Fundamentals of Multivariate Modeling (Spring 2018; EPSY 905)

Course Information

Instructor:Dr. Jonathan Templin
Office Phone:785-864-5714
Office:614 Joseph R. Pearson Hall
Office Hours:10am-12pm Fridays (see main website for cancellations) and by appointment

Graduate Teaching AssistantJihong Zhang

Course Meeting Time:1:30pm-4:20pm Tuesdays
Course Meeting Location:150 Joseph R. Pearson Hall

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.

Helpful R Links and Resources

Books: R for Data Science (Grolemund and Wickham, 2015)
R for SAS and SPSS Users by Muenchen (2007)
An introduction to R by Venables et al. (2013)
aRrgh: a newcomer's (angry) guide to R
DataCamp Introduction to R
Cookbook for R
Quick References:Reference Card #1 (various authors, noted on card)
Reference Card #2 (various authors, noted on card)

Tentative Course Schedule

Introduction to R

Videos to Watch Prior to Lecture
Quizzes To Take Before Class:
In-Class Lecture Materials:
In-Class Lecture Videos:
Forthcoming after class
Active Homework Assignments
Additional Links and Information

General Linear Models

Videos to Watch Prior to Lecture
Slides and Files From Videos
Optional Readings on Class OneDrive Folder:
  • Hoffman (2015), Chapter 2
Quizzes To Take Before Class:
In-Class Lecture Materials:
  • Forthcoming
In-Class Lecture Videos:
  • Forthcoming after class
Active Homework Assignments
Additional Links and Information

Homework Schedule

Homework NumberHomework TypeDeadline
HW0 (Extra Credit)OnlineTBA
HW1: R PracticeNot Online29 Mon
HW2: GLMsOnline12 Feb
HW3: GeneralizedOnline26 Feb
HW4: MatricesOnline12 Mar
HW5: Path AnalysisOnline26 Mar
HW6: REMLOnline09 Apr
HW7: Multiple ImputationOnline23 Apr
HW8: PCA/EFAOnline08 May