In a typical study involving covariance structure
modeling, fit of a model or a set of alternative
models is evaluated using several indicators of fit
under one estimation method, usually maximum
likelihood. This study examined the stability across
estimation methods of incremental and nonincremental
fit measures that use the information
about the fit of the most restricted (null) model as
a reference point in assessing the fit of a more
substantive model to the data. A set of alternative
models for a large empirical dataset was analyzed
by asymptotically distribution-free, generalized
least squares, maximum likelihood, and ordinary
least squares estimation methods. Four incremental
and four nonincremental fit indexes were compared.
Incremental indexes were quite unstable
across estimation methods-maximum likelihood
and ordinary least squares solutions indicated
better fit of a given model than asymptotically
distribution-free and generalized least squares solutions.
The cause of this phenomenon is explained
and illustrated, and implications and recommendations
for practice are discussed. Index terms:
covariance structure models, goodness of fit,
incremental fit index, maximum likelihood estimation,
parameter estimation, structural equation models.
Sugawara, Hazuki M & MacCallum, Robert C. (1993). Effect of estimation method on incremental fit indexes for covariance structure models. Applied Psychological Measurement, 17, 365-377. doi:10.1177/014662169301700405
Sugawara, Hazuki M.; MacCallum, Robert C..
Effect of estimation method on incremental fit indexes for covariance structure models.
Retrieved from the University of Minnesota Digital Conservancy,
Content distributed via the University of Minnesota's Digital
Conservancy may be subject to additional license and use
restrictions applied by the depositor.