Multilevel models are increasingly used for analysis of clustered and longitudinal data, and methods for continuous outcomes are commonly used and applied. However, many research studies have non-normal outcomes, for example, outcomes that are dichotomous, ordinal, or nominal. Although methods for such non-normal outcomes have been available for quite some time, they are perhaps not as routinely applied as models for continuous outcomes.
This workshop will focus on analysis of dichotomous, ordinal and nominal multilevel outcomes. Both clustered and longitudinal data will be considered, and the following models will be described: multilevel logistic regression for dichotomous outcomes, multilevel logistic regression for nominal outcomes, and multilevel proportional odds and non-proportional odds models for ordinal outcomes. The latter models are useful because the proportional odds assumption of equal covariate effects across the cumulative logits of the model is often unreasonable.