Oort Frans J, Jak Suzanne
Research Institute of Child Development and Education, Faculty of Social and Behavioural Sciences, University of Amsterdam, Amsterdam, The Netherlands.
Utrecht University, Utrecht, The Netherlands.
Res Synth Methods. 2016 Jun;7(2):156-67. doi: 10.1002/jrsm.1203.
Meta-analytic structural equation modeling (MASEM) involves fitting models to a common population correlation matrix that is estimated on the basis of correlation coefficients that are reported by a number of independent studies. MASEM typically consist of two stages. The method that has been found to perform best in terms of statistical properties is the two-stage structural equation modeling, in which maximum likelihood analysis is used to estimate the common correlation matrix in the first stage, and weighted least squares analysis is used to fit structural equation models to the common correlation matrix in the second stage. In the present paper, we propose an alternative method, ML MASEM, that uses ML estimation throughout. In a simulation study, we use both methods and compare chi-square distributions, bias in parameter estimates, false positive rates, and true positive rates. Both methods appear to yield unbiased parameter estimates and false and true positive rates that are close to the expected values. ML MASEM parameter estimates are found to be significantly less bias than two-stage structural equation modeling estimates, but the differences are very small. The choice between the two methods may therefore be based on other fundamental or practical arguments. Copyright © 2016 John Wiley & Sons, Ltd.
元分析结构方程模型（MASEM）涉及将模型拟合到一个共同总体相关矩阵，该矩阵是根据多项独立研究报告的相关系数估计得出的。MASEM通常包括两个阶段。就统计特性而言，已发现表现最佳的方法是两阶段结构方程模型，其中在第一阶段使用最大似然分析来估计共同相关矩阵，在第二阶段使用加权最小二乘分析将结构方程模型拟合到共同相关矩阵。在本文中，我们提出了一种替代方法，即ML MASEM，它全程使用最大似然估计。在一项模拟研究中，我们使用了这两种方法，并比较了卡方分布、参数估计偏差、假阳性率和真阳性率。两种方法似乎都能产生无偏参数估计以及接近预期值的假阳性率和真阳性率。结果发现，ML MASEM参数估计的偏差明显小于两阶段结构方程模型估计，但差异非常小。因此，这两种方法之间的选择可能基于其他基本或实际的考量。版权所有© 2016约翰威立父子有限公司。
