case simple error bar used error_scale Optional numeric determining width expected error distribution. Defaults 0.3 error_nudge Optional numeric determining degree measures central tendency shifted right raw data; defaults 0.4 error_normalize Optional; one 'groups', '', 'panels' determine width expected error distributions normalized. Defaults 'groups'. See documentation ggdist difference_axis_units Optional; one 'raw' 'sd' determine markings difference axis raw-score units standard-deviation units. 'sd' standard deviation mean difference used, true even 'median' selected effect size difference_axis_breaks Optional numeric > 1 suggested number breaks difference axis. Defaults 5 difference_axis_space Optional numeric > 0 indicate spacing difference axis. Defaults 1 simple_contrast_labels Optional logical determine contrasts given simple labels ('Reference', 'Comparison', 'Difference') descriptive labels based contrast specified. ylim Optional 2-item vector specifying y-axis limits. Defaults c(NA NA); Use NA specify auto-limit. ybreaks Optional numeric > 2 suggested number y-axis breaks; defaults 5 rope Optional 2-item vector item 2 >= item 1. Use specify range values use visualize hypothesis test. values , point-null hypothesis test visualized. item2 > item1 interval-null hypothesis test visualized. Defaults c(NA, NA), visualize hypothesis test rope_units Optional; one 'raw' 'sd' indicate units rope passed. Defaults 'raw' ggtheme Optional ggplot2 theme object specify visual style plot. Defaults ggplot2::theme_classic()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_mdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots for comparing continuous outcome variables between conditions — plot_mdiff","text":"Returns ggplot object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_mdiff.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plots for comparing continuous outcome variables between conditions — plot_mdiff","text":"function developed primarily student use within jamovi learning along text book Introduction New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024). Expect breaking changes function improved general use. Work still done includes: Revise avoid deprecated ggplot features Revise consistent ability control aesthetics consistent layer names","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_mdiff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots for comparing continuous outcome variables between conditions — plot_mdiff","text":"","code":"# From summary data estimate <- estimate_mdiff_two( comparison_mean = 12.09, comparison_sd = 5.52, comparison_n = 103, reference_mean = 6.88, reference_sd = 4.22, reference_n = 48, grouping_variable_levels = c(\"Ref-Laptop\", \"Comp-Pen\"), outcome_variable_name = \"% Transcription\", grouping_variable_name = \"Note-taking type\", assume_equal_variance = TRUE ) if (FALSE) { # To visualize the estimated mean difference plot_mdiff(estimate, effect_size = \"mean\") } # From raw data data(\"data_penlaptop1\") estimate <- esci::estimate_mdiff_two( data = data_penlaptop1, outcome_variable = transcription, grouping_variable = condition, switch_comparison_order = TRUE, assume_equal_variance = TRUE ) if (FALSE) { # To visualize the estimated median difference (raw data only) plot_mdiff(estimate, effect_size = \"median\") }"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_meta.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a forest plot displaying results of a meta-analysis — plot_meta","title":"Generates a forest plot displaying results of a meta-analysis — plot_meta","text":"`plot_meta' returns ggplot2 object visualizing results meta-analysis, showing study effect size CI, overall effect size CI diamond, effect sizes estimated moderator level (defined), (optionally) prediction intervals subsequent studies. function requires input esci_estimate object generated esci meta-analysis function: meta_any(), meta_d1(), meta_d2(), meta_mdiff_two(), meta_mean(), meta_pdiff_two(), meta_proportion(), meta_r().","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_meta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a forest plot displaying results of a meta-analysis — plot_meta","text":"","code":"plot_meta( estimate, mark_zero = TRUE, include_PIs = FALSE, report_CIs = FALSE, explain_DR = FALSE, meta_diamond_height = 0.35, ggtheme = ggplot2::theme_classic() )"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_meta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a forest plot displaying results of a meta-analysis — plot_meta","text":"estimate esci_estimate object generated esci meta_ function mark_zero Boolean; defaults TRUE include dotted line indicated effect (effect_size = 0) include_PIs Boolean; defaults FALSE; set TRUE include prediction intervals overall effect moderator level (defined) report_CIs Boolean; defaults FALSE; set TRUE include printed representation study effect size CI along right- hand figure explain_DR Boolean; dedaults FALSE; set TRUE moderator defined show RE FE effect sizes represent diamond ration measure effect-size heterogeneity calculated meta_diamond_height Optional real number > 0 indicate height meta-analytic diamond drawn; defaults 0.35 ggtheme Optional ggplot2 theme object control overall styling; defaults ggplot2::theme_classic()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_meta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a forest plot displaying results of a meta-analysis — plot_meta","text":"Returns ggplot object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_meta.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generates a forest plot displaying results of a meta-analysis — plot_meta","text":"function developed primarily student use within jamovi learning along text book Introduction New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024). Expect breaking changes function improved general use. Work still done includes: Revise avoid deprecated ggplot features Revise consistent ability control aesthetics consistent layer names","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_meta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a forest plot displaying results of a meta-analysis — plot_meta","text":"","code":"# Data set -- see Introduction to the New Statistics, 1st edition brain_scan_persuasion <- data.frame( study_name = c(\"McCabe 1\", \"McCabe 2\", paste(\"Michael\", seq(1:10), sep = \" \")), nbM = c(2.89, 2.69, 2.90, 2.62, 2.96, 2.93, 2.86, 2.50, 2.41, 2.54, 2.73, 2.66), nbS = c(0.79, 0.55, 0.58, 0.54, 0.36, 0.60, 0.59, 0.84, 0.78, 0.66, 0.67, 0.65), nbN = c(28, 26, 98, 42, 24, 184, 274, 58, 34, 99, 98, 94), bM = c(3.12, 3.00, 2.86, 2.85, 3.07, 2.89, 2.91, 2.60, 2.74, 2.72, 2.68, 2.64), bS = c(0.65, 0.54, 0.61, 0.57, 0.55, 0.60, 0.52, 0.83, 0.51, 0.68, 0.69, 0.71), bN = c(26, 28, 99, 33, 21, 184, 255, 55, 34, 95, 93, 97), mod = as.factor( c(\"Simple\", \"Critique\", \"Simple\",\"Simple\",\"Simple\", \"Simple\",\"Simple\",\"Critique\",\"Critique\",\"Critique\", \"Critique\",\"Critique\") ), ds = c(0.31217944, 0.56073138, -0.06693802, 0.41136192, 0.23581389, -0.06652995, 0.08958082, 0.11892778, 0.49506069, 0.26765910, -0.07326, -0.02925) ) # Meta-analysis: random effects, no moderator estimate <- esci::meta_mdiff_two( data = brain_scan_persuasion, comparison_means = bM, comparison_sds = bS, comparison_ns = bN, reference_means = nbM, reference_sds = nbS, reference_ns = nbN, labels = study_name, effect_label = \"Brain Photo Rating - No Brain Photo Rating\", assume_equal_variance = TRUE, random_effects = TRUE ) if (FALSE) { # Forest plot esci::plot_meta(estimate) } # Meta-analysis: random effects, moderator if (FALSE) { # Forest plot esci::plot_meta(estimate) } # Meta-analysis: random effects, moderator, output d_s if (FALSE) { # Forest plot esci::plot_meta(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_pdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots for comparing categorical outcome variables between conditions — plot_pdiff","title":"Plots for comparing categorical outcome variables between conditions — plot_pdiff","text":"plot_pdiff helps visualize comparisons categorical outcome variable conditions. plots proportions cases level grouping variable emphasizes 1-df comparison among conditions, plotting estimated difference confidence interval difference axis. can pass esci-estimate objects generated estimate_pdiff_one(), estimate_pdiff_two(), estimate_pdiff_paired(), estimate_pdiff_ind_contrast() function returns ggplot2 object.