Longitudinal Data Analysis Using R

A 2-Day 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 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.

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. Although the course will be taught in R, complete Stata and SAS syntax are available upon 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.

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 course materials.

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 $139 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 STH728 or click here. For guaranteed rate and availability, you must reserve your room no later than Friday, June 28, 2019.

If you need to make reservations after the cut-off date, you may call Club Quarters directly and ask for the “Statistical Horizons” rate (do not use the code or mention a room block) 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


“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

“Stephen Vaisey makes longitudinal data analysis a lot of fun! This course is both engaging and informative.”
  Nathan O’Hara, University of Maryland

“It was a great course. A great instructor. Enjoyed taking it. Learned a lot.”
  Ananda Manage, Sam Houston State University

“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