Using predictions to compare groups in regression models for binary outcomes∗

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 identification of the coefficients and are expressed in the natural metric of the outcome probability. While we develop our approach using the logit model 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. ∗We thank Long Doan, Trent Mize, Rich Williams, and an anonymous reviewer for their comments. †[email protected], Departments of Sociology & Statistics, Indiana University, Bloomington, IN 47401. ‡[email protected]; Department of Sociology, University of Notre Dame, Notre Dame, IN 46556.