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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Collections - College of Social Sciences > Department of Psychology > 1. Journal Articles
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