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On-Demand Seminar

Longitudinal Data Analysis Using SAS

An On-Demand Seminar Taught by

Paul Allison
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For many years, Dr. Paul Allison has been teaching his acclaimed two-day seminar on Longitudinal Data Analysis Using SAS 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.

The course takes place in a series of four weekly installments of videos, quizzes, readings, and assignments, and requires about 4-6 hours/week. You can participate at your own convenience; there are no set times when you are required to be online. The course can be accessed with any recent web browser on almost any platform, including iPhone, iPad, and Android devices. It consists of 10 video modules:

  1. Advantages and disadvantages of panel data
  2. Robust standard errors and generalized least squares for linear models
  3. Random effects (or mixed) linear models
  4. Fixed effects linear models
  5. The between-within method
  6. Logistic regression with robust standard errors and GEE
  7. Logistic regression with random effects
  8. Fixed effects logistic regression
  9. Models and methods for count data
  10. Linear structural equation models

Each module is followed by a short multiple-choice quiz to test your knowledge. There are also weekly exercises that ask you to apply what you’ve learned to a real data set.

Each week, there are assigned articles to read. There is also an online discussion forum where you can post questions or comments about any aspect of the course. All questions will be promptly answered by Dr. Allison.

Downloadable course materials include the following pdf files:

  • All slides displayed in the videos.
  • Exercises for each week.
  • Readings for each week.
  • Computer code for all exercises (in SAS, Stata, and R formats).
  • A certificate of completion.

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"I am walking away from this course with the ability to implement longitudinal analysis..."

“Longitudinal Data Analysis Using SAS is an excellent and well-structured course. Paul guides participants through the theory, implementation, and interpreting of various longitudinal models in a way that facilitates deep understanding. I am walking away from this course with the ability to implement longitudinal analysis in many different settings and situations.”

Kim Greco

Boston Children’s Hospital / Harvard University

"... I was able to better understand the general concepts inherent to analysis of panel data."

“I found it very useful to iterate through analysis techniques for linear models, then logistic models, then count data models, and finally linear structural equation models. This allowed me to clearly see the differences between each type of response variable. More importantly, I was able to better understand the general concepts inherent to analysis of panel data. Each pass yielded a clearer overall picture.”

Laura Berbert

Boston Children’s Hospital

"... I feel more confident in using these statistical methods."

“This course is a refresher course for me. I took a course on longitudinal data analysis about ten years ago. I am happy to learn some new methods such as BW. The concepts are much clearer to me – thanks to your clear explanation. The lecture materials and classroom examples and exercises really help me understand the key concepts and I feel more confident in using these statistical methods. This course has also inspired me to read/learn more about longitudinal data analysis and I will read and learn more on my own.”

Howie Lim

University of Malaya

"Dr. Allison is an excellent instructor..."

“I am always hesitant to attend statistical workshops because I am not a statistician and I use statistics only for research data analysis. But I am glad that I came to this workshop. Dr. Allison is an excellent instructor and I am happy to say that I have learned a lot about statistical models for longitudinal data.”

Koushik Adhikari

University of Georgia