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On Group Comparisons with Logistic Regression Models

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  • Kuha, Jouni
  • Mills, Colin

Abstract

It is widely believed that regression models for binary responses are problematic if we want to compare estimated coefficients from models for different groups or with different explanatory variables. This concern has two forms. The first arises if the binary model is treated as an estimate of a model for an unobserved continuous response, and the second when models are compared between groups which have different distributions of other causes of the binary response. We argue that these concerns are usually misplaced. The first of them is only relevant if the unobserved continuous response is really the subject of substantive interest. If it is, the problem should be addressed through better measurement of this response. The second concern refers to a situation which is unavoidable but unproblematic, in that causal effects and descriptive associations are inherently group-dependent and can be compared as long as they are correctly estimated.

Suggested Citation

  • Kuha, Jouni & Mills, Colin, 2017. "On Group Comparisons with Logistic Regression Models," SocArXiv gwck3, Center for Open Science.
    • Handle: RePEc:osf:socarx:gwck3
    DOI: 10.31219/osf.io/gwck3
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    References listed on IDEAS

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    1. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    2. Richard Breen & Anders Holm & Kristian Bernt Karlson, 2014. "Correlations and Nonlinear Probability Models," Sociological Methods & Research, , vol. 43(4), pages 571-605, November.
    3. Jacob Marschak, 1959. "Binary Choice Constraints on Random Utility Indicators," Cowles Foundation Discussion Papers 74, Cowles Foundation for Research in Economics, Yale University.
    4. 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.
    5. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, October.
    6. Maarten Buis, 2015. "Logistic regression: Why we often can do what we think we can do," United Kingdom Stata Users' Group Meetings 2015 08, Stata Users Group.
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    Cited by:

    1. Gerhard Tutz, 2020. "Modelling heterogeneity: on the problem of group comparisons with logistic regression and the potential of the heterogeneous choice model," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(3), pages 517-542, September.

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