STATISTICS FOR PSYCHOSOCIAL RESEARCH: STRUCTURAL MODELS Syllabus
Course Learning Objectives
Upon successfully completing this course, students will be able to:
- design path analysis models
- analyze latent variable panel data with linear structural equation models
- design latent class analysis models in the situation of categorical data
- describe causal inference techniques
Course DescriptionPresents 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.
Intended AudienceDoctoral students interested in Psychosocial Sciences or statistical methodology
Methods of AssessmentStudent evaluation based on class participation, problem sets, and a final exam.
Prerequisites330.657 or consent of instructor
Additional Faculty Notes:
Bollen, KA. Structural Equations with Latent Variables, New York: Wiley and Sons, 1989.
Please see the course Session for a full list of dates and items for this course.
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