ESTIMATION OF SAMPLING VARIANCE OF CORRELATIONS IN META‐ANALYSIS
通过蒙特卡洛模拟比较了传统和均值两种相关系数抽样方差估计量在元分析中的表现，发现均值估计量偏差更小，推荐使用除非有强理论支持的调节效应假设。
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.