Meta-analytic structural equation modeling: A two-stage approach
Meta-analytic structural equation modeling: A two-stage approach
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DOI：
10.1037/1082-989x.10.1.40
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发表时间：
2005-03-01
影响因子：
7
通讯作者：
Chan, W
中科院分区：
文献类型：
--
作者：
Cheung, MWL;Chan, W
为了综合使用结构方程模型（SEM）的研究，研究人员通常使用Pearson相关系数（单变量r），Fisher z得分（单变量z）或广义最小二乘法（GLS）来联合收割机相关矩阵。然后使用SEM分析合并的相关矩阵。对于这些临时程序，可能会出现可疑的推论。提出了一种两阶段结构方程模型（TSSEM）方法，将元分析技术和结构方程模型结合到一个统一的框架中。仿真结果表明，univariate-r，univariate-z和TSSEM方法在测试相关矩阵的齐性和估计合并相关矩阵方面表现良好。当拟合SEM时，只有TSSEM工作良好。GLS方法在中小样本中表现不佳。
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.