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mod_stats.html

@@ -372,7 +372,6 @@ <h2 id="toc-title">On this page</h2>
   </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="#extracting-study-results" id="toc-extracting-study-results" class="nav-link" data-scroll-target="#extracting-study-results">Extracting Study Results</a></li>
   <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>
@@ -865,9 +864,7 @@ <h2 class="anchored" data-anchor-id="meta-analysis">Meta-Analysis</h2>
 </ul></li>
 <li>The sample size of the response</li>
 </ol>
-<section id="extracting-study-results" class="level3">
-<h3 class="anchored" data-anchor-id="extracting-study-results">Extracting Study Results</h3>
-</section>
+<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>

search.json

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     "href": "mod_stats.html#meta-analysis",
     "title": "Analysis & Modeling",
     "section": "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\n\nExtracting Study Results\n\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.\n\nCalculating Effect Sizes\n\n\nPerforming Meta-Analysis",
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