A highly interactive 5-day course gently introducing Bayesian estimation for linear regression analysis, factor analysis, mediation analysis with manifest and latent variables, and longitudinal growth models. The first four days are designed to teach participants on how to estimate the above models in the Bayesian framework, and day 5 is dedicated solely to finding prior information for participants’ data.
The popularity of Bayesian statistics has increased over the years, however, as of now Bayesian methods are not a part of the statistics curricula in most graduate programs internationally. The Bayesian estimation framework can handle some commonly encountered problems in classical statistics, such as the lack of power in small sample research and convergence issues in complex models. Furthermore, some researchers prefer the Bayesian framework because it provides a way of sequentially updating knowledge with new data instead of requiring that each new study tests the null hypothesis that there is no effect in the population.
During this course, students will be gently introduced to Bayesian statistics using class examples. The instructors will clarify the differences between the philosophies and interpretations in classical and Bayesian frameworks, and will illustrate types of research questions that can be answered only using Bayesian methods. This course will also give students experience with running Bayesian analyses and interpreting results, and will instruct participants on the prevailing “best practices” for Bayesian estimation in structural equation models. Participants will emerge from the course with knowledge about how to apply Bayesian methods to answer their research questions, and with the ability to understand articles that examine and apply Bayesian methods for structural equation modeling. We highly recommend bringing your own data as well; however, we have plenty of data available for participants to analyze. Using these examples, we will explore the benefits of Bayesian statistics and discuss what is needed to fit your first Bayesian structural equation model.
Among our Methodology and Statistics postgraduate courses, there are two other courses that address Bayesian statistics. The distinction between these three courses is as follows:
-Theory-Based Hypothesis Evaluation Using the p-value, Bayes factor, and information criteria, an applied course on evaluating theory-based hypotheses (via Bayesian and information-theoretic model selection) and on addressing causes of the replication crisis. This course focusses on the application of model/hypothesis selection methods.
- Applied Bayesian Statistics, to learn more about Bayes’ theorem, Gibbs sampling and the Metropolis-Hastings algorithm, Bayes factors, the evaluation of informative hypotheses, and Bayesian methods for linear regression, moderation, and mediation with observed variables. This course pays attention to the statistical theory using formulas.
- A gentle introduction to Bayesian Statistics, for material on Bayesian Structural Equation Modeling. This course focusses on SEM and does not cover hypothesis testing nor Bayes factors.
For an overview of all our summer school courses offered by the Department of Methodology and Statistics please click here.
Milica Miočević & Rens van de Schoot
Housing through Utrecht Summer School
Irma Reyersen - firstname.lastname@example.org