Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

Click here to see the video

SAGETRACK

Sign In to gain access to subscriptions and/or personal tools.
Organizational Research Methods
This Article
Right arrow Full Text (PDF)
Right arrow All Versions of this Article:
1094428107300339v1
11/1/54    most recent
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Steel, P. D. G.
Right arrow Articles by Kammeyer-Mueller, J.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati  
What's this?

Bayesian Variance Estimation for Meta-Analysis

Quantifying Our Uncertainty

Piers D. G. Steel

University of Calgary, Piers.Steel{at}Haskayne.UCalgary.ca

John Kammeyer-Mueller

University of Florida

A primary goal in meta-analysis is determining the variance across a set of correlations after taking into account statistical and psychometric artifacts. If the residual variance is large, substantive moderators of the relationship likely exist; if there is little residual variance, the meta-analytic estimate of the effect size is expected to generalize across multiple settings. Surprisingly little attention has been directed toward some critical shortcomings of traditional methods for estimating residual variance. In this article, the authors argue that residual variance estimates are often based on an unrealistic model of the sampling distribution of residual variance. The authors review alternative Bayesian techniques for estimation that avoid these problems and provide simulation results demonstrating the superiority of the Bayesian approach.

Key Words: generalizability theory • computer simulation techniques • Monte Carlo • bootstrapping • meta-analysis • Bayesian analysis

This version was published on January 1, 2008

Organizational Research Methods, Vol. 11, No. 1, 54-78 (2008)
DOI: 10.1177/1094428107300339
Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati    What's this?