Inconvenient truths about logistic regression and the remedy of marginal effects

Abstract

Logistic regression is a standard technique in public administration research. However, there are two inconvenient truths about logistic regression of which scholars should be aware. First, logistic regression results are difficult to interpret. Raw coefficients are expressed in an enigmatic log odds scale and odds ratios are regularly misinterpreted as risk ratios. Second, logistic regression results are non-collapsible, which renders model comparisons invalid. A review of recent public administration articles reveals that these inconvenient truths still plague the discipline. This paper advocates the use of average marginal effects to reckon with both inconvenient truths. Average marginal effects are easy to comprehend because they measure effect sizes on a probability scale. And average marginal effects are collapsible, and hence facilitate valid model comparisons. These concepts are illustrated using data simulations and data from the 2017 Current Population Survey. The paper concludes with suggestions for improved research practice.