31 August 2018
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, and longitudinal growth models. The first three days are designed to teach participants on how to estimate the above models in the Bayesian framework, day 4 is dedicated solely to practicing new skills on the participants’ data sets (or example data sets provided by instructors), and day 5 offers a showcase of a variety of modern Bayesian methods.
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.
Preliminary Course outline:
Morning: Conceptual introduction + reasons for using Bayesian methods + discussion on interpretability of results when using p-values/95%intervals + model selection+ posterior distributions + credibility intervals + empirical example of a linear regression analysis in the Bayesian framework
Afternoon: Computer lab software intro (choose from: Mplus, Blavaan, JAGS, or STAN) and fitting your first Bayesian model
Morning: Q&A + factor analysis in the Bayesian framework + approximate measurement invariance in the Bayesian framework + WAMBS-checklist (when to worry and how to avoid the misuse of Bayesian Statistics)
Afternoon: Computer lab on sensitivity to prior distributions + factor analysis + measurement invariance
Morning: Q&A + Bayesian mediation analysis + Bayesian longitudinal growth models
Afternoon: Computer lab on Bayesian mediation and longitudinal growth models
All day: Q&A + applying new methods to participants’ data or to example data sets provided by instructors
Showcase of developments in Bayesian analysis:
- Bayes for Small samples
- Experts as source of priors
- Updating expert opinions for 1 individual
- Updating Bayes Factors within a dataset
- Replication studies with Bayes Factors
Dr. Rens van de Schoot
Knowledge of regression analysis and basic SEM is required. No previous knowledge of Bayesian analysis is assumed. 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 the new developments and applications in the area of Bayesian methods for social sciences.
Participants will also complete the course with a foundation for future learning about Bayesian modeling and knowledge about available resources to guide such endeavors.
Certificate of Attendance
EUR 600: Course + course materials + lunch
EUR 200: Housing
on course website