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Latent Class Analysis and Model Selection

  • José G. Dias
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

This paper discusses model selection for latent class (LC) models. A large experimental design is set that allows the comparison of the performance of different information criteria for these models, some compared for the first time. Furthermore, the level of separation of latent classes is controlled using a new procedure. The results show that AIC3 (Akaike information criterion with 3 as penalizing factor) outperforms other model selection criteria for LC models.

Keywords

Monte Carlo Bayesian Information Criterion Latent Class Latent Class Analysis Latent Class Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin · Heidelberg 2006

Authors and Affiliations

  • José G. Dias
    • 1
  1. 1.Department of Quantitative MethodsISCTE — Instituto Superior de Ciências do Trabalho e da EmpresaLisboaPortugal

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