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Cited 19 time in webofscience Cited 19 time in scopus
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Understanding the Model Size Effect on SEM Fit Indices

Authors
Shi, D.Lee, T.Maydeu-Olivares, A.
Issue Date
Apr-2019
Publisher
SAGE Publications Inc.
Keywords
model size effect; structural equation modeling (SEM); fit indices
Citation
Educational and Psychological Measurement, v.79, no.2, pp 310 - 334
Pages
25
Journal Title
Educational and Psychological Measurement
Volume
79
Number
2
Start Page
310
End Page
334
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/3394
DOI
10.1177/0013164418783530
ISSN
0013-1644
1552-3888
Abstract
This study investigated the effect the number of observed variables (p) has on three structural equation modeling indices: the comparative fit index (CFI), the Tucker–Lewis index (TLI), and the root mean square error of approximation (RMSEA). The behaviors of the population fit indices and their sample estimates were compared under various conditions created by manipulating the number of observed variables, the types of model misspecification, the sample size, and the magnitude of factor loadings. The results showed that the effect of p on the population CFI and TLI depended on the type of specification error, whereas a higher p was associated with lower values of the population RMSEA regardless of the type of model misspecification. In finite samples, all three fit indices tended to yield estimates that suggested a worse fit than their population counterparts, which was more pronounced with a smaller sample size, higher p, and lower factor loading. © 2018, The Author(s) 2018.
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