Longitudinal Data Analysis Using SAS
A 2-Day Seminar Taught by Paul D. Allison, Ph.D.
To see a sample of the course materials, click here.
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.
This course covers four methods for solving the problem of dependent observations: robust standard errors, generalized estimating equations, random effects models and fixed effects models. You’ll learn how to use these methods 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 SAS procedures: GLM, SURVEYREG, GENMOD, MIXED, LOGISTIC, SURVEYLOGISTIC, GLIMMIX, and CALIS.
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.
There is now a free version of SAS, called the SAS University Edition, that is available to anyone. It has everything needed to run the exercises in this course, and it will run on Windows, Mac or Linux computers. However, you do need a 64-bit machine with at least 1 GB of RAM. You also have to download and install virtualization software that is available free from third-party vendors. The SAS Studio interface runs in your browser, but you do not have to be connected to the Internet. The download and installation are a bit complicated, but well worth the time and effort.
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.
LOCATION, FORMAT, MATERIALS
The seminar meets Friday, May 5 and Saturday, May 6 at the Hilton Garden Inn, Marina Del Rey, 4200 Admiralty Way, Marina Del Rey, CA 90292. The class will meet from 9 to 5 each day with a 1-hour lunch break.
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 includes all course materials.
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 room block has been arranged at the Hilton Garden Inn, Marina Del Rey, 4200 Admiralty Way, Marina Del Rey, CA 90292. The special rate of $269 per night is available until Wednesday, April 5, 2017. You can make your reservation either online or by phone:
- Reserve a room online by clicking here and choosing your dates. Then click on “add special rate code” underneath the dates, and type SHLLC in the middle box that says “group code”.
- Reserve a room by phone by calling the Hilton Garden Inn at 1-310-301-2000 and mentioning the “Statistical Horizons” room block.
- Opportunities and challenges of panel data.
a. Data requirements
b. Benefits of panel data
c. Problem of dependence
d. Software considerations
- Linear models
a. Robust standard errors
b. Generalized estimating equations
c. Random effects models
d. Fixed effects models
e. Between-within models
- 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 models
- Models for count data
a. Poisson vs. negative binomial models
b. GEE and random effects
c. Fixed effects and between-within models
- Linear structural equation models
a. Fixed and random effects in the SEM context
b. Models for reciprocal causation with lagged effects
“Dr. Allison’s LDA using SAS course was incredibly comprehensive and well-organized. It appeared that even the most sophisticated statisticians in the room- in addition to early-career students, like myself- could benefit equally from the class content and materials. Perhaps the most attractive part of the course was Dr. Allison’s clarity and obvious interest in the various topics. I am appreciative to Statistical Horizons and its constituents for affording me the opportunity to learn these important techniques.”
Liza Rimsky, Long Island University- Brooklyn
“The presentation of the different topics is well ordered. The manual is very easy to follow. The way Dr. Allison speaks is very engaging and his encouragement to ask questions from the participants is refreshing.”
Annelyn O’Dwyer, Baylor Scott & White
“Just sign up for LDA if you want to learn about core concepts and knowledge of the statistical package to analyze longitudinal data. Dr. Paul Allison will clearly lead you to be familiar with this methodology.”
Lewis Lee, University of Pittsburgh
“I could follow this course even having no previous experience with SAS at all.”
Pedro Mario Pan Neto, Federal University of Sao Paulo
“I was worried that I couldn’t keep up, but Dr. Allison made this complex topic accessible to all. Dr. Allison was so willing to share his materials and expertise. It was all very helpful and applicable to my work. Thank you.”
Pauline Swiger, UAB-School of Nursing Office of Research & Scholarship