Comparison of Three Meta-Analytic Procedures for Estimating Moderating Effects of Categorical Variables
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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).
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meta-analysis; moderator variable; moderating effect; categorical variable
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Required Publisher Statement: © SAGE. Aguinis, H., Sturman, M. C., & Pierce, C. A. (2008). Final version published as: Comparison of three meta-analytic procedures for estimating moderating effects of categorical variables. Organizational Research Methods, 11(1), 9-34. doi: 10.1177/1094428106292896. Reprinted with permission. All rights reserved.