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Some Contributions to Efficient Statistics in Structural Models: Specification and Estimation of Moment Structures

Published online by Cambridge University Press:  01 January 2025

P. M. Bentler*
Affiliation:
University of California, Los Angeles
*
Address reprint requests to P. M. Bentler, Department of Psychology, Franz Hall, University of California, Los Angeles, 405 Hilgard Avenue, Los Angeles, CA 90024, USA.

Abstract

Current practice in structural modeling of observed continuous random variables is limited to representation systems for first and second moments (e.g., means and covariances), and to distribution theory based on multivariate normality. In psychometrics the multinormality assumption is often incorrect, so that statistical tests on parameters, or model goodness of fit, will frequently be incorrect as well. It is shown that higher order product moments yield important structural information when the distribution of variables is arbitrary. Structural representations are developed for generalizations of the Bentler-Weeks, Jöreskog-Keesling-Wiley, and factor analytic models. Some asymptotically distribution-free efficient estimators for such arbitrary structural models are developed. Limited information estimators are obtained as well. The special case of elliptical distributions that allow nonzero but equal kurtoses for variables is discussed in some detail. The argument is made that multivariate normal theory for covariance structure models should be abandoned in favor of elliptical theory, which is only slightly more difficult to apply in practice but specializes to the traditional case when normality holds. Many open research areas are described.

Information

Type
Original Paper
Copyright
Copyright © 1983 The Psychometric Society

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