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+516308ad

mod_stats.html

@@ -370,11 +370,7 @@ <h2 id="toc-title">On this page</h2>
   <ul class="collapse">
   <li><a href="#aic-case-study" id="toc-aic-case-study" class="nav-link" data-scroll-target="#aic-case-study">AIC Case Study</a></li>
   </ul></li>
-  <li><a href="#meta-analysis" id="toc-meta-analysis" class="nav-link" data-scroll-target="#meta-analysis">Meta-Analysis</a>
-  <ul class="collapse">
-  <li><a href="#calculating-effect-sizes" id="toc-calculating-effect-sizes" class="nav-link" data-scroll-target="#calculating-effect-sizes">Calculating Effect Sizes</a></li>
-  <li><a href="#performing-meta-analysis" id="toc-performing-meta-analysis" class="nav-link" data-scroll-target="#performing-meta-analysis">Performing Meta-Analysis</a></li>
-  </ul></li>
+  <li><a href="#meta-analysis" id="toc-meta-analysis" class="nav-link" data-scroll-target="#meta-analysis">Meta-Analysis</a></li>
   <li><a href="#additional-resources" id="toc-additional-resources" class="nav-link" data-scroll-target="#additional-resources">Additional Resources</a>
   <ul class="collapse">
   <li><a href="#papers-documents" id="toc-papers-documents" class="nav-link" data-scroll-target="#papers-documents">Papers &amp; Documents</a></li>
@@ -435,7 +431,8 @@ <h2 class="anchored" data-anchor-id="needed-packages">Needed Packages</h2>
 <span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="do">## So you can skip this step if you've installed these before</span></span>
 <span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">"tidyverse"</span>)</span>
 <span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">"lmerTest"</span>)</span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">"palmerpenguins"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">"palmerpenguins"</span>)</span>
+<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">"esc"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>We’ll go ahead and load some of these libraries as well to be able to better demonstrate these concepts.</p>
 <div class="cell">
@@ -865,12 +862,33 @@ <h2 class="anchored" data-anchor-id="meta-analysis">Meta-Analysis</h2>
 <li>The sample size of the response</li>
 </ol>
 <p>Once you have that information, you can calculate effect sizes for the various groups in each study. Note that the importance of this information to meta-analyses should also highlight <u>how vital it is that you report this information in your own research</u>! Doing so will enable future meta-analyses to include your study and increase the scientific impact of your work as well as its professional benefits to you.</p>
-<section id="calculating-effect-sizes" class="level3">
-<h3 class="anchored" data-anchor-id="calculating-effect-sizes">Calculating Effect Sizes</h3>
-</section>
-<section id="performing-meta-analysis" class="level3">
-<h3 class="anchored" data-anchor-id="performing-meta-analysis">Performing Meta-Analysis</h3>
-</section>
+<p>One such effect size is Cohen’s <em>d</em> and is a reasonable effect size for quantifying the difference in means between two groups. In order to perform this calculation you simply need the mean, standard deviation, and sample size for two groups. Let’s check out an example to demonstrate.</p>
+<div class="cell">
+<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Load needed library</span></span>
+<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(esc)</span>
+<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Calculate Cohen's d</span></span>
+<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a>calc_effect <span class="ot">&lt;-</span> esc<span class="sc">::</span><span class="fu">esc_mean_sd</span>(<span class="at">grp1m =</span> <span class="dv">50</span>, <span class="at">grp2m =</span> <span class="dv">60</span>, </span>
+<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a>                                <span class="at">grp1sd =</span> <span class="dv">10</span>, <span class="at">grp2sd =</span> <span class="dv">10</span>, </span>
+<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a>                                <span class="at">grp1n =</span> <span class="dv">50</span>, <span class="at">grp2n =</span> <span class="dv">50</span>)</span>
+<span id="cb11-8"><a href="#cb11-8" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb11-9"><a href="#cb11-9" aria-hidden="true" tabindex="-1"></a><span class="co"># Check out output</span></span>
+<span id="cb11-10"><a href="#cb11-10" aria-hidden="true" tabindex="-1"></a>calc_effect</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stdout">
+<pre><code>
+Effect Size Calculation for Meta Analysis
