Longitudinal Data Analysis Using R

A 3-Day Remote Seminar Taught by
Stephen Vaisey, Ph.D.

Read reviews of this seminar

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

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 (also known as repeated measures) 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 April 8, we are offering this seminar as a 3-day synchronous*, remote workshop. Each day will consist of a 4-hour live lecture held via the free video-conferencing software Zoom. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you 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. An additional lab session will be held Thursday and Friday afternoons, where you 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 and will be accessible for two weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.

This is a hands-on course with opportunity for you 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. Before the seminar begins, you will receive an email with the meeting code and password 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. You 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

“Stephen is a truly talented instructor. He can break down otherwise obscure statistical concepts and make them really easy to understand.”
  Laura Avila, The Hospital for Sick Children

“The instructor was super well versed in the topic. He gave tons of examples and practical tricks to compare models and visualize our data. The course was filled with humor and anecdotes. I certainly gained a better understanding of differences across modeling approaches and when they should be used. It allowed me to think of model selection with more confidence.”
  Mathieu Bélanger, Universite de Sherbrooke

“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