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Using Heterogeneous Choice Models to Compare Logit and Probit Coefficients Across Groups

Author

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  • Richard Williams

    (University of Notre Dame, Notre Dame, Indiana, rwilliam@nd.edu)

Abstract

Allison (1999) notes that comparisons of logit and probit coefficients across groups can be invalid and misleading, proposes a procedure by which these problems can be corrected, and argues that ``routine use [of this method] seems advisable'' and that ``it is hard to see how [the method] can be improved.'' In this article, the author argues that as originally proposed, Allison's method can have serious problems and should not be applied on a routine basis. However, this study also shows that his model belongs to a larger class of models variously known as heterogeneous choice or location-scale models. Several advantages of this broader and more flexible class of models are illustrated. Dependent variables can be ordinal in addition to binary, sources of heterogeneity can be better modeled and controlled for, and insights can be gained into the effects of group characteristics on outcomes that would be missed by other methods.

Suggested Citation

  • Richard Williams, 2009. "Using Heterogeneous Choice Models to Compare Logit and Probit Coefficients Across Groups," Sociological Methods & Research, , vol. 37(4), pages 531-559, May.
    • Handle: RePEc:sae:somere:v:37:y:2009:i:4:p:531-559
    DOI: 10.1177/0049124109335735
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    References listed on IDEAS

    as
    1. Richard Williams, 2006. "Generalized ordered logit/partial proportional odds models for ordinal dependent variables," Stata Journal, StataCorp LP, vol. 6(1), pages 58-82, March.
    2. Yatchew, Adonis & Griliches, Zvi, 1985. "Specification Error in Probit Models," The Review of Economics and Statistics, MIT Press, vol. 67(1), pages 134-139, February.
    3. Lindeboom, Maarten & van Doorslaer, Eddy, 2004. "Cut-point shift and index shift in self-reported health," Journal of Health Economics, Elsevier, vol. 23(6), pages 1083-1099, November.
    4. J. Scott Long & Jeremy Freese, 2006. "Regression Models for Categorical Dependent Variables using Stata, 2nd Edition," Stata Press books, StataCorp LP, edition 2, number long2, March.
    Full references (including those not matched with items on IDEAS)

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