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Latent Variable Modeling in Heterogeneous Populations

Published online by Cambridge University Press:  01 January 2025

Bengt O. Muthén
Bengt O. Muthén*
Affiliation:
Graduate School of Education, University of California, Los Angeles
*
Requests for reprints should be addressed to Bengt O. Muthrn, Graduate School of Education, University of California, Los Angeles, California, 90024-1521.
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Abstract

Common applications of latent variable analysis fail to recognize that data may be obtained from several populations with different sets of parameter values. This article describes the problem and gives an overview of methodology that can address heterogeneity. Artificial examples of mixtures are given, where if the mixture is not recognized, strongly distorted results occur. MIMIC structural modeling is shown to be a useful method for detecting and describing heterogeneity that cannot be handled in regular multiple-group analysis. Other useful methods instead take a random effects approach, describing heterogeneity in terms of random parameter variation across groups. These random effects models connect with emerging methodology for multilevel structural equation modeling of hierarchical data. Examples are drawn from educational achievement testing, psychopathology, and sociology of education. Estimation is carried out by the LISCOMP program.

Keywords

mixturescovariance structuresmultiple-group analysisMIMICLISCOMPrandom parametersmultilevelhierarchical data
Type
Original Paper
Information
Psychometrika , Volume 54 , Issue 4 , December 1989 , pp. 557 - 585
Copyright
Copyright © 1989 The Psychometric Society

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Footnotes

Presidential address delivered at the Psychometric Society meetings in Los Angeles, USA and Leuven, Belgium, July 1989. The research was supported by Grant No. SES-8821668 from the National Science Foundation and by Grant No. OERI-G-86-003 from the Office for Educational Research and Improvement, Department of Education. I thank Leigh Burstein, Mike Hollis, Linda Muthén, and Albert Satorra for helpful discussions and Tammy Tam, Jin-Wen Yang, Suk-Woo Kim, and Lynn Short for computational assistance. Designs were created by Arlette Collier, Rita Ling and Jennifer Edic-Bryant.

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