Comparison of three meta-analytic procedures for estimating moderating effects of categorical variables
Abstract
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).
Publication Title
Organizational Research Methods
Recommended Citation
Aguinis, H., Sturman, M., & Pierce, C. (2008). Comparison of three meta-analytic procedures for estimating moderating effects of categorical variables. Organizational Research Methods, 11 (1), 9-34. https://doi.org/10.1177/1094428106292896
