Comments from recent participants
Participants in two courses were asked to rate the course on a 10-point scale. Of the 73 who responded, the mean rating was 8.7. They were also invited to write attributed comments about the course. Here are all the comments that were received:
"A very well organized course that is extremely useful for anyone interested in applying longitudinal data analysis methods in academic research."
Tatiana Manolova, Bentley University
"Paul Allison is the best in the business. After years of looking things up in his books, his longitudinal data analysis class exceeded my expectations. I am eager to put my new skills to work."
Emily Parker, Health Partners Research Foundation
"This was one of the best stats courses I've every taken, the other being Allison's Missing Data course. I understood concepts that I didn't grasp through my previous stats courses. The information is very clearly explained, and practical examples used to illustrate concepts. The rationale behind why things are done in a certain way is clearly explained."
Margaret Hsieh, University of Pittsburgh Medical Center
"The literature on repeated measures and longitudinal data is immense because there are numerous models which can be used to analyze the data. Dr. Allison's Longitudinal Data Analysis course provides careful guidance on these models, the differences between them, and the inferences that can be made from the results. It provides the student with confidence to use the models and forms a basis for additional reading."
Robert Goldberg-Alberts, Wyeth
"A very hands-on course taught by a very knowledgeable, kind and helpful professor. Highly recommended."
Enya He, University of North Texas
"Thorough, clearly presented treatment of a complex topic."
Palmer Bessey, Weil Cornell Medical College
"I had taught myself some longitudinal methods, such as GEE. What I lacked was a good understanding of the relative pros and cons of each model. Dr. Allison's structured and iterative discussion of robust standard errors, GEE, random effects, fixed effects and hybrid methods helped to describe each method in comparison with the others. This will help me in making model fit decisions in my research."
Matt Epperson, Rutgers University
"This a great course for those with some exposure to correlated data, but are unsure how to best work with their data. Very useful combination of statistical theory and hand-on application of SAS programming."
Kate Bauer, University of Minnesota
"This is a good course to stretch your statistical knowledge. I will definitely recommend it as a challenge to those looking for a refresher course on longitudinal analysis."
Elizabeth Vasquez, Helen Hayes Hospital
"Dr. Allison's deep understanding of this topic and his ability to teach it is outstanding. This course was a great opportunity to learn from the #1 expert in longitudinal data analysis for panel data."
Michelle Lalonde, University of Toronto
"This is a very good training course for panel/longitudinal data analysis, which only takes two days to learn both linear and non-linear analysis. Very good course arrangement and coding examples. Highly recommended!"
Jianjun Zhang, West Virginia University
"Professor Allison is an excellent teacher! I learned a lot from this class. I would highly recommend this class to anyone who does longitudinal data analysis."
Chang Liu, Merck
"Very helpful class! I would recommend it to anybody trying to understand various methods for analyzing longitudinal data."
Janet Grubber, Duke University Medical Center