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Pek, Jolynn; Losardo, Diane; Bauer, Daniel J. – Structural Equation Modeling: A Multidisciplinary Journal, 2011
Compared to parametric models, nonparametric and semiparametric approaches to modeling nonlinearity between latent variables have the advantage of recovering global relationships of unknown functional form. Bauer (2005) proposed an indirect application of finite mixtures of structural equation models where latent components are estimated in the…
Descriptors: Structural Equation Models, Sampling, Statistical Inference, Computation
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Bauer, Daniel J.; Baldasaro, Ruth E.; Gottfredson, Nisha C. – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Structural equation models are commonly used to estimate relationships between latent variables. Almost universally, the fitted models specify that these relationships are linear in form. This assumption is rarely checked empirically, largely for lack of appropriate diagnostic techniques. This article presents and evaluates two procedures that can…
Descriptors: Structural Equation Models, Mixed Methods Research, Statistical Analysis, Sampling
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Kamata, Akihito; Bauer, Daniel J. – Structural Equation Modeling: A Multidisciplinary Journal, 2008
The relations among several alternative parameterizations of the binary factor analysis model and the 2-parameter item response theory model are discussed. It is pointed out that different parameterizations of factor analysis model parameters can be transformed into item response model theory parameters, and general formulas are provided.…
Descriptors: Factor Analysis, Data Analysis, Item Response Theory, Correlation
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Bauer, Daniel J. – Structural Equation Modeling: A Multidisciplinary Journal, 2005
To date, finite mixtures of structural equation models (SEMMs) have been developed and applied almost exclusively for the purpose of providing model-based cluster analyses. This type of analysis constitutes a direct application of the model wherein the estimated component distributions of the latent classes are thought to represent the…
Descriptors: Structural Equation Models, Multivariate Analysis, Data Analysis, Evaluation Methods
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Hipp, John R.; Bauer, Daniel J.; Bollen, Kenneth A. – Structural Equation Modeling: A Multidisciplinary Journal, 2005
This article describes a SAS macro to assess model fit of structural equation models by employing a test of the model-implied vanishing tetrads. Use of this test has been limited in the past, in part due to the lack of software that fully automates the test in a user-friendly way. The current SAS macro provides a straightforward method for…
Descriptors: Alternative Assessment, Structural Equation Models, Computer Software, Evaluation Methods