Comparison of three meta-analytic procedures for estimating moderating effects of categorical variables

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).