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A Solution to Separation in Binary Response Models

Published online by Cambridge University Press:  04 January 2017

Christopher Zorn
Christopher Zorn*
Affiliation:
Law and Social Science Program, National Science Foundation, 4201 Wilson Boulevard, Suite 995, Arlington, VA 22230. e-mail: czorn@nsf.gov
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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.

Information

Type
Research Article
Information
Political Analysis , Volume 13 , Issue 2 , Spring 2005 , pp. 157 - 170
Copyright
Copyright © The Author 2005. Published by Oxford University Press on behalf of the Society for Political Methodology 

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