EFFECT OF ESTIMATION METHOD ON INCREMENTAL FIT INDEXES FOR COVARIANCE STRUCTURE MODELS

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 non-incremental 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.