Event Phone: 1-610-715-0115

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 Rex B. Kline, Ph.D

Techniques of structural equation modeling (SEM) have become widely used in many disciplines, including psychology, education, medicine, management, epidemiology, and sociology. According to Google Scholar, there were more than 28,000 articles published in 2022 that included the phrase “structural equation model.”

Unfortunately, there are problems in many, if not most, published SEM studies. One common shortcoming is incomplete reporting about the results, especially about model fit – that is, whether the implications of the researcher’s model are consistent with the data. Incomplete reporting means that readers may be unable to adequately judge the trustworthiness or scientific merit of the results.

This introductory-level 3-day seminar (1) introduces basic concepts and techniques in SEM while (2) emphasizing best practices – that is, SEM done right. The main goal is to help you distinguish your own work in SEM by following best practices and avoiding common mistakes.

This seminar begins with the study of core SEM techniques, such as path analysis for estimating causal effects of observed variables and confirmatory factor analysis (CFA) for estimating causal effects of latent variables in measurement models.  These techniques are described with examples of applications using real data sets.

Complete and transparent assessment of model fit includes evaluation of both global and local model fit. Global fit concerns the overall or average correspondence between model and data, and local fit concerns the accuracy of predictions based on the model for pairs of measured variables. Local fit assessment can also be described as inspecting model residuals. In too many published SEM studies, little or no information about local fit is reported. This omission conflicts with well-established reporting standards for SEM studies.