Longitudinal Data Analysis Using Structural Equation Modeling

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Date/Time
Date(s) - 02/25/2016 - 02/26/2016
All Day

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Start Date: 2/25/2016

End Date: 2/26/2016

Location:

Hotel Birger Jarl Conference

Tulegatan 8

Stockholm, Sweden

Website: http://statisticalhorizons.com/seminars/public-seminars/sweden-course-series-longitudinal-data-analysis-using-structural-equation-modeling

Contact: 610-642-1941

Taught by Paul Allison, Ph.D.

For the past eight years, Professor Paul Allison has been teaching his acclaimed two-day seminar “Longitudinal Data Analysis Using Stata”. In this new seminar he takes up where that course leaves off, with methods for analyzing panel data using structural equation modeling.

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. However, Arellano-Bond is known to be statistically inefficient and can also be severely biased in important situations.

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 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 better capabilities for handling missing data, evaluating model fit, relaxing constraints, and allowing for non-normal data.

This seminar takes a deep dive into the ML-SEM method for estimating dynamic panel models, exploring the ins and outs of assumptions, model specification, software programming, model evaluation and interpretation of results. Several real data sets are analyzed in great detail, testing out alternative methods and working toward an optimal solution. Both the –sem- and the –gsem- commands will be explored. In addition, Professor Allison will explain his new –xtdpdml- command which radically reduces the programming necessary to run the panel data models.

This is an applied course with a minimal number of formulas and a maximal number of examples. Although the methodology is cutting edge, the emphasis is on how to actually do the analysis in order accomplish your objectives.

At the end of this seminar, you should be able to confidently apply the ML-SEM method for dynamic panel data to your own research projects. You will also have a thorough understanding of the rationale, assumptions, and interpretation of these methods. Note: the methods covered in this course require panel data with at least three time points, and the number of individuals should be substantially larger than the number of time points.

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