A Gentle Introduction to Bayesian Statistics
Faculty of Social and Behavioural Sciences
26 August 2019
30 August 2019
Utrecht, The Netherlands
A highly interactive 5-day course gently introducing Bayesian estimation for linear regression analysis, factor analysis, approximate measurement invariance testing, and longitudinal growth models. 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. We highly recommend bringing your own data to get input for your paper.
Download the day-to-day programme (PDF)
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 regression and structural equation models (SEM). 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.
For an overview of all our summer school courses offered by the Department of Methodology and Statistics please click here.
Dr. Rens van de Schoot
Katharina Meitinger & 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.
Housing through Utrecht Summer School
Addressing the Replication Crisis Using (Bayesian) Model Selection
Introduction to Multilevel Analysis
Introduction to Structural Equation Modeling using Mplus
Advanced course on using Mplus
Advanced Multilevel Analysis
Data Science: Statistical Programming with R
12 August 2019