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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 3-Day Livestream Seminar Taught by Roy Levy, Ph.D.

Bayesian approaches to data analysis offer a number of advantages over conventional approaches. In addition, a number of advanced methods and models may be seen as implicitly or explicitly Bayesian. This seminar describes Bayesian approaches, and their attending advantages, in several advanced modeling frameworks, including factor analysis, structural equation models, and multilevel models.

Building off an understanding of Bayesian normal distribution and regression modeling, this seminar will cover Bayesian confirmatory factor analysis in depth, including procedures for model evaluation and model comparison. Importantly, we will cover the components of factor analysis in detail because they will also serve as the components for the other advanced models we will cover, including structural equation modeling, multilevel modeling, and (time permitting) missing data modeling. The presentation of each of these topics is intended to illuminate broader ideas of Bayesian statistical modeling, such that key principles can be abstracted even for those researchers not working with the particular type of model at hand.

Familiarity with conventional approaches to factor analysis, structural equation models, multilevel models, and missing data model would be beneficial, but not required. Each will be reviewed from a conventional perspective before pursuing a Bayesian perspective. Although this material is necessarily complex, it will be presented in a manner targeting the applied researcher, with examples primarily from social science and educational research, accompanied by input and output from software. Examples will be accompanied by input and output from Stan and R. Throughout the course participants will be able to practice exercises using these software packages. Additional notes on the software packages used are given below.