PLEASE NOTE: this course is almost fully booked!
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.
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.
Participants are requested to bring their own laptop for lab meetings.
For an overview of all our summer school courses offered by the Department of Methodology and Statistics please click here.
Katharina Meitinger & Rens van de Schoot
Housing through Utrecht Summer School
Irma Reyersen - email@example.com