A hierarchical Bayesian method is proposed that can be used to fit multiple psychometric
functions (PFs) simultaneously across conditions and subjects. The method incorporates the …

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 …

A Single Cohesive Framework of Tools and Procedures for Psychometrics and Assessment
Bayesian Psychometric Modeling presents a unified Bayesian approach across traditionally …

This article presents relevant research on Bayesian methods and their major applications to
modeling in an effort to lay out differences between the frequentist and Bayesian paradigms …

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

Bayesian methods offer new insight into standard statistical models and provide novel
solutions to problems common in psychological research, such as missing data. Appeals for …

In this paper we argue that model selection, as commonly practised in psychometrics,
violates certain principles of coherence. On the other hand, we show that Bayesian …

A conceptual distinction is drawn between indicators, which serve to define latent variables,
and outcomes, which do not. However, commonly used frequentist and Bayesian estimation …

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 …

In a frequentist framework, the exact fit of a structural equation model (SEM) is typically
evaluated with the chi-square test and at least one index of approximate fit. Current …