Comparing GEE and Robust Standard Errors for Conditionally Dependent Data

Abstract

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.