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_pdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots for comparing categorical outcome variables between conditions — plot_pdiff","text":"","code":"plot_pdiff( estimate, error_layout = c(\"halfeye\", \"eye\", \"gradient\", \"none\"), error_scale = 0.3, error_normalize = c(\"groups\", \"all\", \"panels\"), difference_axis_breaks = 5, difference_axis_space = 1, simple_contrast_labels = TRUE, ylim = c(NA, NA), ybreaks = 5, rope = c(NA, NA), ggtheme = NULL )"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_pdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots for comparing categorical outcome variables between conditions — plot_pdiff","text":"estimate esci-estimate object generated estimate_pdiff_ function error_layout Optional; one 'halfeye', 'eye', 'gradient' 'none' determine expected error distribution displayed estimated parameter. Defaults 'halfeye'. Currently apply 'median' selected effect size, case simple error bar used error_scale Optional numeric determining width expected error distribution. Defaults 0.3 error_normalize Optional; one 'groups', '', 'panels' determine width expected error distributions normalized. Defaults 'groups'. See documentation ggdist difference_axis_breaks Optional numeric > 1 suggested number breaks difference axis. Defaults 5 difference_axis_space Optional numeric > 0 indicate spacing difference axis. Defaults 1 simple_contrast_labels Optional logical determine contrasts given simple labels ('Reference', 'Comparison', 'Difference') descriptive labels based contrast specified. ylim Optional 2-item vector specifying y-axis limits. Defaults c(NA NA); Use NA specify auto-limit. ybreaks Optional numeric > 2 suggested number y-axis breaks; defaults 5 rope Optional 2-item vector item 2 >= item 1. Use specify range values use visualize hypothesis test. values , point-null hypothesis test visualized. item2 > item1 interval-null hypothesis test visualized. Defaults c(NA, NA), visualize hypothesis test ggtheme Optional ggplot2 theme object specify visual style plot. Defaults ggplot2::theme_classic()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_pdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots for comparing categorical outcome variables between conditions — plot_pdiff","text":"Returns ggplot object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_pdiff.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plots for comparing categorical outcome variables between conditions — plot_pdiff","text":"function developed primarily student use within jamovi learning along text book Introduction New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024). Expect breaking changes function improved general use. Work still done includes: Revise avoid deprecated ggplot features Revise consistent ability control aesthetics consistent layer names","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_pdiff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots for comparing categorical outcome variables between conditions — plot_pdiff","text":"","code":"estimate <- estimate_pdiff_two( comparison_cases = 10, comparison_n = 20, reference_cases = 78, reference_n = 252, grouping_variable_levels = c(\"Original\", \"Replication\"), conf_level = 0.95 ) if (FALSE) { # To visualize the estimate plot_pdiff(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot an estimated proportion — plot_proportion","title":"Plot an estimated proportion — plot_proportion","text":"plot_proportion creates ggplot2 plot suitable visualizing estimated proportion categorical variable. function can passed esci_estimate object generated estimate_proportion()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot an estimated proportion — plot_proportion","text":"","code":"plot_proportion( estimate, error_layout = c(\"halfeye\", \"eye\", \"gradient\", \"none\"), error_scale = 0.3, error_normalize = c(\"groups\", \"all\", \"panels\"), rope = c(NA, NA), plot_possible = FALSE, ggtheme = NULL )"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_proportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot an estimated proportion — plot_proportion","text":"estimate esci_estimate object generated estimate_proportion() error_layout Optional; One 'halfeye', 'eye', 'gradient' 'none' expected sampling error measure central tendency displayed. Caution - displayed error distributions seem correct yet error_scale Optional real number > 0 speciyin width expected sampling error visualization; default 0.3 error_normalize Optional; One 'groups' (default), '', 'panels' specifying width expected sampling error distributions calculated. rope Optional two-item vector specifying region practical equivalence (ROPE) highlighted plot. point null hypothesis, pass value (e.g. c(0, 0) test point null exaclty 0); interval null pass ascending values (e.g. c(-1, 1)) plot_possible Boolean; defaults FALSE; TRUE plot lines discrete proportion possible given sample size (e.g proportion 10 total cases, draw lines 0, .1, .2, etc.) ggtheme Optional ggplot2 theme object control overall styling; defaults ggplot2::theme_classic()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot an estimated proportion — plot_proportion","text":"Returns ggplot object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_proportion.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot an estimated proportion — plot_proportion","text":"function developed primarily student use within jamovi learning along text book Introduction New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024). Expect breaking changes function improved general use. Work still done includes: Revise avoid deprecated ggplot features Revise consistent ability control aesthetics consistent layer names","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_proportion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot an estimated proportion — plot_proportion","text":"","code":"estimate <- esci::estimate_proportion( cases = c(8, 22-8), outcome_variable_levels = c(\"Affected\", \"Not Affected\") ) if (FALSE) { # To visualize the estimate plot_proportion(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_rdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots for comparing Pearson r values between conditions — plot_rdiff","title":"Plots for comparing Pearson r values between conditions — plot_rdiff","text":"plot_rdiff helps visualize comparisons Pearson's r estimates conditions. plots Pearson's r value level grouping variable emphasizes 1-df comparison among conditions, plotting estimated difference confidence interval difference axis. can pass esci-estimate objects generated estimate_rdiff_two(). function returns ggplot2 object.