Weighting by Inverse Variance or by Sample Size in Random-Effects Meta-Analysis

Most of the statistical procedures in meta-analysis are based on the estimation of average effect sizes from a set of primary studies. The optimal weight for averaging a set of independent effect sizes is the inverse variance of each effect size, but in practice these weights have to be estimated, being affected by sampling error. When assuming a random-effects model, there are two alternative procedures for averaging independent effect sizes: Hunter and Schmidt's estimator, which consists of weighting by sample size as an approximation to the optimal weights; and Hedges and Vevea's estimator, which consists of weighting by an estimation of the inverse variance of each effect size. In this article, the bias and mean squared error of the two estimators were assessed via Monte Carlo simulation of meta-analyses with the standardized mean difference as the effect-size index. Hedges and Vevea's estimator, although slightly biased, achieved the best performance in terms of the mean squared error. As the differences between the values of both estimators could be of practical relevance, Hedges and Vevea's estimator should be selected rather than that of Hunter and Schmidt when the effect-size index is the standardized mean difference.