A common problem in models for dichotomous dependent variables is "separation," which occurs when one or more of a model's covariates perfectly predict some binary outcome. Separation raises a particularly difficult set of issues, often forcing researchers to choose between omitting clearly important covariates and undertaking post-hoc data or estimation corrections. In this article I present a method for solving the separation problem, based on a penalized likelihood correction to the standard binomial GLM score function. I then apply this method to data from an important study on the postwar fate of leaders.
All Science Journal Classification (ASJC) codes
- Sociology and Political Science
- Political Science and International Relations