Title

Sensitivity Of Fit Indices To Model Misspecification And Model Types

Abstract

The search for cut-off criteria of fit indices for model fit evaluation (e.g., Hu & Bender, 1999) assumes mat these fit indices are sensitive to model misspecification, but not to different types of models. If fit indices were sensitive to different types of models that are misspecified to the same degree, it would be very difficult to establish cut-off criteria that would be generally useful. The issue about SEM fit indices being sensitive to different types of models has not received sufficient attention, although there is some research suggesting that this might be the case (e.g., Kenny & McCoach, 2003). This study examines if fit indices are sensitive to different types of models while controlling for the severity of model misspecification. The findings show that most fit indices, including some very popular ones (e.g., RMSEA), may be sensitive to different types of models that have the same degree of specification error. The findings suggest that, for most fit indices, it would be difficult to establish cut-off criteria that would be generally useful in SEM applications. Copyright © 2007, Lawrence Erlbaum Associates, Inc.

Publication Date

1-1-2007

Publication Title

Multivariate Behavioral Research

Volume

42

Issue

3

Number of Pages

509-529

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1080/00273170701382864

Socpus ID

36048984166 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/36048984166

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