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 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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