Logistic regression: When can we do what we think we can do?

Abstract

In a much cited overview article Mood (2010) criticized many of the ways in which the raw coefficients and odds ratios from logistic regression have been used. However, logistic regression has an unusual dependent variable: a probability, which measures how certain we are that an event of interest happens. This degree of certainty is a function of how much information we have, which in case of logistic regression is captured by the variables we add to the model. If the dependent variable is interpreted in that way many of the problems with logistic regression pointed out by Mood (2010) turn out to be desirable properties of the logistic regression model.