Revisiting the Model Size Effect in Structural Equation Modeling
- Authors
- Shi, Dexin; Lee, Taehun; Terry, Robert A.
- Issue Date
- Jan-2018
- Publisher
- ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
- Keywords
- correctional methods; likelihood ratio statistic; model size effect; structural equation modeling (SEM)
- Citation
- STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, v.25, no.1, pp 21 - 40
- Pages
- 20
- Journal Title
- STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL
- Volume
- 25
- Number
- 1
- Start Page
- 21
- End Page
- 40
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/1460
- DOI
- 10.1080/10705511.2017.1369088
- ISSN
- 1070-5511
1532-8007
- Abstract
- Fitting a large structural equation modeling (SEM) model with moderate to small sample sizes results in an inflated Type I error rate for the likelihood ratio test statistic under the chi-square reference distribution, known as the model size effect. In this article, we show that the number of observed variables (p) and the number of free parameters (q) have unique effects on the Type I error rate of the likelihood ratio test statistic. In addition, the effects of p and q cannot be fully explained using degrees of freedom (df). We also evaluated the performance of 4 correctional methods for the model size effect, including Bartlett's (1950), Swain's (1975), and Yuan's (2005) corrected statistics, and Yuan, Tian, and Yanagihara's (2015) empirically corrected statistic. We found that Yuan et al.' s (2015) empirically corrected statistic generally yields the best performance in controlling the Type I error rate when fitting large SEM models.
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