Course: Statistics for Psychosocial Research: Structural Models
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.
MH 330.657 or equivalent. Auditing MH 330.657 is a sufficient entry criterion provided that the auditing student has completed the problem sets in that course. The course is designed to build upon what is learned in MH 330.657.
This course is the second in a two-quarter series on Statistics for Psychosocial Research. The series is oriented towards latent variable models and related methods and is taught jointly by the Departments of Mental Health and Biostatistics. The first quarter concentrates on measurement and the second quarter on structural models. The first quarter course, or permission of the instructor, is required for enrollment in the second quarter course.
Completion of three problems sets (each of which contribute 20% towards the final grade) and one in-class close-book final exam (which contributes 40% toward the final grade).
Attendance of weekly laboratory sessions is strongly recommended.