Fundamentals of Multivariate Modeling (Spring 2018; EPSY 905)

Course Information

Instructor:Dr. Jonathan Templin
email:jtemplin@ku.edu
Office Phone:785-864-5714
Office:614 Joseph R. Pearson Hall
Office Hours:Vary by week. See schedule here: https://calendar.google.com/calendar/embed?src=jtemplin%40ku.edu&ctz=America%2FChicago

Graduate Teaching AssistantJihong Zhang
GTA Email:jihong.zhang@ku.edu
GTA Office Hours:Fridays 9am-11am

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)
Websites:Quick-R
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
 None
Quizzes To Take Before Class:
None
In-Class Lecture Materials:
In-Class Lecture Videos:
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:
In-Class Lecture Videos:
Active Homework Assignments
Additional Links and Information
None

Interactions in GLMs

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:
In-Class Lecture Videos:
Active Homework Assignments
Additional Links and Information
None

Interactions, Continued

Videos to Watch Prior to Lecture
  • None
Slides and Files From Videos
  • None
Optional Readings on Class OneDrive Folder:
  • None
Quizzes To Take Before Class:
In-Class Lecture Materials:
In-Class Lecture Videos:
Active Homework Assignments
Additional Links and Information

Distributions and Estimation

Videos to Watch Prior to Lecture
Slides and Files From Videos
Optional Readings on Class OneDrive Folder:
  • Kutner et al. (2005): Appendix A and Ch. 1
  • Enders (2010): Ch. 3
Quizzes To Take Before Class:
In-Class Lecture Materials:
In-Class Lecture Videos:
Active Homework Assignments
  • None
Additional Links and Information
  • None

Generalized Linear Models

Videos to Watch Prior to Lecture
  • Introduction to Generalized Linear Models: https://youtu.be/TCEIuqZaEg8
  • Note: watch until 57 minutes in…then move to lecture video below.
Slides and Files From Videos
Optional Readings on Class OneDrive Folder:
  • Enders (2010) ch. 3 (ML)
  • Azen and Walker (2011) chs. 2 & 6 (GLMs)
  • Atkins and Gallop (2007)
  • Cohen, Cohen, West, and Aiken (2002)
Quizzes To Take Before Class:
In-Class Lecture Materials:
In-Class Lecture Videos:
Active Homework Assignments
Additional Links and Information

Matrix Algebra and MVN

Videos to Watch Prior to Lecture
  • None
Slides and Files From Videos
  • None
Optional Readings on Class OneDrive Folder:
  • Johnson & Wichern (2002) chs. 2, 3, and 4.
Quizzes To Take Before Class:
  • None
In-Class Lecture Materials:
In-Class Lecture Videos:
  • Forthcoming after class
Active Homework Assignments
Additional Links and Information
  • None

Multivariate Linear Models

Videos to Watch Prior to Lecture
Slides and Files From Videos
Optional Readings on Class OneDrive Folder:
  • None
Quizzes To Take Before Class:
  • None
In-Class Lecture Materials:
In-Class Lecture Videos:
Active Homework Assignments
Additional Links and Information
  • Forthcoming

Path Analysis

 

Videos to Watch Prior to Lecture
Slides and Files From Videos
Optional Readings on Class OneDrive Folder:
  • Kline (2005) chs. 5, 6
Quizzes To Take Before Class:
In-Class Lecture Materials:
  • None
In-Class Lecture Videos:
Active Homework Assignments
Additional Links and Information
  • Forthcoming

Mixed Models

 

Videos to Watch Prior to Lecture
Slides and Files From Videos
Optional Readings on Class OneDrive Folder:
  • None
Quizzes To Take Before Class:
In-Class Lecture Materials:
  • None
In-Class Lecture Videos:
Active Homework Assignments
Additional Links and Information
  • None

Repeated Measures

 

Videos to Watch Prior to Lecture
Slides and Files From Videos
Optional Readings on Class OneDrive Folder:
  • Maxwell & Delaney (2004) ch. 12-15
  • Wright (1998; shows how Mixed Models can give MANOVA test statistics)
Quizzes To Take Before Class:
In-Class Lecture Materials:
  • None
In-Class Lecture Videos:
  • None (no class)
Active Homework Assignments
Additional Links and Information
  • None

Bayesian and MCMC

Videos to Watch Prior to Lecture
  • None
Slides and Files From Videos
  • None
Optional Readings on Class OneDrive Folder:
  • None
Quizzes To Take Before Class:
  • None
In-Class Lecture Materials:
In-Class Lecture Videos:
  • Forthcoming after class
Active Homework Assignments
  • None
Additional Links and Information

Missing Data and Multiple Imputation

Videos to Watch Prior to Lecture
  • None
Slides and Files From Videos
  • None
Optional Readings on Class OneDrive Folder:
  • Enders (2010), Ch. 4, 7, 8, 9
Quizzes To Take Before Class:
  • None
In-Class Lecture Materials:
In-Class Lecture Videos:
Active Homework Assignments
  • None
Additional Links and Information
  • None

PCA and EFA

Videos to Watch Prior to Lecture
  • None
Slides and Files From Videos
  • None
Optional Readings on Class OneDrive Folder:
  • Johnson & Wichern (2002), Chs. 8 & 9
Quizzes To Take Before Class:
  • None
In-Class Lecture Materials:
In-Class Lecture Videos:
Active Homework Assignments
Additional Links and Information
  • Forthcoming

Classification and Clustering

Videos to Watch Prior to Lecture
  • None
Slides and Files From Videos
  • None
Optional Readings on Class OneDrive Folder:
  • Vermunt & Magidson (2002)
  • McCutcheon (2002)
Quizzes To Take Before Class:
  • None
In-Class Lecture Materials:
In-Class Lecture Videos:
Active Homework Assignments
  • None
Additional Links and Information
  • None

Homework Schedule

Note: All homework assignments are due at 11:59pm of the date noted below.

Homework NumberHomework TypeDeadline
HW0 (Extra Credit)OnlineTBA
HW1: R PracticeNot Online29 Mon
HW2: GLMsOnline12 Feb
HW3: GeneralizedOnline12 Mar
HW4: Multivariate Models with MLOnline09 Apr
HW5: Multivariate Models with REMLOnline30 Apr