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Model Selection Strategies and the Use of Association Models to Detect Group Differences

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  • RAYMOND SIN-KWOK WONG

    (University of California, Santa Barbara)

Abstract

This article discusses the use of association models to detect group differences and the choice of model selection criteria under different conditions. The performances of several commonly used model selection criteria in log-linear modeling are examined using Monte Carlo simulations. The results suggest that no single criterion can play the role of panacea in model selection. L 2 / df , normed fit index (NFI) (or 1 - L 1 2 /L 0 2 ), and Akaike's information criterion (AIC) are systematically biased toward models incorporating group differences. The log-likelihood ratio test (LRT), in conjunction with the nested chi-square difference test, is useful for samples with moderate sizes. Bayesian information criterion (BIC) is most reliable among the measures tested. It consistently provides correct information about group differences in association, especially when the sample size is large. Although BIC may give equivocal results under certain conditions, the ambiguities can be mitigated by an inclusion of competing models and a cautious interpretation of any small improvement in BIC.

Suggested Citation

  • Raymond Sin-Kwok Wong, 1994. "Model Selection Strategies and the Use of Association Models to Detect Group Differences," Sociological Methods & Research, , vol. 22(4), pages 460-491, May.
    • Handle: RePEc:sae:somere:v:22:y:1994:i:4:p:460-491
    DOI: 10.1177/0049124194022004002
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    References listed on IDEAS

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    1. Becker, Mark P., 1989. "On the bivariate normal distribution and association models for ordinal categorical data," Statistics & Probability Letters, Elsevier, vol. 8(5), pages 435-440, October.
    2. Ulf Olsson, 1979. "Maximum likelihood estimation of the polychoric correlation coefficient," Psychometrika, Springer;The Psychometric Society, vol. 44(4), pages 443-460, December.
    3. 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.
    4. Ledyard Tucker & Charles Lewis, 1973. "A reliability coefficient for maximum likelihood factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 38(1), pages 1-10, March.
    5. Nobuo Inagaki, 1977. "Two errors in statistical model fitting," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 29(1), pages 131-152, December.
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