Comparison of Three Meta-Analytic Procedures for Estimating Moderating Effects of Categorical Variables
Herman Aguinis1*,
Michael C. Sturman2,
and
Charles A. Pierce3
1 University of Colorado at Denver and Health Sciences Center
2 Cornell University
3 University of Memphis
* To whom correspondence should be addressed. E-mail: Herman.Aguinis{at}cudenver.edu.
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Abstract |
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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).