+
+     Conversion: mean and sd to effect size d
+    Effect Size:  -1.0000
+ Standard Error:   0.2121
+       Variance:   0.0450
+       Lower CI:  -1.4158
+       Upper CI:  -0.5842
+         Weight:  22.2222</code></pre>
+</div>
+</div>
+<p>Note that Cohen’s <em>d</em> is just one effect size available to you and others may be more appropriate in certain contexts. Just like any other metric, which effect size you choose is a mix of your scientific intution and appropriateness for the content of your data. For a deeper dive into the breadth of effect size considerations available to you, see <a href="https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/effects.html#effects">the relevant chapter</a> of the <em>Doing Meta-Analysis in R</em> online book.</p>
+<p>After you’ve calculated all relevant effect sizes–using your chosen flavor of effect size–the “actual” meta-analysis is nearly finished. Simply create a graph of the effect sizes with error bars indicating confidence intervals (included by default in most effect size functions). Where the error bars overlap among studies, there is no significant difference between those effect sizes. Conversely, where the error bars <em>do not</em> overlap among studies the effect sizes do significantly differ indicating that the studies results’ differ for the data used to calculate the effect size.</p>
 </section>
 <section id="additional-resources" class="level2">
 <h2 class="anchored" data-anchor-id="additional-resources">Additional Resources</h2>

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-    "text": "Needed Packages\nIf you’d like to follow along with the code chunks included throughout this module, you’ll need to install the following packages:\n\n# Note that these lines only need to be run once per computer\n## So you can skip this step if you've installed these before\ninstall.packages(\"tidyverse\")\ninstall.packages(\"lmerTest\")\ninstall.packages(\"palmerpenguins\")\n\nWe’ll go ahead and load some of these libraries as well to be able to better demonstrate these concepts.\n\n# Load needed libraries\nlibrary(tidyverse)\nlibrary(lmerTest)\nlibrary(palmerpenguins)",
+    "text": "Needed Packages\nIf you’d like to follow along with the code chunks included throughout this module, you’ll need to install the following packages:\n\n# Note that these lines only need to be run once per computer\n## So you can skip this step if you've installed these before\ninstall.packages(\"tidyverse\")\ninstall.packages(\"lmerTest\")\ninstall.packages(\"palmerpenguins\")\ninstall.packages(\"esc\")\n\nWe’ll go ahead and load some of these libraries as well to be able to better demonstrate these concepts.\n\n# Load needed libraries\nlibrary(tidyverse)\nlibrary(lmerTest)\nlibrary(palmerpenguins)",
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@@ -804,7 +804,7 @@
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-    "text": "Meta-Analysis\nMany synthesis projects are able to find the original data of each study, harmonize that data, and then perform standard analyses on that synthesized data. However, in some cases you may find that the data used in different projects are not directly comparable. For instance, if you want to know what the effect of restoration methods are on forest recovery you might not be able to simply combine data from different studies that use widely different restoration methods, data collection methods, and have different forest community compositions. In such cases you can use meta-analysis to compare the results of different studies rather than using their data. Meta-analysis is named the way it is because it is an analysis of prior analyses.\nTo perform meta-analysis you’ll need to calculate an “effect size” for all studies you’d like to include. An effect size captures the direction and magnitude of the relationship analyzed in each original study. If you use a standard effect size calculation for each stud, you’ll make it possible to directly compare results across these studies (even if context differs among them!). Note that some people disagree with the word “effect” in “effect size” because it suggests a causal relationship; for our purposes, let’s consider ‘effect’ to be inclusive of correlative relationships and ignore the possible implication of causality.\nIn order to calculate these effect sizes you’ll need to extract the following information from each study:\n\nA measure of the ‘central tendency’ of the response\n\nOften the arithmetic mean but can also be a proportion or a correlation\nYou’ll need to do this separately for any groups within the study\n\nA measure of the variation in the response\n\nTypically standard deviation\n\nThe sample size of the response\n\nOnce you have that information, you can calculate effect sizes for the various groups in each study. Note that the importance of this information to meta-analyses should also highlight how vital it is that you report this information in your own research! Doing so will enable future meta-analyses to include your study and increase the scientific impact of your work as well as its professional benefits to you.\n\nCalculating Effect Sizes\n\n\nPerforming Meta-Analysis",
