, Volume 54, Issue 4, pp 557–585

Latent variable modeling in heterogeneous populations

  • Bengt O. Muthén

DOI: 10.1007/BF02296397

Cite this article as:
Muthén, B.O. Psychometrika (1989) 54: 557. doi:10.1007/BF02296397


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.

Key words

mixturescovariance structuresmultiple-group analysisMIMICLISCOMPrandom parametersmultilevelhierarchical data

Copyright information

© The Psychometric Society 1989

Authors and Affiliations

  • Bengt O. Muthén
    • 1
  1. 1.Graduate School of EducationUniversity of CaliforniaLos Angeles