Modeling Longitudinal and Panel Data

Dr. James Hardin and Dr. Joseph Hilbe

Aim of Course:

This course covers the extension of Generalized Linear Models (GLM) to model varieties of longitudinal and clustered data, called panel data. Specifically, the course treats generalized estimating equations (GEE), a population averaging method that models panel data in which the response is a member of the exponential family of distributions; e.g., continuous, binary, grouped, and count. GEE is one of several methods used to model panel data --- the most noted alternative being random effect models.
The course will discuss GEE theory, relevant correlation structures, and differences in both theory and application between population averaging GEE (PA-GEE) and random effects or subject specific panel models (SS-GEE).

Who Should Take This Course:

Social scientists, and medical and psychological researchers who need to analyze and model longitudinal or panel data.

For those enrolled in a Program of Advanced Statistical Studies, this is a required or elective course in the following Programs:

• Statistics for Social Sciences - elective
• Biostatistics (epidemiology) - elective
• Biostatistics (controlled trials) - elective

Course Program:

The course is structured as follows

The Instructor:

Dr. James Hardin is a Research Associate Professor at the University of South Carolina. Co-author (with Joseph Hilbe) of Generalized Estimating Equations, Dr. Hardin is on the editorial board of The Stata Journal and is the developer of the Stata GEE command, and with Dr Hilbe is developer of the GLM command.

Dr. Joseph Hilbe is Emeritus Professor at the University of Hawaii and Solar System Ambassador with NASA's Jet Propulsion Laboratory at California Institute of Technology. Since 1992 Prof. Hilbe has served as an Adjunct Professor of Statistics at Arizona State University. Professor Hilbe is currently on the editorial boards of seven academic journals in statistics, and from 1997-2009 was Software Reviews Editor for The American Statistician. Professor Hilbe is an elected Fellow of both the American Statistical Association and Royal Statistical Society, and is an elected member (Fellow) of the International Statistical Institute. An author of over one hundred journal articles, and numerous published statistical procedures and book chapters, Dr. Hilbe is author of Logistic Regression Models (2009, Chapman & Hall/CRC) and Negative Binomial Regression (2007, Cambridge University Press), and, with James Hardin, is author of Generalized Estimating Equations (2003, Chapman & Hall/CRC) and Generalized Linear Models and Extensions (2001, 2007, Stata Press). He is also co-author of the forthcoming books, R for Stata Users (Springer, with R. Muenchen), and Quasi-Least Squares Regression (Chapman & Hall/CRC, with J. Shults).

Organization of the Course:

The course takes place over the internet, at statistics.com. During each course week, you participate at times of your own choosing - there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor. The course is scheduled to take place over 4 weeks, and typically requires 15 hours per week. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials and work through exercises. Discussion among participants is encouraged. The instructor will provide answers and comments.

Certificates and Grades:

You may be interested only in learning the material presented, and not be concerned with grades or certificates. Or you may be enrolled in a statistics.com Program in Advanced Statistical Studies that requires demonstration of proficiency in the subject, in which case your work will be assessed for purposes of issuing a grade. Or you may require only a "Certificate of Course Completion," along with professional development credit in the form of Continuing Education Units (CEU's). As you begin the class, you will be asked to specify your category.

Credit:

This course offers continuing education units (CEU's). For those successfully completing the course (generally this means marks of 50% or better on the homework), 5.0 CEU's and a certificate will be issued by statistics.com, upon request.

Dates:

Jul. 2 - Jul. 30, 2010
Click here to be notified of future course offerings.

Participants gain access to the online materials on the first day of the course, and typically spend about 15 hours per week (at their convenience). You retain full access to course materials, including discussion board, for two weeks after the course closing date.

Level:

Advanced

Prerequisite:

Participants should be familiar with Generalized Linear Models. Those unfamiliar with this material should take the Generalized Linear Models course first.

Course Text:

James W. Hardin and Joseph M. Hilbe (2003), Generalized Estimating Equations, (not included in course price) available here. PLEASE ORDER YOUR COPY IN TIME FOR THE COURSE STARTING DATE.

Software:

In some lessons, you will benefit from being able to implement models in a software program that is able to do GEE (for example, Stata, SAS, S-PLUS, SPSS, R). Click Here for information on obtaining a free (or nominal cost) copy of various software packages for use during the course.

Registration:

Register Online - $469
Register Online (academic) - $369 (you must be affiliated with a college, university or high school)

Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment. Please use this printed registration form, for these and other special orders.

Consider registering for this course together with two other Modeling courses as part of our special 3 course package registration for tuition savings.

Note: Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date, unless you specify otherwise.

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