Overview of Meta-Analysis, Part 5c (of 7): Primary Meta-Analyses (cont.)
Posted: May 13, 2012 | Author: A. R. Hafdahl | Filed under: Overview of Meta-Analysis | Tags: Bayesian analysis, between-studies variance component, dependence, fixed effect, heterogeneity, interval estimation, meta-analysis, meta-regression, model comparison, moderator, multivariate effect size, random effect, significance testing, standardized mean difference | 1 CommentThis 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.)
Posted: April 30, 2012 | Author: A. R. Hafdahl | Filed under: Overview of Meta-Analysis | Tags: between-studies variance component, conditional variance, fixed effect, heterogeneity, interval estimation, math notation, meta-analysis, meta-regression, random effect, significance testing, standardized mean difference | 2 CommentsThis 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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Sneak Preview: Degraded Effect Sizes and Tricky Conditional Variances
Posted: April 2, 2012 | Author: A. R. Hafdahl | Filed under: Sneak Preview | Tags: Bayesian analysis, conditional variance, correlation, effect size, meta-analysis, missing data, resampling, significance testing, standardized mean difference, vote counting | Leave a commentMy 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 2 (of 7): Sampling Error
Posted: March 11, 2012 | Author: A. R. Hafdahl | Filed under: Overview of Meta-Analysis | Tags: binary outcome, conditional variance, correlation, dependence, effect size, heterogeneity, meta-analysis, missing data, multivariate effect size, primary-study design, sample size, standardized mean difference | Leave a commentIn 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
Posted: February 27, 2012 | Author: A. R. Hafdahl | Filed under: Overview of Meta-Analysis | Tags: binary outcome, correlation, dependence, effect size, meta-analysis, missing data, multivariate effect size, standardized mean difference, substantive application | Leave a commentThis 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
Meta-Analysis Sandwich 