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Gives an overview of "multilevel models" and their application in public health and biomedical research. Multilevel models are statistical regression models for data that are clustered in some way, violating the usual independence assumption. Typically, the predictor and outcome variables occur at multiple levels of aggregation (e.g., at the personal, family, neighborhood, community and/or regional levels). Multilevel models account for the clustering of the outcomes and are used to ask questions about the influence of factors at different levels and about their interactions. Students focus on the main ideas and on examples of multilevel models from public health research. Students learn to formulate their substantive questions in terms of a multilevel model, to fit multilevel models using Stata during laboratory sessions and to interpret the results.