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Presents quantitative approaches to theory construction in the context of multiple response variables, with models for both continuous and categorical data. Topics include the statistical basis for causal inference; principles of path analysis; linear structural equation analysis incorporating measurement models; latent class regression; and analysis of panel data with observed and latent variable models. Draws examples from the social sciences, including the status attainment approach to intergenerational mobility, behavior genetics models of disease and environment, consumer satisfaction, functional impairment and disability, and quality of life.

Upon successful completion of this course, students will be able to design path analysis models; to analyze latent variable longitudinal data with linear structural equation models; to design latent class analysis models in the situation of categorical data; and to read and evaluate scientific articles as regards testing of causal relationships in public health based on a priori theory.

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