# Tutorial

## 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 …

## Visualising within-subject effects and stochastic dominance with (augmented) modified Brinley plots

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).

## 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'.

## Using R to make sense of the generalised linear model

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.