Journal article
Information-theoretic latent distribution modeling: distinguishing discrete and continuous latent variable models
Psychological methods, Vol.11(3), pp.228-243
09/2006
DOI: 10.1037/1082-989X.11.3.228
PMID: 16953702
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.
Details
- Title: Subtitle
- Information-theoretic latent distribution modeling: distinguishing discrete and continuous latent variable models
- Creators
- Kristian E Markon - Department of Psychology, University of Minnesota, Twin Cities Campus, Minneapolis, MN 55455, USA. mark0060@tc.umn.eduRobert F Krueger
- Resource Type
- Journal article
- Publication Details
- Psychological methods, Vol.11(3), pp.228-243
- DOI
- 10.1037/1082-989X.11.3.228
- PMID
- 16953702
- ISSN
- 1082-989X
- eISSN
- 1939-1463
- Grant note
- MH65137 / NIMH NIH HHS
- Language
- English
- Date published
- 09/2006
- Academic Unit
- Psychological and Brain Sciences
- Record Identifier
- 9984083887302771
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