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. © The Author 2005. Published by Oxford University Press on behalf of the Society for Political Methology. All rights reserved.
CITATION STYLE
Zorn, C. (2005). A Solution to separation in binary response models. Political Analysis, 13(2), 157–170. https://doi.org/10.1093/pan/mpi009
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