+    "text": "Meta-Analysis\nMany synthesis projects are able to find the original data of each study, harmonize that data, and then perform standard analyses on that synthesized data. However, in some cases you may find that the data used in different projects are not directly comparable. For instance, if you want to know what the effect of restoration methods are on forest recovery you might not be able to simply combine data from different studies that use widely different restoration methods, data collection methods, and have different forest community compositions. In such cases you can use meta-analysis to compare the results of different studies rather than using their data. Meta-analysis is named the way it is because it is an analysis of prior analyses.\nTo perform meta-analysis you’ll need to calculate an “effect size” for all studies you’d like to include. An effect size captures the direction and magnitude of the relationship analyzed in each original study. If you use a standard effect size calculation for each stud, you’ll make it possible to directly compare results across these studies (even if context differs among them!). Note that some people disagree with the word “effect” in “effect size” because it suggests a causal relationship; for our purposes, let’s consider ‘effect’ to be inclusive of correlative relationships and ignore the possible implication of causality.\nIn order to calculate these effect sizes you’ll need to extract the following information from each study:\n\nA measure of the ‘central tendency’ of the response\n\nOften the arithmetic mean but can also be a proportion or a correlation\nYou’ll need to do this separately for any groups within the study\n\nA measure of the variation in the response\n\nTypically standard deviation\n\nThe sample size of the response\n\nOnce you have that information, you can calculate effect sizes for the various groups in each study. Note that the importance of this information to meta-analyses should also highlight how vital it is that you report this information in your own research! Doing so will enable future meta-analyses to include your study and increase the scientific impact of your work as well as its professional benefits to you.\nOne such effect size is Cohen’s d and is a reasonable effect size for quantifying the difference in means between two groups. In order to perform this calculation you simply need the mean, standard deviation, and sample size for two groups. Let’s check out an example to demonstrate.\n\n# Load needed library\nlibrary(esc)\n\n# Calculate Cohen's d\ncalc_effect <- esc::esc_mean_sd(grp1m = 50, grp2m = 60, \n                                grp1sd = 10, grp2sd = 10, \n                                grp1n = 50, grp2n = 50)\n\n# Check out output\ncalc_effect\n\n\nEffect Size Calculation for Meta Analysis\n\n     Conversion: mean and sd to effect size d\n    Effect Size:  -1.0000\n Standard Error:   0.2121\n       Variance:   0.0450\n       Lower CI:  -1.4158\n       Upper CI:  -0.5842\n         Weight:  22.2222\n\n\nNote that Cohen’s d is just one effect size available to you and others may be more appropriate in certain contexts. Just like any other metric, which effect size you choose is a mix of your scientific intution and appropriateness for the content of your data. For a deeper dive into the breadth of effect size considerations available to you, see the relevant chapter of the Doing Meta-Analysis in R online book.\nAfter you’ve calculated all relevant effect sizes–using your chosen flavor of effect size–the “actual” meta-analysis is nearly finished. Simply create a graph of the effect sizes with error bars indicating confidence intervals (included by default in most effect size functions). Where the error bars overlap among studies, there is no significant difference between those effect sizes. Conversely, where the error bars do not overlap among studies the effect sizes do significantly differ indicating that the studies results’ differ for the data used to calculate the effect size.",
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