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_rdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots for comparing Pearson r values between conditions — plot_rdiff","text":"","code":"plot_rdiff( estimate, error_layout = c(\"halfeye\", \"eye\", \"gradient\", \"none\"), error_scale = 0.3, error_normalize = c(\"groups\", \"all\", \"panels\"), difference_axis_breaks = 5, simple_contrast_labels = TRUE, ylim = c(NA, NA), ybreaks = 5, rope = c(NA, NA), ggtheme = NULL )"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_rdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots for comparing Pearson r values between conditions — plot_rdiff","text":"estimate esci-estimate object generated estimate_pdiff_ function error_layout Optional; one 'halfeye', 'eye', 'gradient' 'none' determine expected error distribution displayed estimated parameter. Defaults 'halfeye'. Currently apply 'median' selected effect size, case simple error bar used error_scale Optional numeric determining width expected error distribution. Defaults 0.3 error_normalize Optional; one 'groups', '', 'panels' determine width expected error distributions normalized. Defaults 'groups'. See documentation ggdist difference_axis_breaks Optional numeric > 1 suggested number breaks difference axis. Defaults 5 simple_contrast_labels Optional logical determine contrasts given simple labels ('Reference', 'Comparison', 'Difference') descriptive labels based contrast specified. ylim Optional 2-item vector specifying y-axis limits. Defaults c(NA NA); Use NA specify auto-limit. ybreaks Optional numeric > 2 suggested number y-axis breaks; defaults 5 rope Optional 2-item vector item 2 >= item 1. Use specify range values use visualize hypothesis test. values , point-null hypothesis test visualized. item2 > item1 interval-null hypothesis test visualized. Defaults c(NA, NA), visualize hypothesis test ggtheme Optional ggplot2 theme object specify visual style plot. Defaults ggplot2::theme_classic()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_rdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots for comparing Pearson r values between conditions — plot_rdiff","text":"Returns ggplot object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_rdiff.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plots for comparing Pearson r values between conditions — plot_rdiff","text":"function developed primarily student use within jamovi learning along text book Introduction New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024). Expect breaking changes function improved general use. Work still done includes: Revise avoid deprecated ggplot features Revise consistent ability control aesthetics consistent layer names","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_rdiff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots for comparing Pearson r values between conditions — plot_rdiff","text":"","code":"# From summary data estimate <- esci::estimate_rdiff_two( comparison_r = .53, comparison_n = 45, reference_r = .41, reference_n = 59, grouping_variable_levels = c(\"Females\", \"Males\"), x_variable_name = \"Satisfaction with life\", y_variable_name = \"Body satisfaction\", grouping_variable_name = \"Gender\", conf_level = .95 ) estimate #> Analysis of summary data: #> #> -- es_r -- #> grouping_variable_name grouping_variable_level x_variable_name #> 1 Gender Females Satisfaction with life #> 2 Gender Males Satisfaction with life #> y_variable_name effect effect_size LL UL SE n df #> 1 Body satisfaction Females 0.41 0.1685419 0.6005378 0.1092338 59 57 #> 2 Body satisfaction Males 0.53 0.2744717 0.7096862 0.1084084 45 43 #> ta_LL ta_UL #> 1 0.2091420 0.5729339 #> 2 0.3188047 0.6847105 #> #> -- es_r_difference -- #> type grouping_variable_name grouping_variable_level #> 1 Comparison Gender Males #> 2 Reference Gender Females #> 3 Difference Gender Males - Females #> x_variable_name y_variable_name effect effect_size #> 1 Satisfaction with life Body satisfaction Males 0.53 #> 2 Satisfaction with life Body satisfaction Females 0.41 #> 3 Satisfaction with life Body satisfaction Males - Females 0.12 #> LL UL SE n df ta_LL ta_UL rz sem #> 1 0.2744717 0.7096862 0.1084084 45 43 0.3188047 0.6847105 0.5901452 0.1543033 #> 2 0.1685419 0.6005378 0.1092338 59 57 0.2091420 0.5729339 0.4356112 0.1336306 #> 3 -0.1987466 0.4209803 NA NA NA -0.1467413 0.3735336 0.1545339 0.2041241 #> z p #> 1 3.8245778 0.0001309964 #> 2 3.2598159 0.0011148455 #> 3 0.7570586 0.4490147644 #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. # To evaluate a hypothesis (interval null from -0.1 to 0.1): esci::test_rdiff(estimate) #> $properties #> $properties$effect_size_name #> [1] \"r\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] FALSE #> #> $properties$rope #> [1] 0 0 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name #> 1 Nil Hypothesis Test Satisfaction with life and Body satisfaction #> effect null_words confidence LL UL #> 1 Males - Females 0.00 95 -0.1987466 0.4209803 #> CI CI_compare t df p #> 1 95% CI [-0.1987466, 0.4209803] The 95% CI contains H_0 0.7570586 NA 0.4490148 #> p_result null_decision #> 1 p ≥ 0.05 Fail to reject H_0 #> conclusion significant #> 1 At α = 0.05, 0.00 remains a plausible value of Ͱ_diff FALSE #> if (FALSE) { # To visualize the values of r and their difference esci::plot_rdiff(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_scatter.html","id":null,"dir":"Reference","previous_headings":"","what":"Generates a scatter plot of data for two continuous variables — plot_scatter","title":"Generates a scatter plot of data for two continuous variables — plot_scatter","text":"plot_scatter returns ggplot2 object data two continuous variables. Can indicate regression line confidence interval,prediction intervals regression residuals . function requires input esci_estimate object generated estimate_r()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_scatter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generates a scatter plot of data for two continuous variables — plot_scatter","text":"","code":"plot_scatter( estimate, show_line = FALSE, show_line_CI = FALSE, show_PI = FALSE, show_residuals = FALSE, show_mean_lines = FALSE, show_r = FALSE, predict_from_x = NULL, plot_as_z = FALSE, ggtheme = ggplot2::theme_classic() )"},{"path":"https://rcalinjageman.github.io/esci/reference/plot_scatter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generates a scatter plot of data for two continuous variables — plot_scatter","text":"estimate esci_estimate object generated estimate_r() show_line Boolean; defaults FALSE; set TRUE show regression line show_line_CI Boolean; defaults FALSE; set TRUE show confidence interval regression line show_PI Boolean; defaults FALSE; set TRUE show prediction intervals show_residuals Boolean; defaults FALSE; set TRUE show residuals prediction show_mean_lines Boolean; defaults FALSE; set TRUE plot lines showing mean variable show_r Boolean; defaults FALSE; set TRUE print r value CI plot predict_from_x Optional real number range x variable plot; Defaults NULL; passed, graph shows predicted Y' x value plot_as_z Boolean; defaults FALSE; set TRUE convert x y scores z scores prior plotting ggtheme Optional ggplot2 theme object control overall styling; defaults ggplot2::theme_classic()","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_scatter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generates a scatter plot of data for two continuous variables — plot_scatter","text":"Returns ggplot object","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_scatter.