With the recent development of easy-to-use tools for Bayesian analysis, psychologists have
started to embrace Bayesian hierarchical modeling. Bayesian hierarchical models provide …

Over the past 2 decades Bayesian methods have been gaining popularity in many scientific
disciplines. However, to this date, they are rarely part of formal graduate statistical training in …

Bayesian data analysis involves describing data by meaningful mathematical models, and
allocating credibility to parameter values that are consistent with the data and with prior …

Hierarchical Bayesian modeling provides a flexible and interpretable way of extending
simple models of cognitive processes. To introduce this special issue, we discuss four of the …

In this guide, we present a reading list to serve as a concise introduction to Bayesian data
analysis. The introduction is geared toward reviewers, editors, and interested researchers …

In this chapter we present such an alterative: hierarchical modeling. In a hierarchical model,
variability from the process of interest, as well as from nuisance sources such as from …

Psychological experiments often yield data that are hierarchically structured. A number of
popular shortcut strategies in cognitive modeling do not properly accommodate this structure …

It is impossible to give a comprehensive introduction to Bayesian data analysis in just one
chapter. In the sequel, I will present what I consider to be the most important components of …

Abstract Model comparison is the cornerstone of theoretical progress in psychological
research. Common practice overwhelmingly relies on tools that evaluate competing models …

Bayesian linear mixed-effects models (LMMs) and Bayesian analysis of variance (ANOVA)
are increasingly being used in the cognitive sciences to perform null hypothesis tests, where …