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.