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generates a scatter plot of data for two continuous variables — plot_scatter","text":"function developed primarily student use within jamovi learning along text book Introduction New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024). Expect breaking changes function improved general use. Work still done includes: Revise avoid deprecated ggplot features Revise consistent ability control aesthetics consistent layer names","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/plot_scatter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generates a scatter plot of data for two continuous variables — plot_scatter","text":"","code":"# From raw data thomason1 <- data.frame( ls_pre = c( 13, 12, 12, 9, 14, 17, 14, 9, 6, 7, 11, 15 ), ls_post = c( 14, 13, 16, 12, 15, 18, 13, 10, 10, 8, 14, 16 ) ) estimate <- esci::estimate_r( thomason1, ls_pre, ls_post ) estimate #> Analysis of raw data: #> Data frame = data #> #> ---Overview--- #> outcome_variable_name mean mean_LL mean_UL median median_LL median_UL #> 1 ls_pre 11.58333 9.476776 13.68989 12.0 9 14 #> 2 ls_post 13.25000 11.410019 15.08998 13.5 10 16 #> sd min max q1 q3 n missing df mean_SE median_SE #> 1 3.315483 6 17 9.0 14.00 12 0 11 0.9570974 1.208488 #> 2 2.895922 8 18 11.5 15.25 12 0 11 0.8359806 1.450186 #> #> -- es_r -- #> x_variable_name y_variable_name effect effect_size LL #> 1 ls_pre ls_post ls_pre and ls_post 0.8923908 0.62894 #> UL SE n df ta_LL ta_UL #> 1 0.967157 0.06139936 12 10 0.6882891 0.9596343 #> #> -- regression -- #> component values LL UL #> 1 Intercept (a) 4.2212267 0.8856544 7.556799 #> 2 Slope (b) 0.7794624 0.5017391 1.057186 #> [1] \"Ŷ = 4.221 + 0.7795*X\" #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. # To evaluate a hypothesis (interval null from -0.1 to 0.1): test_correlation(estimate, rope = c(-0.1, 0.1)) #> $properties #> $properties$effect_size_name #> [1] \"r\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] TRUE #> #> $properties$rope #> [1] -0.1 0.1 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name effect null_words #> 1 Nil Hypothesis Test ls_pre and ls_post ls_pre and ls_post 0.00 #> confidence LL UL CI #> 1 95 0.62894 0.967157 95% CI [0.62894, 0.967157] #> CI_compare t df p p_result #> 1 The 95% CI does not contain H_0 4.300635 10 1.703091e-05 p < 0.05 #> null_decision conclusion significant #> 1 Reject H_0 At α = 0.05, 0.00 is not a plausible value of Ͱ TRUE #> #> $interval_null #> test_type outcome_variable_name effect #> 1 Practical significance test ls_pre and ls_post ls_pre and ls_post #> rope confidence #> 1 (-0.10, 0.10) 95 #> CI #> 1 95% CI [0.62894, 0.967157]\\n90% CI [0.6882891, 0.9596343] #> rope_compare p_result conclusion #> 1 95% CI fully outside H_0 p < 0.05 At α = 0.05, conclude Ͱ is substantive #> significant #> 1 TRUE #> if (FALSE) { # To visualize the value of r plot_correlation(estimate) # To visualize the data (scatterplot) plot_scatter(estimate) # To visualize the data (scatterplot) and use regression to obtain Y' from X plot_scatter(estimate, predict_from_x = 10) } # From summary data estimate <- esci::estimate_r(r = 0.536, n = 50) estimate #> Analysis of summary data: #> #> -- es_r -- #> x_variable_name y_variable_name effect effect_size #> 1 My x variable My y variable My x variable and My y variable 0.536 #> LL UL SE n df ta_LL ta_UL #> 1 0.2978573 0.7058914 0.1018149 50 48 0.3391487 0.6820747 #> #> #> Note: LL and UL are lower and upper boundaries of confidence intervals with 95% expected coverage. if (FALSE) { # To visualize the estimate plot_correlation(estimate) }"},{"path":"https://rcalinjageman.github.io/esci/reference/print.esci_estimate.html","id":null,"dir":"Reference","previous_headings":"","what":"Print an esci_estimate — print.esci_estimate","title":"Print an esci_estimate — print.esci_estimate","text":"Pretties printing complex esci_estimate object.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/print.esci_estimate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print an esci_estimate — print.esci_estimate","text":"","code":"# S3 method for esci_estimate print(x, ..., verbose = FALSE)"},{"path":"https://rcalinjageman.github.io/esci/reference/print.esci_estimate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print an esci_estimate — print.esci_estimate","text":"x object print; must class esci_estimate ... S3 signature generic plot function. verbose optional logical print details; defaults false","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_correlation.html","id":null,"dir":"Reference","previous_headings":"","what":"Test a hypothesis about the strength of a Pearson's r correlation — test_correlation","title":"Test a hypothesis about the strength of a Pearson's r correlation — test_correlation","text":"test_correlation suitable testing hypothesis strength correlation two continuous variables (designs Pearson's r suitable measure correlation).","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_correlation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test a hypothesis about the strength of a Pearson's r correlation — test_correlation","text":"","code":"test_correlation(estimate, rope = c(0, 0), output_html = FALSE)"},{"path":"https://rcalinjageman.github.io/esci/reference/test_correlation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test a hypothesis about the strength of a Pearson's r correlation — test_correlation","text":"estimate esci_estimate object generated estimate_r function rope two-element vector defining Region Practical Equivalence (ROPE). Specify c(0, 0) test point null exactly 0. Specify two ascending values -1 1 test interval null (e.g. c(.25, .45) test hypothesis Pearson's r population (rho) .25 .45). output_html TRUE return results HTML; FALSE (default) return standard output","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_correlation.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test a hypothesis about the strength of a Pearson's r correlation — test_correlation","text":"Returns list 1-2 data frames point_null - always returned test_type - 'Nil hypothesis test', meaning test H0 = 0 outcome_variable_name - Name outcome variable effect - Label effect tested null_words - Express null words confidence - Confidence level, integer (95 95%, etc.) LL - Lower boundary confidence% CI effect UL - Upper boundary confidence% CI effect CI - Character representation CI effect CI_compare - Text description relation CI null t - applicable, t value hypothesis test df - applicable, degrees freedom hypothesis test p - applicable, p value hypothesis test p_result - Text representation p value obtained null_decision - Text represention decision null conclusion - Text representation conclusion draw significant - TRUE/FALSE significant alpha = 1-CI interval_null - returned interval null specified test_type - 'Practical significance test', meaning test interval null outcome_variable_name - effect - Name outcome variable rope - Test representaiton null interval confidence - Confidence level, integer (95 95%, etc.) CI - Character representation CI effect rope_compare - Text description relation CI null interval p_result - Text representation p value obtained conclusion - Text representation conclusion draw significant - TRUE/FALSE significant alpha = 1-CI","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_correlation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Test a hypothesis about the strength of a Pearson's r correlation — test_correlation","text":"function can passed esci_estimate object generated estimate_r(). can test hypotheses specific value difference (point null) range values (interval null)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_correlation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test a hypothesis about the strength of a Pearson's r correlation — test_correlation","text":"","code":"# example code estimate <- esci::estimate_r(r = 0.536, n = 50) # Test against a point null of exactly 0 test_correlation(estimate) #> $properties #> $properties$effect_size_name #> [1] \"r\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] FALSE #> #> $properties$rope #> [1] 0 0 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name #> 1 Nil Hypothesis Test My x variable and My y variable #> effect null_words confidence LL UL #> 1 My x variable and My y variable 0.00 95 0.2978573 0.7058914 #> CI CI_compare t df #> 1 95% CI [0.2978573, 0.7058914] The 95% CI does not contain H_0 4.103289 48 #> p p_result null_decision #> 1 4.073183e-05 p < 0.05 Reject H_0 #> conclusion significant #> 1 At α = 0.05, 0.00 is not a plausible value of Ͱ TRUE #> # Test against an interval null (-0.1, 0.1) test_correlation(estimate, rope = c(-0.1, 0.1)) #> $properties #> $properties$effect_size_name #> [1] \"r\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] TRUE #> #> $properties$rope #> [1] -0.1 0.1 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name #> 1 Nil Hypothesis Test My x variable and My y variable #> effect null_words confidence LL UL #> 1 My x variable and My y variable 0.00 95 0.2978573 0.7058914 #> CI CI_compare t df #> 1 95% CI [0.2978573, 0.7058914] The 95% CI does not contain H_0 4.103289 48 #> p p_result null_decision #> 1 4.073183e-05 p < 0.05 Reject H_0 #> conclusion significant #> 1 At α = 0.05, 0.00 is not a plausible value of Ͱ TRUE #> #> $interval_null #> test_type outcome_variable_name #> 1 Practical significance test My x variable and My y variable #> effect rope confidence #> 1 My x variable and My y variable (-0.10, 0.10) 95 #> CI #> 1 95% CI [0.2978573, 0.7058914]\\n90% CI [0.3391487, 0.6820747] #> rope_compare p_result conclusion #> 1 95% CI fully outside H_0 p < 0.05 At α = 0.05, conclude Ͱ is substantive #> significant #> 1 TRUE #>"},{"path":"https://rcalinjageman.github.io/esci/reference/test_mdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Test a hypothesis about a difference in a continuous outcome variable. — test_mdiff","title":"Test a hypothesis about a difference in a continuous outcome variable. — test_mdiff","text":"test_mdiff suitable conducting testing hypothesis magnitude difference two conditions continuous outcome variable. can test hypotheses differences means medians independent paired designs.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_mdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test a hypothesis about a difference in a continuous outcome variable. — test_mdiff","text":"","code":"test_mdiff( estimate, effect_size = c(\"mean\", \"median\"), rope = c(0, 0), rope_units = c(\"raw\", \"sd\"), output_html = FALSE )"},{"path":"https://rcalinjageman.github.io/esci/reference/test_mdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test a hypothesis about a difference in a continuous outcome variable. — test_mdiff","text":"estimate esci_estimate object generated estimate_mdiff_ function effect_size One 'mean' 'median'. effect size selected must available esci_estimate object; medians available estimate generated raw data. rope two-element vector defining Region Practical Equivalence (ROPE). Specify c(0, 0) test point null exactly 0. Specify two ascending values test interval null (e.g. c(-1, 1) test hypothesis tha difference -1 1). rope_units One 'raw' (default) 'sd', specifies units ROPE. 'sd' specified, rope defined standard deviation units (e.g. c(-1, 1) taken -1 1 standard deviations 0). sd used, ROPE converted raw scores test conducted raw scores. output_html TRUE return results HTML; FALSE (default) return standard output","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_mdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test a hypothesis about a difference in a continuous outcome variable. — test_mdiff","text":"Returns list 1-2 data frames point_null - always returned test_type - 'Nil hypothesis test', meaning test H0 = 0 outcome_variable_name - Name outcome variable effect - Label effect tested null_words - Express null words confidence - Confidence level, integer (95 95%, etc.) LL - Lower boundary confidence% CI effect UL - Upper boundary confidence% CI effect CI - Character representation CI effect CI_compare - Text description relation CI null t - applicable, t value hypothesis test df - applicable, degrees freedom hypothesis test p - applicable, p value hypothesis test p_result - Text representation p value obtained null_decision - Text represention decision null conclusion - Text representation conclusion draw significant - TRUE/FALSE significant alpha = 1-CI interval_null - returned interval null specified test_type - 'Practical significance test', meaning test interval null outcome_variable_name - effect - Name outcome variable rope - Test representaiton null interval confidence - Confidence level, integer (95 95%, etc.) CI - Character representation CI effect rope_compare - Text description relation CI null interval p_result - Text representation p value obtained conclusion - Text representation conclusion draw significant - TRUE/FALSE significant alpha = 1-CI","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_mdiff.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Test a hypothesis about a difference in a continuous outcome variable. — test_mdiff","text":"function can passed esci_estimate object generated estimate_mdiff_one(), estimate_mdiff_two(), estimate_mdiff_paired(), estimate_mdiff_ind_contrast(). can test hypotheses specific value difference (point null) range values (interval null)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_mdiff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test a hypothesis about a difference in a continuous outcome variable. — test_mdiff","text":"","code":"# example code data(\"data_penlaptop1\") estimate <- esci::estimate_mdiff_two( data = data_penlaptop1, outcome_variable = transcription, grouping_variable = condition, switch_comparison_order = TRUE, assume_equal_variance = TRUE ) # Test mean difference against point null of 0 esci::test_mdiff( estimate, effect_size = \"mean\" ) #> $properties #> $properties$effect_size_name #> [1] \"mean\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] FALSE #> #> $properties$rope #> [1] 0 0 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name effect null_words confidence #> 1 Nil Hypothesis Test transcription Pen ‒ Laptop 0.00 95 #> LL UL CI #> 1 -8.729915 -2.685265 95% CI [-8.729915, -2.685265] #> CI_compare t df p p_result #> 1 The 95% CI does not contain H_0 -3.77382 63 0.0003579282 p < 0.05 #> null_decision conclusion #> 1 Reject H_0 At α = 0.05, 0.00 is not a plausible value of μ_diff #> significant #> 1 TRUE #> # Test median difference against point null of 0 # Note that t, df, p return NA because test is completed # by interval. esci::test_mdiff( estimate, effect_size = \"median\" ) #> $properties #> $properties$effect_size_name #> [1] \"median\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] FALSE #> #> $properties$rope #> [1] 0 0 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name effect null_words confidence #> 1 Nil Hypothesis Test transcription Pen ‒ Laptop 0.00 95 #> LL UL CI #> 1 -8.085644 -0.3143563 95% CI [-8.085644, -0.3143563] #> CI_compare t df p p_result null_decision #> 1 The 95% CI does not contain H_0 NA NA NA p < 0.05 Reject H_0 #> conclusion significant #> 1 At α = 0.05, 0.00 is not a plausible value of η_diff TRUE #> # Test mean difference against interval null of -10 to 10 esci::test_mdiff( estimate, effect_size = \"mean\", rope = c(-10, 10) ) #> $properties #> $properties$effect_size_name #> [1] \"mean\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] TRUE #> #> $properties$rope #> [1] -10 10 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name effect null_words confidence #> 1 Nil Hypothesis Test transcription Pen ‒ Laptop 0.00 95 #> LL UL CI #> 1 -8.729915 -2.685265 95% CI [-8.729915, -2.685265] #> CI_compare t df p p_result #> 1 The 95% CI does not contain H_0 -3.77382 63 0.0003579282 p < 0.05 #> null_decision conclusion #> 1 Reject H_0 At α = 0.05, 0.00 is not a plausible value of μ_diff #> significant #> 1 TRUE #> #> $interval_null #> test_type outcome_variable_name effect #> 1 Practical significance test transcription Pen ‒ Laptop #> rope confidence #> 1 (-10.00, 10.00) 95 #> CI #> 1 95% CI [-8.729915, -2.685265]\\n90% CI [-8.232423, -3.182757] #> rope_compare p_result conclusion #> 1 90% CI fully inside H_0 p < 0.05 At α = 0.05, conclude μ_diff is negligible #> significant #> 1 TRUE #> # Test mean difference against interval null of d (-0.20, 0.20) d = 0.2 is often # thought of as a small effect, so this test examines if the effect is # negligible (clearly between negligble and small), substantive (clearly more # than small), or unclear. The d boundaries provided are converted to raw scores # and then the CI of the observed effect is compared to the raw-score boundaries esci::test_mdiff( estimate, effect_size = \"mean\", rope = c(-0.2, 0.2), rope_units = \"sd\" ) #> $properties #> $properties$effect_size_name #> [1] \"mean\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] TRUE #> #> $properties$rope #> [1] -0.2 0.2 #> #> $properties$rope_units #> [1] \"sd\" #> #> #> $point_null #> test_type outcome_variable_name effect null_words confidence #> 1 Nil Hypothesis Test transcription Pen ‒ Laptop 0.00 95 #> LL UL CI #> 1 -8.729915 -2.685265 95% CI [-8.729915, -2.685265] #> CI_compare t df p p_result #> 1 The 95% CI does not contain H_0 -3.77382 63 0.0003579282 p < 0.05 #> null_decision conclusion #> 1 Reject H_0 At α = 0.05, 0.00 is not a plausible value of μ_diff #> significant #> 1 TRUE #> #> $interval_null #> test_type outcome_variable_name effect #> 1 Practical significance test transcription Pen ‒ Laptop #> rope confidence #> 1 (-1.21805, 1.21805) 95 #> CI #> 1 95% CI [-8.729915, -2.685265]\\n90% CI [-8.232423, -3.182757] #> rope_compare p_result conclusion #> 1 95% CI fully outside H_0 p < 0.05 At α = 0.05, conclude μ_diff is substantive #> significant #> 1 TRUE #>"},{"path":"https://rcalinjageman.github.io/esci/reference/test_pdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Test a hypothesis about a difference in proportion — test_pdiff","title":"Test a hypothesis about a difference in proportion — test_pdiff","text":"test_pdiff suitable testing hypothesis difference proportions two conditions categorical outcome variable. can test hypotheses independent paired designs.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_pdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test a hypothesis about a difference in proportion — test_pdiff","text":"","code":"test_pdiff(estimate, rope = c(0, 0), output_html = FALSE)"},{"path":"https://rcalinjageman.github.io/esci/reference/test_pdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test a hypothesis about a difference in proportion — test_pdiff","text":"estimate esci_estimate object generated estimate_pdiff_ function rope two-element vector defining Region Practical Equivalence (ROPE). Specify c(0, 0) test point null exactly 0. Specify two ascending values -1 1 test interval null (e.g. c(-.25, .25) test hypothesis difference proportion -.25 .25). output_html TRUE return results HTML; FALSE (default) return standard output","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_pdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test a hypothesis about a difference in proportion — test_pdiff","text":"Returns list 1-2 data frames point_null - always returned test_type - 'Nil hypothesis test', meaning test H0 = 0 outcome_variable_name - Name outcome variable effect - Label effect tested null_words - Express null words confidence - Confidence level, integer (95 95%, etc.) LL - Lower boundary confidence% CI effect UL - Upper boundary confidence% CI effect CI - Character representation CI effect CI_compare - Text description relation CI null t - applicable, t value hypothesis test df - applicable, degrees freedom hypothesis test p - applicable, p value hypothesis test p_result - Text representation p value obtained null_decision - Text represention decision null conclusion - Text representation conclusion draw significant - TRUE/FALSE significant alpha = 1-CI interval_null - returned interval null specified test_type - 'Practical significance test', meaning test interval null outcome_variable_name - effect - Name outcome variable rope - Test representaiton null interval confidence - Confidence level, integer (95 95%, etc.) CI - Character representation CI effect rope_compare - Text description relation CI null interval p_result - Text representation p value obtained conclusion - Text representation conclusion draw significant - TRUE/FALSE significant alpha = 1-CI","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_pdiff.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Test a hypothesis about a difference in proportion — test_pdiff","text":"function can passed esci_estimate object generated estimate_pdiff_one(), estimate_pdiff_two(), estimate_pdiff_paired(), estimate_pdiff_ind_contrast(). can test hypotheses specific value difference (point null) range values (interval null)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_pdiff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test a hypothesis about a difference in proportion — test_pdiff","text":"","code":"estimate <- estimate_pdiff_two( comparison_cases = 10, comparison_n = 20, reference_cases = 78, reference_n = 252, grouping_variable_levels = c(\"Original\", \"Replication\"), conf_level = 0.95 ) # Test against null of exactly test_pdiff(estimate) #> $properties #> $properties$effect_size_name #> [1] \"P\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] FALSE #> #> $properties$rope #> [1] 0 0 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name case_label effect #> 1 Nil Hypothesis Test My outcome variable P_Affected Replication ‒ Original #> null_words confidence LL UL CI #> 1 0.00 95 -0.02757339 0.4055261 95% CI [-0.02757339, 0.4055261] #> CI_compare t df p p_result null_decision #> 1 The 95% CI contains H_0 1.710401 NA 0.08719178 p ≥ 0.05 Fail to reject H_0 #> conclusion significant #> 1 At α = 0.05, 0.00 remains a plausible value of Π_diff FALSE #> # Test against null of (-0.1, 0.1) test_pdiff(estimate, rope = c(-0.1, 0.1)) #> $properties #> $properties$effect_size_name #> [1] \"P\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] TRUE #> #> $properties$rope #> [1] -0.1 0.1 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name case_label effect #> 1 Nil Hypothesis Test My outcome variable P_Affected Replication ‒ Original #> null_words confidence LL UL CI #> 1 0.00 95 -0.02757339 0.4055261 95% CI [-0.02757339, 0.4055261] #> CI_compare t df p p_result null_decision #> 1 The 95% CI contains H_0 1.710401 NA 0.08719178 p ≥ 0.05 Fail to reject H_0 #> conclusion significant #> 1 At α = 0.05, 0.00 remains a plausible value of Π_diff FALSE #> #> $interval_null #> test_type outcome_variable_name case_label #> 1 Practical significance test My outcome variable P_Affected #> effect rope confidence #> 1 Replication ‒ Original (-0.10, 0.10) 95 #> CI #> 1 95% CI [-0.02757339, 0.4055261]\\n90% CI [0.007242087, 0.3707107] #> rope_compare p_result #> 1 95% CI has values inside and outside H_0 p ≥ 0.05 #> conclusion significant #> 1 At α = 0.05, not clear if Π_diff is substantive or negligible FALSE #>"},{"path":"https://rcalinjageman.github.io/esci/reference/test_rdiff.html","id":null,"dir":"Reference","previous_headings":"","what":"Test a hypothesis about a difference in correlation strength — test_rdiff","title":"Test a hypothesis about a difference in correlation strength — test_rdiff","text":"test_rdiff suitable testing hypothesis difference correlation (r) two conditions. moment, can test hypotheses independent-group designs.","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_rdiff.