Understanding the Model Size Effect on SEM Fit Indices

By Dexin Shi, Taehun Lee, Albert Maydeu-Olivares. Educational and Psychological Measurement, 2018

759 citations recorded by the source corpus.

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.

Subfields: Atomic and Molecular Physics, and Optics, Materials Chemistry, Surfaces, Coatings and Films

View Paper: https://www.semanticscholar.org/paper/367a76162fe5652587e8fde98ad7cda5180a6d13

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Citations in the corpus

Assessing Fit in Ordinal Factor Analysis Models: SRMR vs. RMSEA (2020, background): “Under correctly specified models, Shi et al. (2018) found that the empirical mean ofX 2ULSMV was generally close to the theoretical value (df), but that the empirical variance of X 2ULSMV severely underestimated the theoretical value (i.e., smaller than 2df).”