Overview of Meta-Analysis, Part 5c (of 7): Primary Meta-Analyses (cont.)

This is the last of three posts in Part 5 of my overview of meta-analysis.  In Part 5a I described six conventional meta-analytic models for effect-size (ES) estimates, and in Part 5b I described estimation and inference for two of those models without covariates.  In this post I’ll extend the methods of Part 5b to two models with covariates and comment on extensions and other variants of these models and procedures, to hint at the wide variety of situations that arise in meta-analysis.  In Parts 6 and 7 of the overview, I’ll address follow-up procedures and ways to report results, respectively.
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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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Resources for Learning and Doing Research Synthesis, Part 1

In this blog I’ll cover diverse topics in meta-analytic methodology.  It isn’t, however, intended as a one-stop resource.  Whether you (plan to) produce or consume research syntheses, teach would-be meta-analysts, or follow this topic for other reasons, you’ll likely benefit from other sources of information and support.  This is especially true if you’re interested in areas of the research synthesis landscape beyond the realm of meta-analytic techniques.

In this two-part post I’ll try to drive visitors away from this blog. 😮  Specifically, I’ll describe several (related) types of resources to consider: organizations and training here in Part 1, and methodological publications, software, and collaborators later in Part 2.
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