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Longitudinal Data Analysis Using R - Online Course

A 3-Day Livestream Seminar Taught by

Stephen Vaisey
Course Dates: Ask about upcoming dates
Schedule: All sessions are held live via Zoom. All times are ET (New York time).

10:00am-12:30pm (convert to your local time) Wednesday, Friday-Saturday
1:30pm-4:00pm Wednesday, 1:30pm-3:30pm Friday & Saturday

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The most common type of longitudinal data is panel data or repeated measures data, consisting of measurements of predictor and response variables at two or more points in time for many individuals (or other units). Panel data enable two major advances over cross-sectional data:

    1. the ability to model the evolution of outcomes over time; and
    2. the ability to “control” for unobserved unit-specific heterogeneity, enabling better causal inferences.

Different data structures allow researchers to use panel data in different ways. In this course, we will focus on the following approaches:

    1. Mixed models (including latent growth curves)
    2. Two period difference-in-differences
    3. Fixed-effects models (one-way and two-way)
    4. Between-within models
    5. Dynamic panel models

In addition to considering these approaches and their implementation in R, we will discuss when each is (not) suitable given data constraints. We will also consider how to adapt these approaches to deal with limited dependent variables (especially binary outcomes).

Starting October 19, we are offering this seminar as a 3-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. 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.

*We understand that finding time to participate in livestream courses can be difficult. 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 four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously. 

Closed captioning is available for all live and recorded sessions.

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


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"Tremendous appreciation to the instructor for sharing truly excellent course materials.”

The lectures were excellent, but for me the most useful take-away is the additional course materials.  Working R scripts for each of the methods presented, with sample exercises, are critical for short-course students to help reinforce understanding give us the ability to tinker a bit to understand how the code works and why it does what it does.  Too often, statistical courses show students a haze of formulae and symbols but no working examples of how to actually execute the analysis in practice, often leaving them no better off than before taking the course.  Tremendous appreciation to the instructor for sharing truly excellent course materials. 

Andrew Althouse

University of Pittsburgh

“Stephen Vaisey's teaching style is excellent..."

“Stephen Vaisey’s teaching style is excellent — practical, humorous, relatable, willing to answer questions, and willing to slow down when necessary. It’s also awesome that you provided all of your code so we can go back to replicate analyses when necessary. Great job!”

Lynne Knobloch-Fedders

Marquette University

"Incredibly intuitive approach to the content..."

“Incredibly intuitive approach to the content: I loved that the class was organized around different types of outcome variables and whether/how they changed over time. I appreciated that explanations of equations were presented alongside real-world examples. I liked being able to code along with the exercises in lab but also appreciated having access to the code so that if I missed something live, I could go back later. As someone who has only 1 year of stats methods experience under their belt, this course was incredibly accessible. I learned so much and feel more confident than I thought possible.” 


Madeline Smith-Johnson

Rice University

"...Stephen excels at translating complicated concepts into easy-to-follow examples."

“I originally took this course with Dr Vaisey several years ago when I was tasked with developing a doctoral-level course on longitudinal data analysis for my own program. The course was immensely helpful in expanding my knowledge and Stephen excels at translating complicated concepts into easy-to-follow examples. When Stephen said he had redeveloped and updated the materials, I knew it would be worthwhile to revisit the course. I am glad that I did.”


Jason Rydberg

University of Massachusetts Lowell

"I gained a new depth of understanding of these kinds of models..."

“Another outstanding statistics course by another outstanding instructor – Steve Vaisey – who made the material accessible and even fun along the way. I gained a new depth of understanding of these kinds of models even though I have been over them previously. I would not hesitate to sign up for another course from Dr. Vaisey. Stat Horizons courses are how I treat myself to gaining statistical knowledge every year. I’ve never been disappointed.”

James Swartz

University of Illinois at Chicago

“The instructor was super well-versed in the topic..."

“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

"I would recommend this course to anyone who is interested in analyzing longitudinal data analysis using R."

“I would recommend this course to anyone who is interested in analyzing longitudinal data analysis using R. Steve is knowledgeable, responsive, and articulate. The interactive learning environment is enjoyable.” 

Yan Guo

UMass Chan Medical School