Previous studies have suggested that action constraints influence visual perception of distances. For instance, the greater the effort to cover a distance, the longer people perceive this distance to be. The present multilevel Bayesian meta-analysis …

A gentle conceptual and practical primer to Bayesian multilevel models using R, brms, and Stan.

Bayesian multilevel models are increasingly used to overcome the limitations of frequentist approaches in the analysis of complex structured data. This paper introduces Bayesian multilevel modelling for the specific analysis of speech data, using the …

An attempt to illustrate what a Bayes factor looks like, using GIFs.

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'.

In the current post, we present and compare three methods of obtaning an estimation of the ICC in multilevel logistic regression models.

This post continues our exploration of the logistic regression model by extending it to a multilevel logistic regression model, using the brms package.

This post aims to assess the average probability of participant presence in psychological experiments and, in the meantime, to introduce Bayesian logistic regression using R and the rethinking package.

According to Rubin (1984), a Bayesianly justifiable analysis is one that "treats known values as observed values of random variables, treats unknown values as unobserved random variables, and calculates the conditional distribution of unknowns given knowns and model specifications using Bayes’ theorem"

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