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The effects of misspecification type and nuisance variables on the behaviors of population fit indices used in structural equation modeling

University of British Columbia

The present study examined the performance of population fit indices used in structural equation modeling. Index performances were evaluated in multiple modeling situations that involved misspecification due to either omitted error covariances or to an incorrectly modeled latent structure. Additional nuisance parameters, including loading size, factor correlation size, model size, and model balance, were manipulated to determine which indices’ behaviors were influenced by changes in modeling situations over and above changes in the size and severity of misspecification. The study revealed that certain indices (CFI, NNFI) are more appropriate to use when models involve latent misspecification, while other indices (RMSEA, GFI, SRMR) are more appropriate in situations where models involve misspecification due to omitted error covariances. It was found that the performances of all indices were affected to some extent by additional nuisance parameters. In particular, higher loading sizes led to increased sensitivity to misspecification and model size affected index behavior differently depending on the source of the misspecification.

Text

2011-07-21

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/36240

Psychology

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/