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Walker, David A.; Smith, Thomas J. – Measurement and Evaluation in Counseling and Development, 2017
Nonnormality of data presents unique challenges for researchers who wish to carry out structural equation modeling. The subsequent SPSS syntax program computes bootstrap-adjusted fit indices (comparative fit index, Tucker-Lewis index, incremental fit index, and root mean square error of approximation) that adjust for nonnormality, along with the…
Descriptors: Robustness (Statistics), Sampling, Statistical Inference, Goodness of Fit
Shiyko, Mariya P.; Li, Yuelin; Rindskopf, David – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Intensive longitudinal data (ILD) have become increasingly common in the social and behavioral sciences; count variables, such as the number of daily smoked cigarettes, are frequently used outcomes in many ILD studies. We demonstrate a generalized extension of growth mixture modeling (GMM) to Poisson-distributed ILD for identifying qualitatively…
Descriptors: Smoking, Behavior Change, Longitudinal Studies, Data
Lombardi, Luigi; Pastore, Massimiliano – Multivariate Behavioral Research, 2012
In many psychological questionnaires the need to analyze empirical data raises the fundamental problem of possible fake or fraudulent observations in the data. This aspect is particularly relevant for researchers working on sensitive topics such as, for example, risky sexual behaviors and drug addictions. Our contribution presents a new…
Descriptors: Deception, Measures (Individuals), Sampling, Structural Equation Models
Residuals and the Residual-Based Statistic for Testing Goodness of Fit of Structural Equation Models
Foldnes, Njal; Foss, Tron; Olsson, Ulf Henning – Journal of Educational and Behavioral Statistics, 2012
The residuals obtained from fitting a structural equation model are crucial ingredients in obtaining chi-square goodness-of-fit statistics for the model. The authors present a didactic discussion of the residuals, obtaining a geometrical interpretation by recognizing the residuals as the result of oblique projections. This sheds light on the…
Descriptors: Structural Equation Models, Goodness of Fit, Geometric Concepts, Algebra
Savalei, Victoria; Yuan, Ke-Hai – Multivariate Behavioral Research, 2009
Evaluating the fit of a structural equation model via bootstrap requires a transformation of the data so that the null hypothesis holds exactly in the sample. For complete data, such a transformation was proposed by Beran and Srivastava (1985) for general covariance structure models and applied to structural equation modeling by Bollen and Stine…
Descriptors: Statistical Inference, Goodness of Fit, Structural Equation Models, Transformations (Mathematics)
van Lang, Natasja D. J.; Boomsma, Anne; Sytema, Sjoerd; de Bildt, Annelies A.; Kraijer, Dirk W.; Ketelaars, Cees; Minderaa, Ruud B. – Journal of Child Psychology and Psychiatry, 2006
Background: Several studies showed a different symptom structure underlying the spectrum of autistic-like disorders from the behaviour triad as mentioned in the DSM-IV. In the present study, a hypothesised symptom model for autism was constructed, based on earlier explorative findings, and was put to a confirmatory test. Method: Items from the…
Descriptors: Autism, Goodness of Fit, Structural Equation Models, Pervasive Developmental Disorders

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