Assessing cutoff values of SEM fit indices: advantages of the unbiased SRMR index and its cutoff criterion based on communality
Entity
UAM. Departamento de Psicología Social y MetodologíaPublisher
RoutledgeDate
2022-01-12Citation
10.1080/10705511.2021.1992596
Structural Equation Modeling: A Multidisciplinary Journal 29.3 (2022): 368-380
ISSN
1070-5511 (print); 1532-8007 (online)DOI
10.1080/10705511.2021.1992596Funded by
This work was supported by the National Science Foundation under Grant No. SES-1659936, by the Research Center for Child Well-Being (NIGMS P20GM130420). and by a Salvador de Madariaga Grant No. PRX18/00297, and Grant No. PGC2018-093838-B-I00 from the Spanish Ministerio de Ciencia, Innovación y UniversidadesProject
Gobierno de España. PGC2018-093838-B-I00; Gobierno de España. SES-1659936Editor's Version
https://doi.org/10.1080/10705511.2021.1992596Subjects
Structural equation modeling (SEM); goodness-of-fit indices; magnitude of factor loadings; reliability paradox; PsicologíaRights
© 2021 Taylor & Francis GroupAbstract
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 R2 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 R2 is recommended as it enables assessing the degree of a model misspecification across model size, sample size, and measurement quality
Files in this item
Google Scholar:Ximénez Gómez, María Carmen
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Maydeu-Olivares, Alberto
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Dexin, Shi
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Revuelta Menéndez, Javier
This item appears in the following Collection(s)
- Producción científica de la UAM [20729]
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