Information-theoretic latent distribution modeling: Distinguishing discrete and continuous latent variable models

Kristian E. Markon, Robert Krueger

Research output: Contribution to journalArticlepeer-review

42 Scopus citations

Abstract

Distinguishing between discrete and continuous latent variable distributions has become increasingly important in numerous domains of behavioral science. Here, the authors explore an information-theoretic approach to latent distribution modeling, in which the ability of latent distribution models to represent statistical information in observed data is emphasized. The authors conclude that loss of statistical information with a decrease in the number of latent values provides an attractive basis for comparing discrete and continuous latent variable models. Theoretical considerations as well as the results of 2 Monte Carlo simulations indicate that information theory provides a sound basis for modeling latent distributions and distinguishing between discrete and continuous latent variable models in particular.

Original languageEnglish (US)
Pages (from-to)228-243
Number of pages16
JournalPsychological Methods
Volume11
Issue number3
DOIs
StatePublished - Sep 2006

Keywords

  • Information theory
  • Information-theoretic approach
  • Latent class
  • Latent distribution modeling
  • Latent trait
  • Model selection

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