Multivariate Modeling Methods (Spring 2026; Clemson University: EDF 9780)

Course Objectives

In this course, contemporary approaches to multivariate analysis with maximum likelihood and Bayesian methods are presented. Classical topics in multivariate analysis including multivariate analysis of variance and exploratory factor analysis, are covered in the context of mixed-effects models, preparing students for subsequent courses and research that use such model-based methods. Possible topics include extensions of linear models (regression and analysis of variance) for non-normal data with link functions, introductory matrix algebra, missing data modeling techniques, models for repeated measures data, and path analysis models for multivariate regression evaluating both moderation and mediation effects. Topics will be finalized after the first meeting of the course.

Note: The materials in this course were created by both myself and Lesa Hoffman through years of collaboration. Materials created by Lesa are used with her permission.

Course Readings

The course will provide all readings on our course Canvas site.

Course Details

Instructor: Jonathan Templin
email: jltempli@clemson.edu
Course website: https://jonathantemplin.github.io/Multivariate-Modeling-Spring-2026/
Course repo: https://github.com/jonathantemplin/Multivariate-Modeling-Spring-2026
Course YouTube Playlist: https://www.youtube.com/playlist?list=PLSmMs4UgmSMhb55Mn9nB2H955lk2895vV
Office: 213C Tillman Hall
Classroom: 402 Tillman Hall
Instructor Office Hours: M 2:00pm-4:00pm via Zoom at https://clemson.zoom.us/j/91876861418?pwd=aZogrbrZXFsiITtCf0B2167qzXUOfK.1
Office Hours Calendar: https://calendar.google.com/calendar/ical/03d02899ca3d2bd58a133223df6349a585cfad8b63bd51b09adef60fbf0edd43%40group.calendar.google.com/public/basic.ics