A Comprehensive Comparison of Model Selection Methods for Testing Factorial Invariance. Issue 3 (3rd May 2020)

• Record Type: Journal Article
• Authors: Liang, Xinya; Luo, Yong
• Is Part Of: Structural equation modeling. Volume 27:Issue 3(2020)
• Page Start: 380
• Page End: 395
• Publication Date: 2020-05-03
• Subjects: Factorial invariance -- Bayesian model selection -- widely available information criterion -- leave-one-out cross-validation; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2019.1649983
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : This study compared the performance of (1) model selection methods based on a point estimate (MSM-P), (2) partially Bayesian methods, and (3) fully Bayesian methods, for testing factorial invariance. The MSM-P considered included the likelihood ratio test (LRT) and a number of information criteria (IC). The Bayesian methods tested included the deviance information criterion (DIC), widely available information criterion (WAIC), and leave-one-out cross-validation (LOO). We investigated true positive and false positive rates of each model selection method under conditions varying in sample size, proportion of non-invariant items, and pattern, location and magnitude of non-invariance. The impact of various prior choices on Bayesian model selection results was also examined. Results showed that DIC, WAIC, and LOO performed comparably well, had a good balance between true and false positive rates, were flexible given the prior choices, and may complement MSM-P in factorial invariance testing. Implications and recommendations based on the findings were discussed.