Multiple-Group Analysis for Structural Equation Modeling With Dependent Samples

Abstract

Multigroup structural equation modeling (SEM) plays a key role in studying measurement invariance and in group comparison. However, existing methods for multigroup SEM assume that different samples are independent. This article develops a method for multigroup SEM with correlated samples. Parallel to that for independent samples, the focus here is on the cross-group stability of the within-group structure and parameters. In particular, the method does not require the specification of any between-group relationship. Rescaled and adjusted statistics as well as sandwich-type covariance matrices make the developed method work for possibly nonnormal variables with finite 4th-order moments. The method is applied to a longitudinal data set on the development of entrepreneurial teams across 4 phases. Detailed analysis is provided regarding the stability of the effect of psychological compatibility on team performance, as it is mediated by fairness perception and team cohesion.

Keywords:

• adjusted statistic
• chi-square-difference test
• correlated populations
• entrepreneurial teams, longitudinal data
• rescaled statistic
• sandwich-type standard error

ACKNOWLEDGMENTS

We would like to thank Dr. Peter Bentler, Dr. Kentaro Hayashi, and two anonymous reviewers for comments on earlier versions of this article.

Additional information

Funding

The research was supported by a grant from the National Natural Science Foundation of China (71002023) and a grant from China Scholarship Council.