Assessing Cutoff Values of SEM Fit Indices: Advantages of the Unbiased SRMR Index and Its Cutoff Criterion Based on Communality. Issue 3 (4th May 2022)

• Record Type: Journal Article
• Authors: Ximénez, Carmen; Maydeu-Olivares, Alberto; Shi, Dexin; Revuelta, Javier
• Is Part Of: Structural equation modeling. Volume 29:Issue 3(2022)
• Page Start: 368
• Page End: 380
• Publication Date: 2022-05-04
• Subjects: Structural equation modeling (SEM) -- goodness-of-fit indices -- magnitude of factor loadings -- reliability paradox; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2021.1992596
• Languages: English
• ISSNs: 1070-5511

Abstract

ABSTRACT: Holding model misspecification constant, the behavior of fit indices depends on factors such as the number of variables being modeled (model size), and the average observed correlation (magnitude of factor loadings or measurement quality). We examine by simulation the interplay of these factors with sample size in CFA models. When a biased estimator of the fit index is used (CFI, TLI, or GFI), the behavior of the sample indices depends on sample size, rendering establishing cutoff values impossible. When an unbiased estimator is used (SRMR, or RMSEA) the behavior of the indices matches that of the population parameter and depends on the average R 2 of the observed variables (communality); and for the RMSEA, also on model size. The use of the unbiased SRMR with a cutoff value adjusted by R 2 is recommended as it enables assessing the degree of a model misspecification across model size, sample size, and measurement quality.