Longitudinal Data Analysis Using R

A 4-Day Remote Seminar Taught by Stephen Vaisey, Ph.D.

Read reviews of this seminar

To see a sample of the course materials, click here.

This seminar is currently sold out. Email info@statisticalhorizons.com to be added to the waitlist.

The most common type of longitudinal data is panel data, consisting of measurements of predictor and response variables at two or more points in time for many individuals (or other units of observation). Panel data enable two major advances over cross-sectional data: 1) the ability to control for unobserved differences across units, and 2) the ability to investigate questions of causal ordering.

Because panel data violate the standard assumption of independent observations, researchers must choose a strategy to deal with (and, ideally, make use of) this non-independence. In this course we will cover four approaches:

  1. Robust standard errors
  2. Random effects models
  3. Fixed effects models
  4. “Between/within” models that combine fixed and random effects

We will cover each of these methods in some detail, considering their advantages and disadvantages. We will also consider different methods for quantitative and categorical outcomes.

Starting August 4, we are offering this seminar as a 4-day synchronous*, remote workshop for the first time. Each day will consist of a 3-hour, live morning lecture held via the free video-conferencing software Zoom. Participants are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if they are unable to attend at the scheduled time. Each lecture session will conclude with a hands-on exercise reviewing the content covered, to be completed on your own that afternoon. A final session will be held each evening as an “office hour”, where participants can review the exercise results with the instructor and ask any questions.

*We understand that scheduling is difficult during this unpredictable time. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session, meaning that you will get all of the class discussion and exercise solutions even if you cannot participate synchronously.

This is a hands-on course with opportunity for participants to practice the different methods using various R packages.


This remote seminar is held via Zoom, a free video conferencing application. Instructions for joining a session via Zoom are available here. Prior to each session, participants will receive an email with the meeting code you must use to join.  

The vast majority of what you will learn in this course can be applied in any software package. However, this seminar will mostly use R for empirical examples and exercises. To replicate the instructor’s workflow in the course, you should have R and RStudio already installed on your computer when the course begins. No previous experience with R is needed, however, because all necessary code will be provided. For those who prefer Stata or SAS, complete code for all analyses will be provided on request.

If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.

Who should attend? 

This course is for anyone who wants to learn to analyze repeated measures panel data. Participants should have a basic foundation in linear regression. Basic knowledge of R is useful, but not necessary.


1. Opportunities and challenges of panel data
2. Linear models
     a. Robust standard errors
     b. Generalized least squares with maximum likelihood
     c. Random effects models
     d. Fixed effects models
     e. Between-within models
3. Logistic regression models
     a. Robust standard errors
     b. Subject-specific vs. population averaged estimates
     c. Random effects models
     d. Fixed effects models
     e. Between-within models
4. Extensions to count data models
5. Introduction to structural equation models for panel data

RevieWs of Longitudinal Data Analysis Using R

“I’d recommend this course to developmental psychologists. The course helped me to refresh my memory and knowledge and also motivated me to use R. Dr. Vaisey did a great job explaining hard to grasp content with a basic language and he is fun.”
  Irem Korucu Kiroglu, Yale University

“Excellent course to take parallel to multilevel modeling to understand how similar concepts from cross sectional data can be applied to panel data. Solid refresher of the statistical approaches in updated software ‘R’.”
  Matthew Augustine, Icahn School of Medicine at Mount Sinai / James J. Peters VA Medical Center

“This course is relentlessly applied, giving students a very solid foundation in applying the material on longitudinal data analysis, although some key theoretical concepts are discussed. And the use of humor during the lectures helps greatly to retain the attention and focus of the students!”
  Terry Kissinger, Federal Deposit Insurance Corporation

“Steve was a great instructor. I came away from the class with the confidence to explain complicated methods in a simple way. I would recommend this course to anyone who communicates about these methods with their own students.”
  Jason Rydberg, University of Massachusetts, Lowell