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

This is the second of three posts in Part 5 of my overview of meta-analysis.  In Part 5a I described six conventional models for meta-analysis, each of which combines within-study and between-studies models.  In this second post I first comment on nested models then describe estimation and inference for two models without covariates—procedures for fitting these models to effect-size (ES) estimates and quantifying uncertainty about their focal (hyper)parameters.  In the third post, Part 5c, I’ll do the same for two models with covariates and also comment on extensions and variants of these models and procedures.
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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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