Longitudinal Data Analysis Using Stata
A 3-Day Remote Seminar Taught by
Paul Allison, Ph.D.
To see a sample of the course materials, click here.
For many years, Dr. Paul Allison has been teaching his acclaimed two-day seminar on Longitudinal Data Analysis Using Stata to audiences around the world. This course covers several popular methods for the analysis of longitudinal data with repeated measures: robust standard errors, generalized least squares, generalized estimating equations, random effects models and fixed effects models.
Starting January 28, 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 session will be held Thursday and Friday afternoons as an “office hour”, 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.
MORE DETAILS ABOUT THE COURSE CONTENT
PANEL DATA OFFER MAJOR OPPORTUNITIES AND SERIOUS PITFALLS
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. Such data have two major attractions: the ability to control for unobservables, and the determination of causal ordering.
However, there is also a major difficulty with panel data: repeated observations are typically correlated, and this invalidates the usual assumption that observations are independent. As a result, confidence intervals and p-values can be severely biased. In some cases, coefficients may also be biased downward.
You’ll learn how to use these methods (robust standard errors, generalized estimating equations, random effects models and fixed effects models) for quantitative outcomes, categorical outcomes, and count data outcomes. You’ll also learn which methods are best suited for different kinds of applications.
This is a hands-on seminar with ample opportunities to practice these new methods.
Here are a few of the topics you won’t want to miss:
- How to use panel data to control for unobserved variables.
- Why fixed effects methods often give very different results from random effects methods.
- How to reshape data from long form to wide form and back again.
- Why the default correlation structure for GEE is usually not the best.
- The difference between maximum likelihood and restricted maximum likelihood.
- How to estimate and interpret random coefficient models.
- Why first-order autoregressive structures are usually unsatisfactory.
- The difference between subject-specific coefficients and population-averaged coefficients, and why it matters.
- How to do longitudinal analysis using ordered logit or multinomial logit.
In this seminar, we will use the following Stata commands: reg, reshape, xtreg, areg, mixed, xtset, xtgee, logit, xtlogit, clogit, melogit, meologit, nbreg, menbreg, lrtest, margins, marginsplot, hausman, xthybrid, and xtdpdml. Lecture notes using SAS and R are available on request from registered participants.
This seminar will use Stata for the many empirical examples and exercises. However, no previous experience with Stata is assumed. Lecture notes and exercises using SAS and R are also available on request. To participate in the hands-on exercises, you are strongly encouraged to use a computer with Stata installed (release 13 or higher; IC, SE, or MP versions are all acceptable).
WHO SHOULD Register?
If you need to analyze longitudinal data and have a basic statistical background, this course is for you. You should have a good working knowledge of the principles and practice of multiple regression, as well as elementary statistical inference. It is also helpful to have some familiarity with logistic regression. But you do not need to know matrix algebra, calculus, or likelihood theory.
1. Opportunities and challenges of panel data.
a. Basic data structure and notation
b. Why do we want panel data?
c. Problem of dependence
d. Software considerations
2. Linear models
a. Robust standard errors
b. Generalized least squares
c. Random effects models
d. Fixed effects models
e. Between-within (hybrid) models
3. Logistic regression models
a. Robust standard errors
b. Generalized estimating equations
c. Subject-specific vs. population averaged methods
d. Random effects models
e. Fixed effects models
f. Between-within (hybrid) models
4. Methods for count data
a. Poisson and negative binomial models.
b. Robust standard errors.
d. Random effects
e. Fixed Effects
f. Between-within (hybrid) models
5. Linear structural equation models
a. Fixed and random effects in the SEM framework
b. xtdpdml command
c. Models for reciprocal causation with lagged effects
“This course on Longitudinal Data Analysis using Stata is very helpful for professionals with a background in basic statistics who want to do complex procedures using panel data. Understanding the different methods that can be used for different types of data and research questions has been useful. The instructor Dr. Allison teaches this course in a very organized fashion, provides plenty of opportunity to ask questions, and gives useful feedback.”
Shibani Kulkarni, Oak Ridge Institute for Science and Education
“This is my second time attending Dr. Allison’s workshops and I immensely enjoyed the learning experience. He makes it fun and simplifies the equations so that someone without a background in statistics can also understand the concepts, not just learn the codes.”
Shweta Gore, Massachusetts General Hospital Institute of Health Professions
“Paul is a very nice lecturer. After this course, I have a better understanding of panel data. I’m more confident to choose different models under different situations. Concept is much clearer than before. I believe that I’ll benefit a lot from this course in my future work as I need to handle quite a number of projects with panel data.”
Huihua Li, Singapore Health Services
“A great synthesis. I love how we cover a wide range of models and how the course is tailored to the software I’m using. Paul is a great instructor and takes the time to answer your questions.”
Claire Tugault-Lafleur, University of British Columbia
“Dr. Paul Allison is very knowledgeable in performing longitudinal data analysis on panel data. His examples and the workshop exercises are relatable and I can easily transfer the techniques to my own work.”
Grace Perez, University of Calgary
“Excellent class, Dr. Allison! I really enjoyed everything in this workshop. I learned so much in two days. I’ve used many methods in this workshop in my past work but it was never clear to me the backend assumption or pros and cons with various methods. Dr. Allison is very helpful and gives us clear guidance and very practical answers during the workshop. It significantly improved my understanding of the topic. A big thank you for your excellent lectures, Dr. Allison.”
Pengcheng (Phil) Zhu, University of San Diego
“Great overview of longitudinal analysis using Stata. I liked the examples and interpretations of each output given by Paul. He answered all questions without delay and tried his best to give the most accurate answer possible.”
Soyeon Kim, Waypoint Centre for Mental Health Care
“As a Ph.D. student, I was not sure if some of the material would be way over my head. However, Dr. Allison was able to teach the course materials very effectively in lay language that everyone can understand. I now feel very confident to take what I learned from this course and apply it to answering some of my research questions more efficiently and with rigor.”
Codie Primeau, Western University
“This course was an excellent overview of a large number of methods for analyzing longitudinal data. I feel that I have the skills necessary not only to analyze data longitudinally in a number of different ways but also to have much more depth in my understanding of potential issues that may arise while doing this type of analysis. This course provides both depth and breadth.”
Erin Grinshteyn, University of San Francisco
“Found the course to be easily approachable and tremendously flexible. I’m leaving here with a lot of new tools in my stats toolbox.”
Rick Trinkner, Arizona State University
“Excellent course, very helpful and professional. The information provided, materials, and explanations are excellent. It really made a difference in my knowledge about longitudinal data analysis.”
Pura Rodriguez de la Vega, Florida International University
“A great course with everything there is to know about longitudinal data analysis.”
Silvia Li, McMaster University