元分析结构方程建模：一种两阶段方法。

Meta-analytic structural equation modeling: a two-stage approach.

作者信息

Cheung Mike W-L, Chan Wai

机构信息

Department of Psychology, University of Hong Kong.

Department of Psychology, Chinese University of Hong Kong.

出版信息

Psychol Methods. 2005 Mar;10(1):40-64. doi: 10.1037/1082-989X.10.1.40.

DOI:10.1037/1082-989X.10.1.40

PMID:15810868

Abstract

To synthesize studies that use structural equation modeling (SEM), researchers usually use Pearson correlations (univariate r), Fisher z scores (univariate z), or generalized least squares (GLS) to combine the correlation matrices. The pooled correlation matrix is then analyzed by the use of SEM. Questionable inferences may occur for these ad hoc procedures. A 2-stage structural equation modeling (TSSEM) method is proposed to incorporate meta-analytic techniques and SEM into a unified framework. Simulation results reveal that the univariate-r, univariate-z, and TSSEM methods perform well in testing the homogeneity of correlation matrices and estimating the pooled correlation matrix. When fitting SEM, only TSSEM works well. The GLS method performed poorly in small to medium samples.

摘要

为了综合运用结构方程模型（SEM）的研究，研究人员通常使用皮尔逊相关系数（单变量r）、费舍尔z分数（单变量z）或广义最小二乘法（GLS）来合并相关矩阵。然后使用SEM对合并后的相关矩阵进行分析。这些临时程序可能会产生有问题的推断。本文提出了一种两阶段结构方程建模（TSSEM）方法，将元分析技术和SEM纳入一个统一的框架。模拟结果表明，单变量r、单变量z和TSSEM方法在检验相关矩阵的同质性和估计合并相关矩阵方面表现良好。在拟合SEM时，只有TSSEM效果良好。GLS方法在中小样本中表现较差。