Latent class models

J. K. Vermunt*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterScientific

9 Citations (Scopus)

Abstract

A statistical model can be called a latent class (LC) or mixture model if it assumes that some of its parameters differ across unobserved subgroups, LCs, or mixture components. This rather general idea has several seemingly unrelated applications, the most important of which are clustering, scaling, density estimation, and random-effects modeling. This article describes simple LC models for clustering, restricted LC models for scaling, and mixture regression models for nonparametric random-effects modeling, as well as gives an overview of recent developments in the field of LC analysis. Moreover, attention is paid to topics such as maximum likelihood estimation, identification issues, model selection, and software.

Original languageEnglish
Title of host publicationInternational encyclopedia of education
EditorsP.L. Peterson, E.L. Baker, B. McGaw
PublisherElsevier Ltd.
Pages238-244
EditionThird
ISBN (Print)9780080448947
DOIs
Publication statusPublished - 2010

Keywords

  • Cluster analysis
  • Finite-mixture model
  • Latent class analysis
  • Latent markov models
  • Latent profile models
  • Mixture growth models
  • Mixture model
  • Mixture regression
  • Random-effects modeling
  • Scaling models
  • Statistical software

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