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Please use this identifier to cite or link to this item: http://hdl.handle.net/11299/116375

Title: Effect of estimation method on incremental fit indexes for covariance structure models
Authors: Sugawara, Hazuki M.
MacCallum, Robert C.
Issue Date: 1993
Citation: 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
Abstract: 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.
URI: http://purl.umn.edu/116375
Appears in Collections:Volume 17, 1993

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