For Those Who Like Odds: A Direct Interpretation of the Logit Coefficient for Continuous Variables


  • *Direct correspondence to Marc L. Swatt, College of Criminal Justice, 433 Churchill Hall, Northeastern University, Boston, MA 02115 〈〉. The authors thank Lisa L. Sample at the University of Nebraska at Omaha for her invaluable assistance with this article. The authors also thank the anonymous reviewer whose comments strengthened this article.


Objective. Researchers have suggested a number of different methods for interpreting the coefficients in a logit model. Unfortunately, many of these interpretations suffer from a lack clarity or involve a substantial number of manipulations of the logit coefficient prior to interpretation. In this article, we discuss a straightforward method of interpreting logit coefficients for continuous dependent variables without the need for extensive transformation.

Method. Drawing on Stolzenberg's (1979) techniques for interpreting logarithmic regression models, we demonstrate that the logit coefficient multiplied by 100 can be directly interpreted as the percentage change in the odds given a unit change in the independent variable. We also derive an analogous interpretation for ordinal logit models.

Results. After these derivations, the ease of this technique is demonstrated using a simple logit model.

Conclusion. Given the generality of this interpretation, as well as its ease of computation, it is hoped that researchers from a number of disciplines will adopt this strategy for interpreting logit models.