Comparison of Weights for Meta-Analysis of r and d Under Realistic Conditions

Abstract

We compared unit, sample size, and inverse variance weighting procedures for estimating the overall mean and random-effects variance component (REVC) in random-effects meta-analysis under realistic conditions for both r and d. Root mean square error and average absolute error of estimation were used to compare weighting schemes via Monte-Carlo simulation. For r, unit weights worked surprisingly well, and sample size weights worked best overall. For d, unit weights worked poorly, and inverse variance weights worked best overall. Discussion focuses on the meta-analyst’s choice of weights, possible explanations for the differences across types of effect size, and implications for meta-analytic inferences in organizational research.