Sneak Preview 2: Outliers, Metric Transformation, and ES Distribution

My previous three posts on fitting models to effect sizes (ESs)—Parts 5a, 5b, and 5c—were the core of my seven-part overview of meta-analysis.  With only two posts remaining in the overview, I’ll pause again to describe three more methodological issues I plan to discuss: potential outliers, transforming ES metrics, and the distribution of ES parameters.  As in my first sneak preview—about degraded ESs and tricky conditional variances (CVs)—I’ll keep these “teaser” descriptions fairly short, mainly to pique your interest; each issue deserves at least one dedicated post with more detail.
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Overview of Meta-Analysis, Part 5a (of 7): Primary Meta-Analyses

The previous four parts of this seven-part overview of meta-analysis focused on obtaining data and preparing them for the central task addressed in this fifth part: meta-analyzing effect-size (ES) estimates, which I’ll cover in three subparts focused on meta-analytic models (Part 5a) and procedures for fitting them to ESs (Parts 5b and 5c).  In the last two parts (6 and 7) I’ll address follow-up techniques to assess potential problems with these primary analyses, as well as useful ways to report these analyses’ results.  (Topics for all seven parts of this overview are listed in Part 1.)
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Sneak Preview: Degraded Effect Sizes and Tricky Conditional Variances

My post on data exploration more than half completed my seven-part overview of meta-analysis.  As a diversion while I write Part 5, let’s consider two of several methodological issues I plan to discuss in this blog: degraded effect sizes (ESs) and tricky conditional variances (CVs).  My main aim here is to pique your interest in future posts by offering a glimpse at ways to manage selected challenges that routine meta-analytic techniques don’t address.  These “teaser” descriptions will be quite superficial.  I plan to elaborate on each of these challenges—as well as many others—after laying a foundation in my seven-part overview.
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Overview of Meta-Analysis, Part 4 (of 7): Data Exploration

This seven-part overview’s first three parts focused on collecting data used in meta-analyses: estimates of effect size (ES), sample sizes or conditional variances (CVs) to quantify ES sampling error or (im)precision, and ES features.  The overview’s subsequent four parts address analyzing these data and presenting results.  In this fourth part I begin by describing preliminary analyses that can help identify errors and issues to attend to in primary analyses. (Part 1 of this overview lists the topics for all seven parts.)
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Overview of Meta-Analysis, Part 3 (of 7): Effect-Size Features

In Part 2 of this seven-part overview, I described obtaining the sample size(s) or conditional variance (CV) associated with an effect-size (ES) estimate to quantify this estimate’s sampling error or (im)precision.  Here in Part 3 I’ll address coding features linked to ESs.  Whereas this overview’s first three parts focus on collecting data used in a research synthesis, its subsequent four parts will address meta-analyzing these data and presenting results.  (Part 1 lists the topics for all seven parts.)
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Overview of Meta-Analysis, Part 2 (of 7): Sampling Error

In Part 1 of this seven-part overview of meta-analysis, I introduced Conn, Hafdahl, Cooper, Brown, and Lusk’s (2009) quantitative review of workplace exercise interventions and discussed extracting effect-size (ES) estimates.  Building on that material, in this second part I’ll address obtaining info about an ES’s sampling error, which plays a critical role in most modern meta-analytic methods.  (Part 1 of this overview lists topics in the subsequent five posts.)
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Overview of Meta-Analysis, Part 1 (of 7): Effect Sizes

This post is the first in a seven-part overview of common meta-analytic tasks.  In this first part I’ll introduce a real-world substantive application of meta-analysis and address estimating effect sizes (ESs).  Subsequent parts will focus on the following topics:

  • Part 2: obtaining information about ES sampling error
  • Part 3: collecting features of ESs
  • Part 4: exploring data
  • Part 5: fitting meta-analytic models to ESs (subparts 5a, 5b, and 5c)
  • Part 6: checking for potential problems
  • Part 7: expressing results informatively

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