**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 |
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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**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 |
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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**Posted:** February 11, 2012 | **Author:** A. R. Hafdahl | **Filed under:** Uncategorized | **Tags:** Bayesian analysis, Campbell Collaboration, Cochrane Collaboration, mixed model, research synthesis, statistics, training |
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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