Estimation of Sampling Variance of Correlations in Meta‐analysis

Monte Carlo simulations were conducted to compare the performance of the traditional (Fisher, 1954) and mean (Hunter & Schmidt, 1990) estimators of the sampling variance of correlations in meta‐analysis. The mean estimator differs from the traditional estimator in that it uses the mean observed correlation, averaged across studies, in the sampling variance formula. The simulations investigated the homogeneous (i.e., no true correlation variance across studies) and heterogeneous case (i.e., true correlation variance across studies). Results reveal that, compared to the traditional estimator, the mean estimator provides less negatively biased estimates of sampling variance in the homogeneous and heterogeneous cases and more positively biased estimates in the heterogenous case. Thus, results support the use of the mean estimator unless strong, theory‐based hypotheses regarding moderating effects exist.