Longitudinal Data Analysis Using Stata February 2020

Event Phone: 1-610-715-0115

We're sorry, but all tickets sales have ended because the event is expired.

There are no upcoming dates for this event.


Cancellation 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).
In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

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

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 investigation 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. As a result, confidence intervals and p-values can be severely biased. In some cases, coefficients may also be biased downward.

This course covers four methods for solving the problem of dependent observations: robust standard errors, generalized estimating equations, random effects models and fixed effects models. You’ll learn how to use these methods for quantitative outcomes, categorical outcomes, and count data outcomes. You’ll also learn which methods are best suited for different kinds of applications.

This is a hands-on seminar with ample opportunities to practice these new methods.

Here are a few of the topics you won’t want to miss:

  • How to use panel data to control for unobserved variables.
  • Why fixed effects methods often give very different results from random effects methods.
  • How to reshape data from long form to wide form and back again.
  • Why the default correlation structure for GEE is usually not the best.
  • The difference between maximum likelihood and restricted maximum likelihood.
  • How to estimate and interpret random coefficient models.
  • Why first-order autoregressive structures are usually unsatisfactory.
  • The difference between subject-specific coefficients and population-averaged coefficients, and why it matters.
  • How to do longitudinal analysis using ordered logit or multinomial logit.

In this seminar, we will use the following Stata commands: reg, reshape, xtreg, areg, mixed, xtset, xtgee, logit, xtlogit, clogit, melogit, meologit, nbreg, menbreg, lrtest, margins, marginsplot, hausman, xthybrid, and xtdpdml. Lecture notes using SAS and R are available on request from registered participants.

Venue:  

Venue Phone: 619-831-0224

Venue Website:

Address:
900 Bayfront Court, San Diego, California, 92101, United States