2014 Stata Summer School:

Longitudinal Data Analysis Using Stata

Taught by Paul Allison, Ph.D.
August 12-13, Hotel Birger Jarl Conference
Stockholm, Sweden 

Read reviews of this seminar. 


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 is a hands-on course with ample opportunity for participants to practice the different methods. 


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. 


This seminar will use Stata for all the examples. Although not required, participants are strongly encouraged to bring their own laptop computers with a recent version of Stata installed.  If you do not currently have Stata, we can provide a temporary license for Stata 13 which you can download and install before coming to the course. Power outlets will be available at each seat.


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.


Please go to the Metrika website for information on registration, and discounted hotel accommodations.


1. Opportunities and challenges of panel data.
    a. Data requirements
    b. Control for unobservables
    c. Determining causal order
    d. Problem of dependence
    e. Software considerations

2. Linear models
   a. Robust standard errors
   b. Generalized estimating equations
   c. Random effects models
   d. Fixed effects models
   e. 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. Hybrid models

4. Count data models
   a. Poisson models
   b. Negative binomial models
   c. Fixed and random effects 

5. Linear structural equation models
   a. Fixed and random effects in the SEM context
   b. Models for reciprocal causation with lagged effects


“Excellent course that covered a terrific review of cross sectional time series and panel data analytics. I recommend this course to anyone who does program monitoring & evaluations using longitudinal data.”
  Felix J. Bradbury, Accenture

“This was an extremely useful course. Course notes and examples were clear and Dr. Allison readily answered questions that enhanced my understanding of the material. It was an excellent course.”
  Holly Foster, Texas A&M University

“Dr. Allison teaches in a very clear manner. I have been trying to figure out fixed and random effects for a while, and now I have, thanks to Dr. Allison.  I like that the course gives examples of exact syntax, corresponding outputs, and interpretations. The printed lecture notes are invaluable.
  Heili Pals, Texas A&M University

“This course, with its excellent examples and clear explanations, helped cement concepts for me that I’d been exposed to previously but didn’t fully understand. It also taught me new methods like mixed models and SEM. I highly recommend the course and Dr. Allison.”
  Tia Palermo, Stony Brook University (SUNY)

“Excellent hands-on experience in longitudinal data analysis for those who work with data and also for those who may not work directly with data but work with others who do. So, if you are a senior faculty member working with younger colleagues or with graduate students and want to know enough about longitudinal data to get by, this course is for you. Job well done!!!”
  Ather Akbari, Saint Mary’s University, Nova Scotia

“The explanation on the use of statistical software is wonderful. The lecturer accepts questions from students many times during the lecture. This lecture is worth the price!”
  Hiroshi Yokomichi, University of Yamanashi, Japan