Book ChapterDOI
Factor Analysis and AIC
TLDR
The information criterion AIC was introduced to extend the method of maximum likelihood to the multimodel situation by relating the successful experience of the order determination of an autoregressive model to the determination of the number of factors in the maximum likelihood factor analysis as discussed by the authors.Abstract:
The information criterion AIC was introduced to extend the method of maximum likelihood to the multimodel situation. It was obtained by relating the successful experience of the order determination of an autoregressive model to the determination of the number of factors in the maximum likelihood factor analysis. The use of the AIC criterion in the factor analysis is particularly interesting when it is viewed as the choice of a Bayesian model. This observation shows that the area of application of AIC can be much wider than the conventional i.i.d. type models on which the original derivation of the criterion was based. The observation of the Bayesian structure of the factor analysis model leads us to the handling of the problem of improper solution by introducing a natural prior distribution of factor loadings.read more
Citations
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Journal ArticleDOI
Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives
Li-tze Hu,Peter M. Bentler +1 more
TL;DR: In this article, the adequacy of the conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice were examined, and the results suggest that, for the ML method, a cutoff value close to.95 for TLI, BL89, CFI, RNI, and G...
Journal ArticleDOI
Evaluating Goodness-of-Fit Indexes for Testing Measurement Invariance
TL;DR: In this paper, the authors examined the change in the goodness-of-fit index (GFI) when cross-group constraints are imposed on a measurement model and found that the change was independent of both model complexity and sample size.
Journal ArticleDOI
Fit indices in covariance structure modeling : Sensitivity to underparameterized model misspecification
Li-tze Hu,Peter M. Bentler +1 more
TL;DR: In this article, the sensitivity of maximum likelihood (ML), generalized least squares (GLS), and asymptotic distribution-free (ADF)-based fit indices to model misspecification, under conditions that varied sample size and distribution.
Journal ArticleDOI
Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study
TL;DR: Whereas the Bayesian Information Criterion performed the best of the ICs, the bootstrap likelihood ratio test proved to be a very consistent indicator of classes across all of the models considered.
Book
Confirmatory Factor Analysis for Applied Research
TL;DR: In this article, the authors present a detailed, worked-through example drawn from psychology, management, and sociology studies illustrate the procedures, pitfalls, and extensions of CFA methodology.
References
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Journal ArticleDOI
A new look at the statistical model identification
TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
Proceedings Article
Information Theory and an Extention of the Maximum Likelihood Principle
TL;DR: The classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion to provide answers to many practical problems of statistical model fitting.
Book ChapterDOI
Information Theory and an Extension of the Maximum Likelihood Principle
TL;DR: In this paper, it is shown that the classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion.
Book
Modern factor analysis
TL;DR: The third edition of HARMAN's authoritative text as mentioned in this paper incorporates the many new advances made in computer science and technology over the last ten years The author gives full coverage to both theoretical and applied aspects of factor analysis from its foundations through the most advanced techniques This highly readable text will be welcomed by researchers and students working in psychology, statistics, economics and related disciplines