26 August 2019
on course website
A Gentle Introduction to Bayesian Statistics
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.
You might also be interested in the following Summer School courses:
- 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.
- 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.
The differences between these courses and this course:
1. Theory-Based Hypothesis Evaluation Using the p-value, Bayes factor, and information criteria addresses classical hypothesis testing, Bayesian model selection and model selection using information criteria. The focus is on the conceptual level (there will be hardly any formulas) and on the application of them in the data-analysis.
2. Applied Bayesian Statistics gives a broad overview of Bayesian statistics, with attention to the statistical theory (using formulas) and the application of Bayesian concepts to mean comparisons and parameter estimation in linear regression models.
3. A gentle introduction to Bayesian Statistics covers introductory concepts in Bayesian statistics, and teaches students how to fit linear regression, path, confirmatory factor analysis, and structural equation models in the Bayesian framework. Unlike the two courses above, this course does not cover hypothesis testing and Bayes factors.
Dr. Rens van de Schoot
Knowledge of regression analysis and basic SEM is required.
No previous knowledge of Bayesian analysis is assumed. If you want to prepare, you could read (not obligatory)
- van de Schoot, R., & Depaoli, S. (2014). Bayesian analyses: Where to start and what to report. European Health Psychologist, 16(2), 75-84.
- Kaplan, D., & Depaoli, S. (2012). Bayesian Structural Equation Modeling. In R. Hoyle (Ed.), Handbook of structural equation modeling. New York: Guilford Press.
You do not need to know matrix algebra, calculus, or likelihood theory. Since the course offers a gentle introduction there are hardly any formulas used in the lectures. The main focus is on conceptually understanding Bayesian statistics and applying Bayesian methods to your own data set. We assume knowledge of the software package you plan to use (R, Mplus, or JAGS).
Participants from a variety of fields—including psychology, education, human development, public health, prevention science, sociology, marketing, business, biology, medicine, political science, and communication—will benefit from the course.
After engaging in course lectures and discussions as well as completing the hands-on practice activities with real data, participants will:
• Explain the differences between ‘classical’ and Bayesian statistics.
• Know when to use to Bayesian analyses instead of classical statistics.
• Know how to apply Bayesian methods to answer their own research questions.
• Know how to apply the WAMBS-checklist (When to worry and how to Avoid the Misuse of Bayesian Statistics).
• Critically evaluate applications of Bayesian methods in scientific studies.
• Have an idea of how to obtain prior information for their own data.
Participants will also complete the course with a foundation for future learning about Bayesian modeling and knowledge about available resources to guide such endeavors.
EUR 600: Course fee
EUR 200: Housing fee
on course website