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Test a hypothesis about a difference in correlation strength — test_rdiff","text":"","code":"test_rdiff(estimate, rope = c(0, 0), output_html = FALSE)"},{"path":"https://rcalinjageman.github.io/esci/reference/test_rdiff.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Test a hypothesis about a difference in correlation strength — test_rdiff","text":"estimate esci_estimate object generated estimate_rdiff_ function rope two-element vector defining Region Practical Equivalence (ROPE). Specify c(0, 0) test point null exactly 0. Specify two ascending values -1 1 test interval null (e.g. c(-.25, .25) test hypothesis difference correlation -.25 .25). output_html TRUE return results HTML; FALSE (default) return standard output","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_rdiff.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Test a hypothesis about a difference in correlation strength — test_rdiff","text":"Returns list 1-2 data frames point_null - always returned test_type - 'Nil hypothesis test', meaning test H0 = 0 outcome_variable_name - Name outcome variable effect - Label effect tested null_words - Express null words confidence - Confidence level, integer (95 95%, etc.) LL - Lower boundary confidence% CI effect UL - Upper boundary confidence% CI effect CI - Character representation CI effect CI_compare - Text description relation CI null t - applicable, t value hypothesis test df - applicable, degrees freedom hypothesis test p - applicable, p value hypothesis test p_result - Text representation p value obtained null_decision - Text represention decision null conclusion - Text representation conclusion draw significant - TRUE/FALSE significant alpha = 1-CI interval_null - returned interval null specified test_type - 'Practical significance test', meaning test interval null outcome_variable_name - effect - Name outcome variable rope - Test representaiton null interval confidence - Confidence level, integer (95 95%, etc.) CI - Character representation CI effect rope_compare - Text description relation CI null interval p_result - Text representation p value obtained conclusion - Text representation conclusion draw significant - TRUE/FALSE significant alpha = 1-CI","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_rdiff.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Test a hypothesis about a difference in correlation strength — test_rdiff","text":"function can passed esci_estimate object generated estimate_rdiff_two(). can test hypotheses specific value difference (point null) range values (interval null)","code":""},{"path":"https://rcalinjageman.github.io/esci/reference/test_rdiff.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Test a hypothesis about a difference in correlation strength — test_rdiff","text":"","code":"# example code estimate <- esci::estimate_rdiff_two( comparison_r = .53, comparison_n = 45, reference_r = .41, reference_n = 59, grouping_variable_levels = c(\"Females\", \"Males\"), x_variable_name = \"Satisfaction with life\", y_variable_name = \"Body satisfaction\", grouping_variable_name = \"Gender\", conf_level = .95 ) test_rdiff(estimate) #> $properties #> $properties$effect_size_name #> [1] \"r\" #> #> $properties$alpha #> [1] 0.05 #> #> $properties$interval_null #> [1] FALSE #> #> $properties$rope #> [1] 0 0 #> #> $properties$rope_units #> [1] \"raw\" #> #> #> $point_null #> test_type outcome_variable_name #> 1 Nil Hypothesis Test Satisfaction with life and Body satisfaction #> effect null_words confidence LL UL #> 1 Males - Females 0.00 95 -0.1987466 0.4209803 #> CI CI_compare t df p #> 1 95% CI [-0.1987466, 0.4209803] The 95% CI contains H_0 0.7570586 NA 0.4490148 #> p_result null_decision #> 1 p ≥ 0.05 Fail to reject H_0 #> conclusion significant #> 1 At α = 0.05, 0.00 remains a plausible value of Ͱ_diff FALSE #>"},{"path":"https://rcalinjageman.github.io/esci/news/index.html","id":"esci-version-102-release-data-march-2024","dir":"Changelog","previous_headings":"","what":"esci version 1.0.2 (Release data: March 2024)","title":"esci version 1.0.2 (Release data: March 2024)","text":"Changes: First release CRAN. Module now complete, documented, relatively complete test coverage. still pretty rough attempt, though. Expect breaking changes still come, especially graphing functions.","code":""}] diff --git a/jamovi/00refs.yaml b/jamovi/00refs.yaml new file mode 100644 index 0000000..a603ab9 --- /dev/null +++ b/jamovi/00refs.yaml @@ -0,0 +1,82 @@ +--- +refs: + + lc_median_bs: + type: 'article' + author: Bonett, D.G. & Price, R. M. + year: 2002 + title: 'Statistical inference for a linear function of medians: Confidence intervals, hypothesis testing, and sample size requirements' + publisher: Psychological Methods + volume: 7 + pages: 370-383 + url: 'https://psycnet.apa.org/doiLanding?doi=10.1037%2F1082-989X.7.3.370' + + lc_stdmean_bs: + type: 'article' + author: Bonett, D.G. + year: 2008 + title: 'Confidence intervals for standardized linear contrasts of means' + publisher: Psychological Methods + volume: 13 + pages: 99-109 + url: 'https://psycnet.apa.org/doiLanding?doi=10.1037%2F1082-989X.13.2.99' + + median_ps: + type: 'article' + author: Bonett, D.G. & Price, R. M. + year: 2020 + title: 'Interval estimation for linear functions of medians in within-subjects and mixed designs' + publisher: British Journal of Mathematical and Statistical Psychology + volume: 73 + pages: 333-346 + url: 'https://bpspsychub.onlinelibrary.wiley.com/doi/10.1111/bmsp.12171' + + ratio_ps: + type: 'article' + author: Bonett, D.G. & Price, R. M. + year: 2020 + title: 'Confidence intervals for ratios of means and medians' + publisher: Journal of Educational and Behavioral Statistics + volume: 45 + pages: 750-770 + url: 'https://journals.sagepub.com/doi/10.3102/1076998620934125' + + metafor: + type: 'article' + author: Viechtbauer, W. + year: 2010 + title: 'Conducting Meta-Analyses in R with the metafor Package' + publisher: Journal of Statistical Software + volume: 36 + pages: 1-48 + url: 'https://doi.org/10.18637/jss.v036.i03' + + ggdist: + type: 'article' + author: Kay, M. + year: 2002 + title: 'ggdist: Visualizations of Distributions and Uncertainty in the Grammar of Graphics' + publisher: IEEE Transactions on Visualization and Computer Graphics + volume: 30 + pages: 414-424 + url: 'https://zenodo.org/records/7933524' + + prop1: + type: 'article' + author: Agresti, A. & Coull, B. A. + year: 1998 + title: 'Approximate is better than exact for interval estimation of binomial proportions' + publisher: The American Statistician + volume: 52 + pages: 119-126 + url: 'https://www.tandfonline.com/doi/abs/10.1080/00031305.1998.10480550' + + prop_paired: + type: 'article' + author: Bonett, D.G. & Price, R. M. + year: 2012 + title: 'Adjusted wald confidence interval for a difference of binomial proportions based on paired data' + publisher: Journal of Educational and Behavioral Statistics + volume: 37 + pages: 479-488 + url: 'https://journals.sagepub.com/doi/10.3102/1076998611411915' diff --git a/jamovi/jamovimagnitude.r.yaml b/jamovi/jamovimagnitude.r.yaml index 1c74279..cb5c672 100644 --- a/jamovi/jamovimagnitude.r.yaml +++ b/jamovi/jamovimagnitude.r.yaml @@ -278,4 +278,5 @@ items: width: 400 height: 300 renderFun: .magnitude_plot + refs: ggdist ... diff --git a/jamovi/jamovimdiff2x2.r.yaml b/jamovi/jamovimdiff2x2.r.yaml index 5009fb6..1671be4 100644 --- a/jamovi/jamovimdiff2x2.r.yaml +++ b/jamovi/jamovimdiff2x2.r.yaml @@ -159,6 +159,7 @@ items: title: Median Difference type: Table visible: (effect_size == 'median_difference' & design == 'fully_between') + refs: lc_median_bs rows: 15 - switch - outcome_variable @@ -302,6 +303,7 @@ items: title: Standardized Mean Difference type: Table rows: 5 + refs: lc_stdmean_bs visible: (effect_size == 'mean_difference' & design == 'fully_between') clearWith: - switch @@ -535,6 +537,7 @@ items: requiresData: true width: 700 height: 400 + refs: ggdist renderFun: .estimation_plot - name: main_effect_B diff --git a/jamovi/jamovimdiffindcontrast.r.yaml b/jamovi/jamovimdiffindcontrast.r.yaml index 9d1a742..287b9d9 100644 --- a/jamovi/jamovimdiffindcontrast.r.yaml +++ b/jamovi/jamovimdiffindcontrast.r.yaml @@ -134,6 +134,7 @@ items: - effect_size - assume_equal_variance - show_details + refs: lc_median_bs columns: - name: outcome_variable_name title: 'Outcome variable' @@ -226,6 +227,7 @@ items: type: Table rows: 1 visible: (effect_size == 'mean_difference') + refs: lc_stdmean_bs clearWith: - switch - outcome_variable @@ -416,6 +418,7 @@ items: - name: estimation_plots title: Estimation Figure type: Array + refs: ggdist template: title: $key type: Image diff --git a/jamovi/jamovimdiffpaired.r.yaml b/jamovi/jamovimdiffpaired.r.yaml index 32d7f57..f8d9b6c 100644 --- a/jamovi/jamovimdiffpaired.r.yaml +++ b/jamovi/jamovimdiffpaired.r.yaml @@ -243,6 +243,7 @@ items: title: Standardized Mean Difference type: Table rows: 1 + refs: lc_stdmean_bs visible: (effect_size == 'mean_difference') clearWith: - switch @@ -310,6 +311,7 @@ items: type: Table visible: (show_ratio & effect_size == 'mean_difference') rows: 1 + refs: ratio_ps clearWith: - switch - reference_measure @@ -362,6 +364,7 @@ items: title: Median Difference type: Table visible: (effect_size == 'median_difference') + refs: median_ps rows: 3 clearWith: - switch @@ -415,6 +418,7 @@ items: type: Table visible: (show_ratio & effect_size == 'median_difference') rows: 1 + refs: ratio_ps clearWith: - switch - reference_measure @@ -595,6 +599,7 @@ items: width: 300 height: 450 requiresData: true + refs: ggdist renderFun: .estimation_plots ... diff --git a/jamovi/jamovimdifftwo.r.yaml b/jamovi/jamovimdifftwo.r.yaml index b393dc1..f63e303 100644 --- a/jamovi/jamovimdifftwo.r.yaml +++ b/jamovi/jamovimdifftwo.r.yaml @@ -204,6 +204,7 @@ items: title: Standardized Mean Difference type: Table rows: 1 + refs: lc_stdmean_bs visible: (effect_size == 'mean_difference') clearWith: - switch @@ -276,6 +277,7 @@ items: type: Table visible: (show_ratio & effect_size == 'mean_difference') rows: (outcome_variable) + refs: ratio_ps clearWith: - switch - outcome_variable @@ -330,6 +332,7 @@ items: type: Table visible: (effect_size == 'median_difference') rows: 3 + refs: lc_median_bs clearWith: - switch - outcome_variable @@ -383,6 +386,7 @@ items: type: Table visible: (show_ratio & effect_size == 'median_difference') rows: (outcome_variable) + refs: ratio_ps clearWith: - switch - outcome_variable @@ -569,6 +573,7 @@ items: - name: estimation_plots title: Estimation Figure type: Array + refs: ggdist template: title: $key type: Image diff --git a/jamovi/jamovimetamdiff.r.yaml b/jamovi/jamovimetamdiff.r.yaml index a213ef1..7a5cd5f 100644 --- a/jamovi/jamovimetamdiff.r.yaml +++ b/jamovi/jamovimetamdiff.r.yaml @@ -118,6 +118,7 @@ items: title: Meta-Analytic Effect Sizes type: Table rows: 1 + refs: metafor clearWith: - switch - comparison_means diff --git a/jamovi/jamovimetamean.r.yaml b/jamovi/jamovimetamean.r.yaml index b1fa45f..5662f75 100644 --- a/jamovi/jamovimetamean.r.yaml +++ b/jamovi/jamovimetamean.r.yaml @@ -87,6 +87,7 @@ items: title: Meta-Analytic Effect Sizes type: Table rows: 1 + refs: metafor clearWith: - means - sds diff --git a/jamovi/jamovimetapdiff.r.yaml b/jamovi/jamovimetapdiff.r.yaml index 059d456..c231c62 100644 --- a/jamovi/jamovimetapdiff.r.yaml +++ b/jamovi/jamovimetapdiff.r.yaml @@ -82,6 +82,7 @@ items: - name: es_meta title: Meta-Analytic Effect Sizes type: Table + refs: metafor rows: 1 clearWith: - reference_cases diff --git a/jamovi/jamovimetaproportion.r.yaml b/jamovi/jamovimetaproportion.r.yaml index 41a2f03..399b084 100644 --- a/jamovi/jamovimetaproportion.r.yaml +++ b/jamovi/jamovimetaproportion.r.yaml @@ -70,6 +70,7 @@ items: title: Meta-Analytic Effect Sizes type: Table rows: 1 + refs: metafor clearWith: - cases - ns diff --git a/jamovi/jamovimetar.r.yaml b/jamovi/jamovimetar.r.yaml index 6e06957..6cafe03 100644 --- a/jamovi/jamovimetar.r.yaml +++ b/jamovi/jamovimetar.r.yaml @@ -84,6 +84,7 @@ items: title: Meta-Analytic Effect Sizes type: Table rows: 1 + refs: metafor clearWith: - rs - ns diff --git a/jamovi/jamovipdiffpaired.r.yaml b/jamovi/jamovipdiffpaired.r.yaml index 94bd06c..60f5bdb 100644 --- a/jamovi/jamovipdiffpaired.r.yaml +++ b/jamovi/jamovipdiffpaired.r.yaml @@ -66,6 +66,7 @@ items: title: Proportion Difference type: Table rows: 3 + refs: prop_paired clearWith: - reference_measure - comparison_measure @@ -200,5 +201,6 @@ items: width: 300 height: 450 requiresData: true + refs: ggdist renderFun: .estimation_plots ... diff --git a/jamovi/jamovipdifftwo.r.yaml b/jamovi/jamovipdifftwo.r.yaml index bd2967d..5238c53 100644 --- a/jamovi/jamovipdifftwo.r.yaml +++ b/jamovi/jamovipdifftwo.r.yaml @@ -77,6 +77,7 @@ items: title: Proportion Difference type: Table rows: 3 + refs: prop1 clearWith: - outcome_variable - grouping_variable @@ -349,6 +350,7 @@ items: - name: estimation_plots title: Estimation Figure type: Array + refs: ggdist template: title: $key type: Image diff --git a/jamovi/jamoviproportion.r.yaml b/jamovi/jamoviproportion.r.yaml index 6a70c12..cf80bcb 100644 --- a/jamovi/jamoviproportion.r.yaml +++ b/jamovi/jamoviproportion.r.yaml @@ -16,6 +16,7 @@ items: title: Overview type: Table rows: 1 + refs: prop1 clearWith: - outcome_variable - cases @@ -147,6 +148,7 @@ items: - name: estimation_plots title: Estimation Figure type: Array + refs: ggdist template: title: $key type: Image diff --git a/man/estimate_mdiff_two.Rd b/man/estimate_mdiff_two.Rd index 7bb7b62..168b0c0 100644 --- a/man/estimate_mdiff_two.Rd +++ b/man/estimate_mdiff_two.Rd @@ -198,8 +198,8 @@ estimate <- estimate_mdiff_two( ) \dontrun{ -# To visualize the estimated median difference (raw data only) -plot_mdiff(estimate, effect_size = "median") +# To visualize the estimated mean difference +plot_mdiff(estimate, effect_size = "mean") } # From raw data @@ -214,8 +214,8 @@ plot_mdiff(estimate, effect_size = "median") ) \dontrun{ -# To visualize the estimated mean difference -plot_mdiff(estimate, effect_size = "mean") +# To visualize the estimated median difference (raw data only) +plot_mdiff(estimate, effect_size = "median") } diff --git a/man/plot_mdiff.Rd b/man/plot_mdiff.Rd index a8bb58e..486f9bf 100644 --- a/man/plot_mdiff.Rd +++ b/man/plot_mdiff.Rd @@ -133,8 +133,8 @@ estimate <- estimate_mdiff_two( ) \dontrun{ -# To visualize the estimated median difference (raw data only) -plot_mdiff(estimate, effect_size = "median") +# To visualize the estimated mean difference +plot_mdiff(estimate, effect_size = "mean") } # From raw data @@ -149,8 +149,8 @@ plot_mdiff(estimate, effect_size = "median") ) \dontrun{ -# To visualize the estimated mean difference -plot_mdiff(estimate, effect_size = "mean") +# To visualize the estimated median difference (raw data only) +plot_mdiff(estimate, effect_size = "median") }