Mean and Variance Corrected Test Statistics for Structural Equation Modeling with Many Variables. Issue 6 (2nd November 2019)

• Record Type: Journal Article
• Authors: Tian, Yubin; Yuan, Ke-Hai
• Is Part Of: Structural equation modeling. Volume 26:Issue 6(2019)
• Page Start: 827
• Page End: 846
• Publication Date: 2019-11-02
• Subjects: Test statistic -- large number of variables -- nonnormality -- empirical modeling; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2019.1598865
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : Data in social and behavioral sciences are routinely collected using questionnaires, and each domain of interest is tapped by multiple indicators. Structural equation modeling (SEM) is one of the most widely used methods to analyze such data. However, conventional methods for SEM face difficulty when the number of variables ( p ) is large even when the sample size ( N ) is also rather large. This article addresses the issue of model inference with the likelihood ratio statistic T m l . Using the method of empirical modeling, mean-and-variance corrected statistics for SEM with many variables are developed. Results show that the new statistics not only perform much better than T m l but also are substantial improvements over other corrections to T m l . When combined with a robust transformation, the new statistics also perform well with non-normally distributed data.