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Using Predictions and Marginal Effects to Compare Groups in Regression Models for Binary Outcomes

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  • J. Scott Long
  • Sarah A. Mustillo

Abstract

Methods for group comparisons using predicted probabilities and marginal effects on probabilities are developed for regression models for binary outcomes. Unlike approaches based on the comparison of regression coefficients across groups, the methods we propose are unaffected by the scalar identification of the coefficients and are expressed in the natural metric of the outcome probability. While we develop our approach using binary logit with two groups, we consider how our interpretive framework can be used with a broad class of regression models and can be extended to any number of groups.

Suggested Citation

  • J. Scott Long & Sarah A. Mustillo, 2021. "Using Predictions and Marginal Effects to Compare Groups in Regression Models for Binary Outcomes," Sociological Methods & Research, , vol. 50(3), pages 1284-1320, August.
    • Handle: RePEc:sae:somere:v:50:y:2021:i:3:p:1284-1320
    DOI: 10.1177/0049124118799374
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    References listed on IDEAS

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    1. Edward C. Norton & Morgen M. Miller & Lawrence C. Kleinman, 2013. "Computing adjusted risk ratios and risk differences in Stata," Stata Journal, StataCorp LP, vol. 13(3), pages 492-509, September.
    2. 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.
    3. Richard Breen & Anders Holm & Kristian Bernt Karlson, 2014. "Correlations and Nonlinear Probability Models," Sociological Methods & Research, , vol. 43(4), pages 571-605, November.
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