Structural Equation Modeling

Description from DukeHub

Basic and advanced Structural Equation Modeling (SEM) with and without latent variables. Topics include statistical theory underlying multivariate statistical modeling specific to SEM, path analysis, confirmatory (and exploratory) factor analysis, multiple group analysis, multiple indicator multiple cause (MIMIC) modeling, full SEM, and contemporary extensions to growth modeling and latent class analysis. Homework involves applying SEM software to real and simulated social science data. Recommended prerequisite: a basic statistics course and a course covering linear regression modeling.

Skills

• data and statisticsdescription: "modeling" and 8 more

AI-resilient: 1 of 5 1/5

• No: Physical, lab or field work
• No: Live work with people
• No: Judgment someone is accountable for
• No: A craft you practice with your hands or body
• ✓Yes: Small, in-person format · seminar, 15 seats per section

The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works

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