Longitudinal Data Analysis Using SAS

A 2-Day Seminar on Regression Analysis for Panel Data
Taught by Paul D. Allison, Ph.D. 

Read reviews of this seminar

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.

But 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 seminar 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 seminar with ample opportunity to practice the methods with real data sets.

Who should attend? 

If you need to analyze longitudinal data and have a basic statistical background, this seminar 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.


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 $895.00 includes all seminar materials. 

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 $142 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 STA109. The room block will expire when it is full or on September 9, 2014. There is a shortage of hotel rooms in Philadelphia for these dates so please make your reservation as early as possible.


This seminar will use SAS for the many empirical examples and the exercises.  However, lecture notes and exercises using Stata are also available on request. At least one hour each day will be devoted to exercises. To optimally benefit, you should bring your own laptop with a recent version of SAS (or Stata) installed. Power outlets will be provided at each seat.

Seminar outline

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


“I found this course to be extremely helpful for me in dealing with the statistical method applications in the general health line research. I would recommend this course to others. This is the first time that I took a class from Statistical Horizons and I did learn a lot.”
  Mei Lu, HFHS

“Materials are very concise and at the same time very informative and self explanatory. Non stat background professionals can also follow this course without great difficulty.”
  Prashanth V Motupalli, Lands End 

“This is a very useful course for researchers in health and social sciences who need to analyze longitudinal data.”
  Jose Tapia, Drexel University 

“Thank you Paul. The course is excellent and helpful. I learned a lot.”
  Zhaohui Wang, BioMedEcon 

“Excellent! It is very helpful to cope with the real-world data in practice.”
  Jinma Ren, University of Illinois College of Medicine