Longitudinal Data Analysis Using Stata

A 2-Day Seminar Taught by Paul Allison, Ph.D. 

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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. There are four widely available methods for dealing with dependence: robust standard errors, generalized estimating equations, random effects models and fixed effects models. This course 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 and categorical outcomes.

This is a hands-on course with ample opportunity for participants to practice the different methods. 


WHO SHOULD ATTEND? 

If you need to analyze longitudinal data and have a basic statistical background, this course is for you. You should have a good working knowledge of the principles and practice of multiple regression, as well as elementary statistical inference. And it is also helpful to have some familiarity with logistic regression. But you do not need to know matrix algebra, calculus, or likelihood theory. 


Computing

This seminar will use Stata for the many empirical examples and exercises. However, no previous experience with Stata is assumed. Lecture notes and exercises using SAS are also available on request. To participate in the hands-on exercises, you are strongly encouraged to bring a laptop computer with Stata installed (release 14; IC, SE, or MP versions are all acceptable). Stata 12 or 13 is OK, but earlier versions of Stata will lack some of the functionality demonstrated in the seminar. A power outlet and wireless access will be available at each seat.

Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s free 30-day evaluation offer or their 30-day software return policy. 


LOCAtion, Format, And Materials 

The course will meet Thursday, May 18 and Friday, May 19 at the Conference Chicago at University Center, 525 South State Street Chicago, IL 60605.

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 $995.00 includes all seminar materials. The early registration fee of $895 is available until April 18.

Refund Policy
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 block of guest rooms has been reserved at the The Congress Plaza Hotel, 520 S. Michigan Avenue, Chicago, IL 60605 at a special rate of $189 per night. This location is about a 5 minute walk to the seminar location. You can make a reservation online by clicking here.  Alternately, you can call 312-427-3800 x 5025 or toll free at 1-800-635-1666 during business hours and identify yourself as part of the Statistical Horizons room block. For guaranteed rate and availability, you must reserve your room no later than Monday, April 17, 2017.

Please book early, rooms fill up quickly. 

SEMINAR OUTLINE

1. Opportunities and challenges of panel data.
        a. Basic data structure and notation
        b. Why do we want panel data?
        c. 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. Between-within (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. Between-within (hybrid) models

4. Methods for count data
       a. Poisson and negative binomial models.
       b. Robust standard errors.
       c. GEE
       d. Random effects
       e. Fixed Effects
        f. Between-within (hybrid) models

5. Linear structural equation models
     a. Fixed and random effects in the SEM framework
     b. xtdpdml command
     c. Models for reciprocal causation with lagged effects


RECENT COMMENTS FROM PARTICIPANTS

“The course provided a great overview of the best models available for working with panel data, how to select the right models, and how to interpret results. I especially appreciated the organization, which divided the course between linear and non-linear models, and moved from basic to more complex models. The course struck the right balance for me between theory and application. The manual and lectures were clear and helpful.”
  James G. Squibb III, Implan Group

“If you have any questions about Longitudinal Data Analysis and are overwhelmed by the internet resources or worse still by all the contradictory citations out there, then sign up for this course. Paul Allison will break it all down for you and make it look easy.”
  Grace Namirembe, Tufts University

“I left the course feeling very confident in my ability to analyze longitudinal data and make informed decisions about appropriate methods. I really enjoyed this course and learned a tremendous amount.”
  Breanne Cave, Police Foundation

“This course is taught superbly in a clear and straightforward manner to provide maximum learning. Just wonderful!”
  Debbie Barrington, Georgetown University