This course is on hierarchical models for continuous and discrete outcomes. Hierarchical models are used when the units of observation are grouped within clusters. Observations in a cluster typically are not mutually independent for given covariate values as required by conventional linear and logistic regression models. Longitudinal or repeated measures data can also be thought of as clustered data with measurement occasions clustered within subjects. The focus of the course is on hierarchical linear models and their assumptions, as well as practical aspects of developing models to address research questions and interpreting the findings.
Credits: 3.0