Day 2: the WAMBS

Outline

The second part of this workshop taps into the critical appraisal of your model. Bayesian analyses (as a matter a fact, all statistical analyses) are full of choices. So, as a researcher, it is our duty to understand what we are doing and provide evidence and arguments for the validity of our choices. To increase reproducibility, we tap into how you can use the WAMBS check-list to inform readers about our choices and decisions on the choice of priors, model convergence, and prior sensitivity.

Materials

Slides

The htlm-version of the slides for this part can be found here

Data

For this part, we again use the straightforward dataset on predicting racetimes for a marathon. The data can be downloaded here (right-click to save as).

References and resources

Depaoli, S., & Van de Schoot, R. (2017). Improving transparency and replication in Bayesian statistics: The WAMBS-Checklist. Psychological methods, 22(2), 240.

Van de Schoot, R., Veen, D., Smeets, L., Winter, S. D., & Depaoli, S. (2020). A tutorial on using the WAMBS checklist to avoid the misuse of Bayesian statistics. Small Sample Size Solutions: A Guide for Applied Researchers and Practitioners; van de Schoot, R., Miocevic, M., Eds, 30-49.

See the WAMBS page on this website for the template we use.

Alternative for the WAMBS is the BARG:

Kruschke, J. K. (2021). Bayesian Analysis Reporting Guidelines. Nature Human Behaviour, 5(10), Article 10. https://doi.org/10.1038/s41562-021-01177-7