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Longitudinal Data Analysis Using SEM

Event Details

Longitudinal Data Analysis Using SEM

Time: January 23, 2015 at 9am to January 24, 2015 at 5pm
Location: Courtyard Fort Myers at Gulf Coast Town Center
Street: 10050 Gulf Center Drive
City/Town: Fort Myers, Florida
Website or Map: http://www.statisticalhorizon…
Phone: 610-642-1941
Event Type: training, seminar
Organized By: Statistical Horizons LLC
Latest Activity: Dec 4, 2014

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Event Description

Taught by Paul Allison, Ph.D.  

For the past eight years, Professor Paul Allison has been teaching his acclaimed two-day seminars on Longitudinal Data Analysis Using SAS and Longitudinal Data Analysis Using Stata. In this new seminar—offered for the first time in January 2015—he takes up where those courses leave off, with methods for analyzing panel data using software for structural equation modeling (SEM).

Panel data have two big attractions for making causal inferences with non-experimental data:

  • The ability to control for unobserved, time-invariant confounders.
  • The ability to determine the direction of causal relationships.

Fixed effects methods that control for unobserved confounders are now well known and widely used. Cross-lagged panel models have long been used to study the direction of causality. But trying to combine these two approaches poses serious problems.

To deal with these difficulties, econometricians have developed the Arellano-Bond methods for estimating dynamic panel data models. In this seminar, we’ll explain the Arellano-Bond method and its close relatives, and we’ll see how to implement them in both SAS and Stata.

However, Arellano-Bond is known to be statistically inefficient and can also be severely biased in important situations. By contrast, Professor Allison has recently shown that dynamic panel models can easily be estimated by maximum likelihood with SEM software (ML-SEM). His recent work demonstrates that the performance of ML-SEM is vastly superior to the econometric methods: In typical situations, the use of Arellano-Bond instead of ML-SEM is equivalent to throwing away half the data. Plus, ML-SEM offers superior capabilities for handling missing data, evaluating model fit, relaxing constraints, and allowing for non-normal data.

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