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Mooijaart, Ab; Bentler, Peter M. – Structural Equation Modeling: A Multidisciplinary Journal, 2010
In the last decades there has been an increasing interest in nonlinear latent variable models. Since the seminal paper of Kenny and Judd, several methods have been proposed for dealing with these kinds of models. This article introduces an alternative approach. The methodology involves fitting some third-order moments in addition to the means and…
Descriptors: Computation, Statistical Analysis, Structural Equation Models, Maximum Likelihood Statistics
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Yuan, Ke-Hai; Bentler, Peter M. – Educational and Psychological Measurement, 2004
In mean and covariance structure analysis, the chi-square difference test is often applied to evaluate the number of factors, cross-group constraints, and other nested model comparisons. Let model M[a] be the base model within which model M[b] is nested. In practice, this test is commonly used to justify M[b] even when M[a] is misspecified. The…
Descriptors: Statistical Significance, Item Response Theory, Computation, Statistical Analysis
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Gold, Michael S.; Bentler, Peter M.; Kim, Kevin H. – Structural Equation Modeling: A Multidisciplinary Journal, 2003
This article describes a Monte Carlo study of 2 methods for treating incomplete nonnormal data. Skewed, kurtotic data sets conforming to a single structured model, but varying in sample size, percentage of data missing, and missing-data mechanism, were produced. An asymptotically distribution-free available-case (ADFAC) method and structured-model…
Descriptors: Monte Carlo Methods, Computation, Sample Size, Comparative Analysis