This workshop covers statistical nonlinear model development with explicitly defined hierarchies. Such multilevel specifications allow researchers to account for different structures in the data and provide for the modeling of variation between defined groups.
The workshop begins with simple nested linear models and proceeds on to non-nested models, multilevel models with dichotomous outcomes, and multilevel generalized linear models. In each case, a Bayesian perspective on inference and computation is featured.
The focus in the workshop will be practical steps for specifying, fitting, and checking multilevel models with much time spent on the details of computation in the R and BUGS environments.
At the conclusion of this workshop participants will: be able to specify and estimate multilevel (hierarchical) models with nonlinear outcomes, and will have had exposure to Bayesian approaches including MCMC computation, as well as be able to assess model reliability and fit in multilevel models.