Longitudinal Data Analysis
A 2-Day Course on Regression Analysis for Panel Data
Taught by Paul D. Allison, Ph.D.
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. There are four widely available methods for dealing with dependence: robust standard errors, generalized estimating equations, random effects models and fixed effects models. This course examines each of these methods in some detail, with an eye to discerning their relative advantages and disadvantages. Different methods are considered for quantitative outcomes, categorical outcomes, and count data outcomes.
This course is based in part on Paul Allison’s Fixed Effects Regression Models, published by Sage in 2009.
Who should attend?
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. And it is also helpful to have some familiarity with logistic regression. But you do not need to know matrix algebra, calculus, or likelihood theory.
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
Participants must make their own arrangements for lodging and meals. A block of rooms have been reserved at the seminar location: Club Quarters Central Loop. For more information, click on the Lodging tab at the top of this page.
This course will use Stata for the many empirical examples, but lecture notes using SAS are also available to course participants. No computers will be provided on site and there will be no supervised exercises. However, you are welcome to bring your own laptop and perform the distributed exercises on your own time.
Of the 34 students who completed evaluation forms last October, 50% rated this course "excellent" and another 35% rated it "very good." They were also invited to write comments about the course. Here are all the comments that were received:
“Dr. Allison is a gifted teacher and I very much enjoyed this course. Specifically, Dr. Allison understands that most students at a workshop of this nature are more interested in knowing how to DO things rather than arcane technical details. Yet, he does not neglect the conceptual part necessary to understand which models to choose. That balance, and Dr. Allison’s willingness to engage his students, are among the most attractive features of this workshop. I highly recommend it. “
Rohit Pradhan, University of Alabama
“Just like Dr. Allison’s books, the course is very user-friendly, easy to understand and apply in practical work. I would definitely recommend this course just as I’ve been doing with his books. He makes advanced statistical models seem less intimidating.”
Nancy Chau, Centre for Addiction and Mental Health
“In this course I could improve my knowledge on Panel Data Modeling. I am sure that will be very helpful in my econometric courses and empirical research.”
“Professor Allison has an efficient, hands-on approach to teach his statistical class. I left with a clear understanding of what to use on my dataset, which will clearly help the quality of my dissertation. Expect to go back home and have the first set of results ready in one week."
Razvan Lungeanu, Northwestern University
“Since my NLH grant award, I have been receiving your emails announcing numerous statistical courses, yet I haven’t given a thought to attend because the tuition is quite expensive, and I was not sure how much you can learn within the very short time period. Just after attending the very first lecture, I found that it is a worthwhile investment. Taking this course is far greater than going over manual or book for several months. I learned far more from your lecture than studying myself with the book for the past whole year.”
Sun Kim, UMass Medical School
“The Longitudinal Data Analysis Using Stata workshop was an exceptional, compacted version of my graduate statistics courses. The workshop helped clarify many important distinctions between statistical models. After completing the course, I feel more confident than ever in my ability to complete my dissertation analysis and defend my statistical models. I highly recommend the course to any graduate student suffering from data-related road blocks in the dissertation process.”
Sophia Lyn Nathenson, University of Utah
“This course provided a thorough overview of longitudinal analysis methods with clear examples for application. I would recommend it to anyone interested in learning, reviewing or practicing these methods.”
Helena Duch, Columbia University
“This course is great because it brings together the conceptual logic of panel models with practical implementation in Stata. I better understand the rationale for using different models and feel confident to actually do them in Stata.”
“I’ve read several texts on panel models but the class vastly improved my insight into the methods.”
Bob Kaminski, University of South Carolina
“This course is very helpful for understanding the logic of conducting panel data analysis and how to use Stata to perform the analysis.”
Ce Shen, Boston College
“I attended a seminar of Paul Allison’s during my PhD. This was about Survival Analysis. Some years later, facing some issues with panel data sets, I did not hesitate to come back to attend a new seminar on Longitudinal Data Analysis. I knew that Paul would cover all the methods I would need to run my future research. It is really efficient. This is an excellent first step to becoming familiar with these methods that are not easy to manage by only reading books. Great course!"
Rozenn Perrigot, University of Rennes 1 and ESC Rennes School of Business
“This was an excellent course. The material was comprehensive and the presentation was clear.”
Farin Kamangar, Morgan State University
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