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A comparison of model selection indices for nested latent class models

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  • Lin Ting Hsiang

    (National Taipei University, Department of Statistics 67, Section 3, Min-Sheng East Road, Taipei (10433) Taiwan, ROC E-mail:)

Abstract

Latent Class analysis has been used to study the hierarchical relationship among sets of categorical variables. Researchers routinely use chi-squared statistics as model-selection criteria. Due to the limitation of chi-squared statistics, it is desirable to develop other model selection indices. In this study, we compared the performance of chi-squared statistics with three information criteria, Akaike's AIC, Schwarz's BIC and Bozdogan's CAIC. The factors actually manipulated in this study were types of latent class model and conditional response probabilities including intrusion and omission error rates for certain models.

Suggested Citation

  • Lin Ting Hsiang, 2006. "A comparison of model selection indices for nested latent class models," Monte Carlo Methods and Applications, De Gruyter, vol. 12(3), pages 239-259, October.
    • Handle: RePEc:bpj:mcmeap:v:12:y:2006:i:3:p:239-259:n:3
    DOI: 10.1515/156939606778705164
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    References listed on IDEAS

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    1. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    2. Hirotugu Akaike, 1987. "Factor analysis and AIC," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 317-332, September.
    3. C. Mitchell Dayton & George Macready, 1976. "A probabilistic model for validation of behavioral hierarchies," Psychometrika, Springer;The Psychometric Society, vol. 41(2), pages 189-204, June.
    4. C. Dayton & George Macready, 1980. "A scaling model with response errors and intrinsically unscalable respondents," Psychometrika, Springer;The Psychometric Society, vol. 45(3), pages 343-356, September.
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