Building a Better Bibliography

My previous blog post was more than two months ago.  Since then I’ve nearly completed Part 6 in my 7-part overview of meta-analysis, about various types of problems that can arise.  It’s long-ish and covers several pertinent issues, such as aberrant cases (e.g., outliers), sources of bias (e.g., reporting biases, artifacts), threats to validity, and sensitivity analyses.  I’d like to post that before September.

Meanwhile, I invite you to test-drive the project that’s consumed much of my time during the past couple of months: building a CiteULike library on methodology for research synthesis (Meth4ReSyn). Read the rest of this entry »


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 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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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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Overview of Meta-Analysis, Part 4 (of 7): Data Exploration

This seven-part overview’s first three parts focused on collecting data used in meta-analyses: estimates of effect size (ES), sample sizes or conditional variances (CVs) to quantify ES sampling error or (im)precision, and ES features.  The overview’s subsequent four parts address analyzing these data and presenting results.  In this fourth part I begin by describing preliminary analyses that can help identify errors and issues to attend to in primary analyses. (Part 1 of this overview lists the topics for all seven parts.)
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