Journal
STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL
Volume 27, Issue 2, Pages 219-239Publisher
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/10705511.2019.1642111
Keywords
Missing data; full information maximum likelihood; RMSEA; CFI
Funding
- Natural Sciences and Engineering Research Council of Canada (NSERC) [RGPIN-2015-05251]
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Normal theory full-information maximum likelihood (FIML) is a common estimation technique for incomplete data in structural equation modeling (SEM). However, it is not commonly known that approximate fit indices (AFIs) can be distorted, relative to their complete data counterparts, when FIML is used to handle missing data. In this article, we show that two most popular AFIs, the root-mean-square error of approximation (RMSEA) and the comparative fit index (CFI) often approach different population values under FIML estimation when missing data are present. By deriving the FIML fit function for incomplete data and showing that it is different from the usual maximum likelihood (ML) fit function for complete data, we provide a mathematical explanation for this phenomenon. We also present several analytic examples as well as the results of two large sample simulation studies to illustrate how AFIs change with missing data.
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