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Analysis of Longitudinal Data

1st term
4 credits
Academic Year:
2022 - 2023
Instruction Method:
Synchronous Online
Class Times:
  • M W,  10:30 - 11:50am
Lab Times:
  • Wednesday,  9:00 - 10:20am
Auditors Allowed:
Yes, with instructor consent
Undergrads Allowed:
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructor:
Scott Zeger

140.621-624 or 140.651-654


Explores statistical models for drawing scientific inferences from longitudinal data. Includes topics: longitudinal study design; exploring longitudinal data; linear and generalized linear regression models for correlated data, including marginal, random effects, and transition models; and handling missing data. Intended for doctoral students in quantitative sciences.


Learning Objectives:

Upon successfully completing this course, students will be able to:

  1. Prepare graphical or tabular displays of longitudinal data that effectively communicate the patterns of scientific interest
  2. Implement and interpret a general linear model to make scientific inferences about the relationship between response and explanatory variables while accounting for the correlation among repeated responses for an individual
  3. Implement and interpret marginal, random effects, or transitional generalized linear models to make scientific inferences when the repeated observations are binary, counts, or non-Gaussian continuous observations
  4. Implement analysis of longitudinal data within SAS or STATA
Methods of Assessment:

This course is evaluated as follows:

  • 33% Homework
  • 33% Lab quizzes
  • 33% Take-home final exam

Instructor Consent:

No consent required

Special Comments:

Please note: This is the virtual/online section of a course that is also offered onsite. Students will need to commit to the modality for which they register. The Implementation and Interpretation of Analysis of Longitudinal Data (Wednesday 9:00 - 10:20) is highly recommended for all students. The course faculty request that all cell phones be silenced during class time out of respect for both the faculty and students. The lecture notes will be posted as powerpoint and pdf files. The course faculty feel use of laptops during class time is fine for taking electronic notes.