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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 4-Day Remote Seminar Taught by Paul Allison, Ph.D.

Since 2015, hundreds of researchers have taken Paul Allison’s annual 5-day summer course on Structural Equation Modeling.  This summer we are doing things a little differently. The course has been divided into two parts, and each part will be taught remotely (via Zoom) over a four-day period.   Part 1 (July 7-10) covers the basics and is designed to get you up and running with SEM. This is an introductory course, and no previous knowledge of SEM is presumed.

Part 2 (July 14-17) covers more advanced topics, like instrumental variables, alternative estimation methods, multiple group models, models for binary and ordinal data, models for longitudinal data, and much more.  To take Part 2, you should already have some knowledge of SEM, ideally by taking Part 1.

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.

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

• Test the implications of causal theories.
• 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).
• Adjust for measurement error in predictor variables.
• Estimate and compare models across multiple groups of individuals.
• Represent causal theories with rigorous diagrams.
• Investigate the properties of multiple-item scales.

Because SEM is such a complex and wide-ranging methodology, learning how to use it can take a substantial investment of time and effort. Now, you have the opportunity to learn the basics of SEM from a master teacher, Professor Paul D. Allison, in just four days.