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Comparison of Three Meta-Analytic Procedures for Estimating Moderating Effects of Categorical Variables

University of Colorado at Denver and Health Sciences Center, Herman.Aguinis{at}cudenver.edu

Michael C. Sturman

Cornell University

Charles A. Pierce

University of Memphis

The authors conducted Monte Carlo simulations to compare the Hedges and Olkin, the Hunter and Schmidt, and a refinement of the Aguinis and Pierce meta-analytic approaches for estimating moderating effects of categorical variables. The simulation examined binary moderator variables (e.g., gender—male, female; ethnicity—majority, minority). The authors compared the three meta-analytic methods in terms of their point estimation accuracy and Type I and Type II error rates. Results provide guidelines to help researchers choose among the three meta-analytic techniques based on theory (i.e., exploratory vs. confirmatory research) and research design considerations (i.e., degree of range restriction and measurement error).

Key Words: meta-analysis • moderator variable • moderating effect • categorical variable

This version was published on January 1, 2008

Organizational Research Methods, Vol. 11, No. 1, 9-34 (2008)
DOI: 10.1177/1094428106292896


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