Browsing by Author "MacCallum, Robert C."
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Item Effect of estimation method on incremental fit indexes for covariance structure models(1993) Sugawara, Hazuki M.; MacCallum, Robert C.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.Item Evaluating goodness of fit in nonmetric multidimensional scaling by ALSCAL.(1981) MacCallum, Robert C.Two types of information are provided to aid users of ALSCAL in evaluating goodness of fit in nonmetric two-way and three-way multidimensional scaling analyses. First, equations are developed for estimating the expected values of SSTRESS and STRESS for random data. Second, a table is provided giving mean values of SSTRESS and STRESS for structured artificial data. This information provides the empirical investigator with a second comparative basis for evaluating values of these indices.Item Validity and cross-validity of metric and nonmetric multiple regression(1979) MacCallum, Robert C.; Cornelius, Edwin T., III; Champney, TimothySeveral questions are raised concerning differences between traditional metric multiple regression, which assumes all variables to be measured on interval scales, and nonmetric multiple regression, which treats variables measured on any scale. Both models are applied to 30 derivation and cross-validation samples drawn from two sets of empirical data composed of ordinally scaled variables. Results indicate that the nonmetric model is, on the average, far superior in fitting derivation samples but that it exhibits much more shrinkage than the metric model. The metric technique fits better than the nonmetric in cross-validation samples. In addition, results produced by the nonmetric model are more unstable across repeated samples. A probable cause of these results is presented, and the need for further research is discussed. A common problem in data analysis involves