Constrained latent class analysis: Simultaneous classification and scaling of discrete choice data

Ulf Böckenholt*, Ingo Böckenholt

*Corresponding author for this work

Research output: Contribution to journalArticle

24 Scopus citations

Abstract

A reparameterization of a latent class model is presented to simultaneously classify and scale nominal and ordered categorical choice data. Latent class-specific probabilities are constrained to be equal to the preference probabilities from a probabilistic ideal-point or vector model that yields a graphical, multidimensional representation of the classification results. In addition, background variables can be incorporated as an aid to interpreting the latent class-specific response probabilities. The analyses of synthetic and real data sets illustrate the proposed method.

Original languageEnglish (US)
Pages (from-to)699-716
Number of pages18
JournalPsychometrika
Volume56
Issue number4
DOIs
StatePublished - Dec 1 1991

Keywords

  • classification
  • latent class analysis
  • multidimensional scaling

ASJC Scopus subject areas

  • Psychology(all)
  • Applied Mathematics

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