2017 Stata Winter School:

Longitudinal Data Analysis Using Stata

Taught by Paul Allison, Ph.D.
February 21-22, Hotel Birger Jarl Conference
Stockholm, Sweden 

Read reviews of this seminar 

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. 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. 


COMPUTING

This seminar will use Stata 14 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.  If you do not already have Stata installed, a temporary license will be provided free of change. A power outlet and wireless access will be available at each seat.


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. 


FORMAT AND MATERIALS

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

 Please go to the Metrika website for information on registration, and discounted hotel accommodations.


COURSE OUTLINE 

  1. Opportunities and challenges of panel data.
            a. Data requirements
            b. Control for unobservables
            c. Determining causal order
            e. 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 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 models
  4. Linear structural equation models
         a. Fixed and random effects in the SEM context
         b. Models for reciprocal causation with lagged effects

COMMENTS BY RECENT 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