My First Bayes: A Gentle introduction to Bayesian Analysis Summer School
Aims
The main objectives of this course are:
- To gain knowledge of Bayesian statistics and learn when to use to Bayesian analyses.
- To become proficient in the use of the program Mplus, Blavaan and JASP for Bayesian analysis.
- To learn how to apply the WAMBS-checklist (When to worry and how to Avoid the Misuse of Bayesian Statistics).
Information
In the 20st century the statistical tools most often used by researchers in the field of management are based on frequentist statistics (i.e., p-values and null hypothesis testing). Since the beginning of the 21st century, however, Bayesian statistical methods are slowly creeping into all fields of science and are becoming ever more popular in applied research. This increase is specifically due to recent computational advancements and the availability of Bayesian estimation methods in popular software and programming languages like Mplus, Blavaan (R), and JASP. Moreover, the use of Bayesian methods has increased because this estimation framework can handle some commonly encountered problems in orthodox statistics. For example, Bayesian methods can be used for, e.g., producing more accurate parameter estimates and aiding in situations where only small sample sizes are available. Alternatively, some researchers implement Bayesian methods simply because they like the methodology, or believe in the Bayesian way of updating knowledge with new data instead of testing the null hypothesis over and over again assuming nothing is going on in the population.
Although it is very attractive to use Bayesian statistics, naively applying Bayesian methods can be dangerous for three main reasons: the potential influence of priors, misinterpretation of Bayesian features and results, and improper reporting of Bayesian results. To deal with these three points of potential danger, we will spend a lot of time discussing these issues. You will learn how to use the WAMBS-checklist (When to worry and how to Avoid the Misuse of Bayesian Statistics). The purpose of the questionnaire is to describe 10 main points that should be thoroughly checked when applying Bayesian analysis.
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 (safely) applying Bayesian methods to empirical data.
Assessment
Before the course starts (beginning of June) you will receive a set of papers to read and a set of exercises that have to be completed before the course starts (the amount of work is 1 ECTS).
After the course you will have to complete a final exercise to get the third ECTS-credit (to be handed in before July 12). More details about this exercise will be provided during the course.
Materials
A literature list will be send to participants in the first week of June
Additional info
After registration please send the following information to the lecturer:
- Which software do you mainly use for your analyses?
- Have you experience with SEM? If so, provide a (very) brief overview.
- Have you experience with Bayesian analyses?
- Are there specific topics you want to be covered?
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For the timetable of this course, please click here.
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To register, ERIM participants can take the following steps:
1. Go to SIN Online and log in with your ERNA credentials if required.
2. Click in the checkbox next to the course title and click Save Changes.
3. Your registration is complete. You will receive an automatic confirmation e-mail.
External (non-ERIM) participants are welcome to this course. To register, please fill in the registration form and e-mail it to summerschool@erim.eur.nl by 4 weeks prior to the start of the course. Please note that the number of places for this course is limited.
This course is free of charge for ERIM members (faculty members, PhD candidates and Research Master students). For external participants, the course fee is 250 euro per ECTS credit.
