140.656.01 MULTILEVEL STATISTICAL MODELS IN PUBLIC HEALTH

Explores conceptual and formal approaches to the design, analysis, and interpretation of studies with a “multilevel” or “hierarchical” (clustered) data structure (e.g., individuals in families in communities). Develops skills to implement and interpret random effects, variance component models that reflect the multi-level structure for both predictor and outcome variables. Topics include: building hierarchies; interpretation of population-average and level-specific summaries; estimation and inference based on variance components; shrinkage estimation; discussion of special topics including centering, use of contextual variables, ecological bias, sample size and missing data within multilevel models. STATA and SAS software are supported.

Upon successfully completing this course, students will be able to: a) define multilevel data, b) implement and interpret results associated with Multi-level Statistical Models (MLMs), c) identify when and why MLMs can or should be used; when they are unnecessary or possibly dangerous; and d) describe the implications of centering, contextual variables, missing data and ecological bias within MLMs.