Longitudinal Data Analysis Using R

A 2-Day Seminar Taught by Stephen Vaisey, Ph.D.

Read reviews from other seminars taught by Stephen Vaisey

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. 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. Generalized estimating equations
  3. Random effects models
  4. Fixed effects models

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.

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


To participate in the hands-on exercises, you are strongly encouraged to bring a laptop computer. The vast majority of what you will learn in this course can be applied in any software package. This seminar will mostly use R for empirical examples and exercises. To replicate the instructor’s workflow in the course, you are strongly encouraged to come with R and RStudio already installed on your computer. However, no previous experience with R is needed because all code will be provided.

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.

LOCAtion, Format, And Materials 

The class will meet from 9 am to 5 pm each day with a 1-hour lunch break at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103. 

Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking. 

Registration and lodging

The fee of $995.00 includes all seminar materials. The early registration fee of $895 is available until July 2.

Refund Policy

If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50). 

Lodging Reservation Instructions 

A block of guest rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $137 per night. This location is about a 5 minute walk to the seminar location. In order to make reservations, call 203-905-2100 during business hours and identify yourself by using group code STA729 or click here. For guaranteed rate and availability, you must reserve your room no later than Friday, June 29, 2018. 

If you make reservations after the cut-off date, ask for the Statistical Horizons room rate (do not use the code) and they will try to accommodate your request.


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


“Dr. Vaisey is a great teacher who is able to impart a significant amount of insight and understanding in a short period of time. Great energy and enthusiasm, very clear. Thanks so much. What I’ve learned is very valuable.”
  Kenneth Coburn, Healthy Quality Partners

“The instructor had excellent mastery of the topic and yet was able to translate his knowledge with great clarity to those new to the concepts. I appreciated his consistent employment of real-world examples to help solidify my understanding of a technique’s applications.”
  Emily Hawks, Adobe Systems

“Stephen Vaisey is a remarkable instructor. His command of the subject is outstanding and his ability to communicate the course content is impressive. He uses numerous examples and takes various approaches to explain concepts through the seminar. Such intense introductions have a tendency to feel long and tiring, so I was pleasantly surprised to find that this seminar was often fun and surprisingly engaging!”
  Andrew Dierkes, University of Pennsylvania

“Steve is the professor I wish I’d had in graduate school. He is a black belt at theory and technical details, and has the ability to communicate the materials in a way that helps you to grow an intuition. This is a rare quality in a statistician and teacher, and Steve nails it. He exhibits humor, thoughtful questions and responses, and the ability to anticipate where people get “stuck.” Take a course from Steve and you’ll be glad you did it!”
  Andy Bogart, RAND Corporation

“Stephen did an excellent job making difficult concepts easy to understand through examples and clear explanations. I learned how to better interpret, compare, and create practical models, all of which apply to many research projects with which I am involved.”
  Scott Friedlander, Los Angeles Biomedical Research Institute