Recent developments in multilevel modeling, including models for binary and count responses

Mixed-effects models contain both fixed and random effects. The fixed effects are analogous to standard regression coefficients and are estimated directly. The random effects are not directly estimated but instead are summarized according to their estimated variances and covariances, known as variance components. Random effects take the form of either random intercepts or random coefficients, and the grouping structure of the data may consist of multiple levels of nested groups. In Stata, one can fit mixed models with continuous (Gaussian) responses by using xtmixed and, in Stata 10, fit mixed models with binary and count responses by using xtmelogit and xtmepoisson, respectively. All three commands have a common multiequation syntax and output, and postestimation tasks such as the prediction of random effects and likelihood-ratio comparisons of nested models also take a common form. This presentation will cover many models that one can fit using these three commands. Among these are simple random intercept models, random-coefficient models, growth curve models, and crossed-effects models.