Correlation-Based Meta-Analytic Structural Equation Modeling: Effects of Parameter Covariance on Point and Interval Estimates
研究了基于相关性的元分析结构方程模型中，随机效应参数协方差对路径系数估计的偏差和置信区间准确性的影响，通过模拟和真实数据验证，发现偏差通常可忽略，但不同方法在区间估计和统计效力上存在差异。
More and more researchers use meta-analysis to conduct multivariate analysis to summarize previous findings. In the correlation-based meta-analytic structural equation modeling (cMASEM), the average sample correlation matrix is used to estimate the average population model. Using a simple mediation model, we illustrated that random effects covariation in population parameters can theoretically bias the path coefficient estimates and lead to nonnormal random effects distribution of the correlations. We developed an R function for researchers to examine by simulation the impact of random effects in other models. We then reanalyzed two real data sets and conducted a simulation study to examine the magnitude of the impact on realistic situations. Simulation results suggest parameter bias is typically negligible (less than .02), parameter bias and root mean square error do not differ across methods, 95% confident intervals are sometimes more accurate for the two-stage structural equation modeling approach with a diagonal random effects model, and power is sometimes higher for the traditional Viswesvaran-Ones approach. Given the increasing popularity of cMASEM in organizational research, these simulation results form the basis for us to make several recommendations on its application.