Abstract
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.
Original language | English (US) |
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Pages (from-to) | 157-170 |
Number of pages | 14 |
Journal | Political Analysis |
Volume | 13 |
Issue number | 2 |
DOIs | |
State | Published - 2005 |
All Science Journal Classification (ASJC) codes
- Sociology and Political Science
- Political Science and International Relations