Action effects on visual perception of distances: A multilevel Bayesian meta-analysis

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 …

An introduction to Bayesian multilevel models using R, brms, and Stan

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

An introduction to Bayesian multilevel models using brms: A case study of gender effects on vowel variability in standard Indonesian

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 …

What does a Bayes factor look like?

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

Checking the asumption of independence in binomial trials using posterior predictive checking

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'.

Three methods for computing the intra-class correlation in multilevel logistic regression

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

Experimental absenteeism and logistic regression, part II

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

Experimental absenteeism and logistic regression, part I

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.

Why the Akaike Information Criterion is as much 'Bayesian' as the Bayesian Information Criterion

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"