Introduction to Longitudinal Data Analysis - STA533


Course Objectives

Students completing this course should have gained:

  • Familiarity with methods for clustered data
  • Confidence and experience in interpreting longitudinal data

Course Topics

The course will be broken into five components.  Some issues will be common between all five parts:

  • PART 1 – Primitive techniques for LDA
  • PART 2 – Introduction to Standard Repeated Methods
  • PART 3 – Advanced Issues for Continuous Responses
  • PART 4 - Categorical Data Methods including GEE and Clustered Techniques
  • PART 5 - Missing Data

Example Syllabus

STA533 – Introduction to Longitudinal Data Analysis

Office Hours: Tuesday: 2pm-3:00pm; Wednesday: 2:30pm-4:30 pm Thursday: 1:00pm-3:00 pm; BY APPOINTMENT as needed.


Longitudinal Data Analysis. Hedeker and Gibbons, Wiley Press, 2006. This book will be a great reference. We will bounce around various portions. Class will focus on the APPLIED setting with some discussion on the THEORY.


Students completing this course should

  • Familiarity with methods for clustered data
  • Confidence and experience in interpreting longitudinal data.
Course Structure

Lecture: STA531 is a lecture course. Lecture outlines will be provide on the Blackboard webpage for the course in advance of the lecture. We will discuss the points as specified in the outline.

Class Participation: We will be building SAS code on the fly; therefore participation and performance of the code will be expected. Please have a flash drive for this class, since you will need to do things as we go.

Reading: You will be given/directed to articles to read for various analytical techniques.

Individual Presentation:  During the semester, the students should decide a hypothesis of interest for which they can acquire/survey data repeatedly. Students will need to acquire at least 8-10 repeated measures per subject. The project will involve their discussion of their specific goals.

Wk Date Topic Chapters
1 1/13 Primitive techniques for LDA 1
2 1/20 Basic techniques- Repeated Measures ANOVA 2
3 1/27

Repeated Measures ANOVA continued

Multivariate ANOVA

2, 3
4 2/3 EXAM 1  
5 2/10 Introduction to HLM 4
6 2/17 HLM Continued 5
7 2/24 Extensions of HLM with Polynomial or Piecewise Modeling 5
9 3/17


Model Diagnostics

10 3/24 EXAM 2  
11 3/31 Generalized Estimating Equation (GEE) 8
12 4/7

GEE Continued

Generalized Linear Mixed Models for Binary/Poission Data

13  4/14 Ordinal Clustered Data 10,11
14 4/21 Missing Data 14
    FINAL- Take Home Due TBA  

Students will be using SAS 9. Students should be comfortable in using SAS prior to taking this course. The following skills are expected to be known:

  • Reading and creating SAS data sets.
  • Familiarity with basic stat procedures such as PROC REG, PROC GLM, PROC GENMOD, and PROC MIXED.
  • Comfortable in the SAS LAB.

Two in class test 20% each (Mid - February & End of March, homework/participation at 20%, final exam at 20%, individual project at 20%

Attendance: Attendance is important and expected. Absence from a test is acceptable for illness/emergency/official University business. Please contact me ASAP by e-mail or phone. Written verification may be required.

Dishonesty: Any instance of dishonesty will be dealt with according to University policy.

Disabilities: We at West Chester University wish to make accommodations for persons with disabilities. Please make your needs known to me and to the Office of Educational Accessibility (3217). Sufficient notice is needed in order to make accommodations possible.

Withdrawal: 3rd week of March

Topics: We will follow the Neter et al. book Chapter 15-25, 27 then followed by Chapter 14 (optionally 13).

Homework Assignments

All Homework will be assigned at the end of each class and from the respective textbook. Homework is to be turned in the following class. Your summary must be complete and easily interpreted. The HW will focus on applications of the analytical methods discussed. We will be using real-life data.

Individual Projects

This project involves you:

  1. Collecting clustered/repeated data
  2. Properly analyzing the data
  3. Producing a written report
  4. Communicating the results through a presentation
More Comments

This class will be extremely fast paced. It is important that the students review the respective chapters in the text book prior to class. This class is 4 credits; therefore, the class may run over the 8:30 P.M. end time. Class time will be split in the Classroom and the computer lab. Do not fall behind in this course. This course coupled with the Categorical Data Analysis course will be extremely challenging, but mastery of these two topics will be tools you will use throughout your careers as statisticians.

Let’s have fun and get to work.


This class will proceed methodically. We will be doing analyses as we go. I encourage you to offer any comments on the structure/improvement of the course, especially as we do analyses. As we will find, there are many ways to “properly” do an analysis, but there is only one way to report it (CLEARLY and CONCISELY).

The material presented here is from a collection of sources. Some from the web, textbook, or hard-copy handouts. The collection of these materials I hope bring the material in a complete and concise format.