Structural Equation Modeling

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Date/Time
Date(s) - 07/11/2016 - 07/15/2016
All Day

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Start Date: 7/11/2016

End Date: 7/15/2016

Location:

The Gleacher Center at The University of Chicago Booth School of Business

450 North Cityfront Plaza Drive

Chicago

Website: http://statisticalhorizons.com/seminars/public-seminars/sem5day2016

Contact: 610-642-1941

Taught by Paul D. Allison, Ph.D. 

Structural Equation Modeling (SEM) is a statistical methodology that is widely used by researchers in the social, behavioral and educational sciences.  First introduced in the 1970s, SEM is a marriage of psychometrics and econometrics. On the psychometric side, SEM allows for latent variables with multiple indicators. On the econometric side, SEM allows for multiple equations, possibly with feedback loops. In today’s SEM software, the models are so general that they encompass most of the statistical methods that are currently used in the social and behavioral sciences.

This 5-day seminar assumes no previous knowledge of SEM, and covers almost a full semester’s worth of material. It is designed to make you a knowledgeable, effective and confident user of methods for structural equation modeling. Each day will include 1 to 2 hours of supervised, practical exercises that will help you achieve mastery of these methods.

Here Are a Few Things You Can Do With Structural Equation Modeling

  • Test complex causal theories with multiple pathways.
    • Estimate simultaneous equations with reciprocal effects.
    • Incorporate latent variables with multiple indicators.
    • Investigate mediation and moderation in a systematic way.
    • Handle missing data by maximum likelihood (better than
    multiple imputation).
    • Analyze longitudinal data.
    • Estimate fixed and random effects models in a comprehensive framework.
    • Adjust for measurement error in predictor variables.

How This Seminar Differs From Paul Allison’s 2-Day seminar “Introduction To Structural Equation Modeling”

This course includes all the material in the 2-day seminar, but in more detail, especially regarding models for categorical data. It also covers many other topics such as missing data, bootstrapping, formative indicators, interactions, and longitudinal data analysis. Lastly, much more time is devoted to exercises.

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