Suchergebnisse

In recent years political scientists have become increasingly sensitive to questions of conditional dependence in their data. I outline and compare two general, widely-used approaches for addressing such dependence—robust variance estimators and generalized estimating equations (GEEs)—using data on votes in Supreme Court search and seizure decisions between 1963 and 1981. The results make clear that choices about the unit on which data are grouped, i.e., clustered, are typically of far greater significance than are decisions about which type estimator is used.

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.