Generalized Estimating Equation Models for Correlated Data: A Review with Applications

Political scientists are often called upon to estimate models in which the standard assumption that the data are conditionally independent can be called into question. I review the method of generalized estimating equations (GEE) for dealing with such correlated data. The GEE approach offers a number of advantages to researchers interested in modeling correlated data, including applicability to data in which the outcome variable takes on a wide range of forms. In addition, GEE models allow for substantial flexibility in specifying the correlation structure within cases and offer the potential for valuable substantive insights into the nature of that correlation. Moreover, GEE models are estimable with many currently available software packages, and the interpretation of model estimates is identical to that for commonly used models for uncorrelated data (e.g., logit and probit). I discuss practical issues relating to the use of GEE models and illustrate their usefulness for analyzing correlated data through three applications in political science. mpirical political science is most often interested in estimating the effect of some set of explanatory covariates on an outcome variable of interest. At the same time, in many cases, the data on which we observe the phenomena of interest are likely to be correlated. The most common instances of correlated data are those involving repeated observations over time, either in the form of panel studies or time-series of crosssections.1 Correlated data can also arise in other ways, including dyadic studies (e.g., Oneal and Russett 1997; Hojnacki and Kimball 1998; Huckfeldt, Sprague, and Levine 2000) and examinations of individual decisions in a collegial context, for example, voting decisions in a legislature (e.g., Levitt 1996; Snyder and Groseclose 2000) or a court (e.g., Traut and Emmert 1998). While issues relating to temporal correlation have recently received a good deal of attention among political scientists (Beck and Katz 1995; Beck, Katz, and Tucker 1998; Box-Steffensmeier and Jones 1997; see also Stimson 1985), the latter form of correlation often goes unaddressed. Moreover, few of the methods for dealing with correlation over time are appropriate for use with data where the interdependence is not of a temporal nature, and fewer still are capable of dealing with noncontinuous dependent variables (for example, dichotomous variables or event counts). In this article, I review a general method for dealing with correlated data: the technique of generalized estimating equations (GEEs).2 GEEs of-