An attempt to illustrate what a Bayes Factor looks like, using GIFs.
I am currently doing a joint PhD between the Univ. Grenoble Alpes (France) and the Ghent University (Belgium). My research is about the psychophysiology of verbal rumination, considered as a dysfunctional kind of inner speech.
Besides, I feel very concerned by the issue of making our research more transparent and more reproducible. I am involved in this project, in which we try to implement new recipes for doing science in a different way.
PhD in Cognitive Psychology, 2019
Univ. Grenoble Alpes
PhD in Clinical and Experimental Psychology, 2019
Ghent University
MSc in Cognitive Science, 2015
Grenoble Institute of Technology
BA in Psychology, 2013
Pierre-Mendès France University
An attempt to illustrate what a Bayes Factor looks like, using GIFs.
Why can’t we be more idiographic in our research? It is the individual organism that is the principle unit of analysis in the science of psychology (Barlow & Nock, 2009).
As put by Gelman et al. (2013, page 148): ‘because a probability model can fail to reflect the process that generated the data in any number of ways, posterior predictive p-values can be computed for a variety of test quantities in order to evaluate more than one possible model failure’.
What is the difference between the errors and the residuals ? What does it mean for a model to predict something ? What is a link function ? In the current post, we use four R functions (viz., the predict
, fitted
, residuals
and simulate
functions) to illustrate the mechanisms and assumptions of the generalised linear model.
In the current post, we present and compare three methods of obtaning an estimation of the ICC in multilevel logistic regression models.
I teach the following courses at Univ. Grenoble Alpes:
A great compendium of indispensable ressources on MLMs can be found on Social by selection.
The last chapters of Broadening Your Statistical Horizons by J. Legler and P. Roback.
Justin Esarey’s enlightening lectures on Bayesian statistics (amongst other things), and the Learn Bayes website.
Statistical rethinking lectures by Richard McElreath on youtube and associated contents.
Bayesian data analysis and cognitive modeling by Michael Franke & Fabian Dablander (slides).
Bayesian Basics, wonderful introduction to Bayesian data analysis by Michael Clark here. See also his other tutorials here.
Andrew Gelman: Statistical Modeling, Causal Inference, and Social Science
Rasmus Bååth: Publishable Stuff
Matti Vuorre: https://vuorre.netlify.com
Leif Nelson, Joe Simmons & Uri Simonsohn: Data Colada
Kristoffer Magnusson: R-Psychologist
Félix Schönbrodt: Nice Bread
Deborah Mayo: Error Statistics Philosophy
Allen Downey: Probably Overthinking It
Alexander Etz: The Etz-Files
Paul Bürkner: More than Bayesian Multilevel Models
Richard McElreath: Elements of Evolutionary Anthropology
Antonio Schettino: Not worth more than a bare mention