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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 3-Day Livestream Seminar Taught by Chris Johnston, Ph.D.

Structural Equation Modeling (SEM) is a general framework for statistical analysis. What distinguishes SEM is that it allows for multiple equations that include latent variables measured with multiple indicators. This allows the researcher to adjust for measurement error and examine the properties of instruments, while simultaneously estimating structural relationships among variables, including direct and indirect effects. This course will provide an intensive introduction to the fundamentals of SEM, as well as several intermediate and advanced topics. Participants will receive guidance on how to implement SEM in widely used software packages.

This seminar provides an intensive introduction to the fundamentals of Structural Equation Modeling (SEM). The course will be roughly divided into four parts.

In Part 1, we will begin with a brief introduction to the SEM framework and what distinguishes SEM from other approaches to statistical analysis. We will then consider maximum likelihood estimation of the simple linear regression model within an SEM framework with both complete and missing data.

In Part 2, we will consider models with multiple, simultaneous equations. We will first explore simple path analysis, including the estimation of direct and indirect effects. We will then consider issues of identification that arise with more complex models, such as those with reciprocal effects.

In Part 3, we will turn to issues of measurement. We will begin by considering the concepts of reliability and validity and the advantages of multiple indicators when measuring a latent construct (e.g., capacity to specify and attenuate measurement error). We will then consider confirmatory factor analysis (CFA), including estimation, interpretation, and assessment.

In Part 4, we will explore a few advanced topics. First, we will integrate the measurement models of Part 3 with the structural models of Part 2 within a general SEM approach. We will then consider alternative methods of estimating these models beyond maximum likelihood. Finally, we will look at estimation with categorical observed variables.

Here are some of the things you will be able to do by the end of this course:

• Handle missing data using full information maximum likelihood.
• Investigate mediation in SEM models.
• Investigate the properties of multi-item scales, with confirmatory factor analyses (CFA).
• Incorporate latent variables into your regression models.
• Use different SEM estimation methods to address continuous and categorical observed variables.