Brief Course Description
In this course, contemporary approaches to multivariate analysis using mixed-effects models estimated 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. 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.
The course will use the R statistical package with R Studio and a set of packages including the EPSY905R Package (Templin, 2018) for all computational and data analysis work involved in the course.
For all other specific information regarding general course policies, course evaluation rubrics, and grading systems, please see the course syllabus at the link below.
Tentatively, the course will be held in a flipped format. Students will watch lecture videos during the week before class. Class time will be spent with Q & A about the video contents and then an exercise relating to the topic or homework using R.
All readings will be made available via campus OneDrive shared folder. Please email me for an invitation to this folder.