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 …

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 …

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 …

This paper describes simulation studies designed to answer the question of “Can an
assessment with these characteristics be modeled with a Bayesian network?” assuming that …

Background Statistical inference through testing a null hypothesis is a commonly used
method in psychological testing despite ongoing criticism of using P-values. In this paper …

Obtaining values for latent variables in factor analysis models, also referred to as factor
scores, has long been of interest to researchers. However, many treatments of factor …

Typical Bayesian methods for models with latent variables (or random effects) involve
directly sampling the latent variables along with the model parameters. In high-level …

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 the psychological literature, there are two seemingly different approaches to inference:
that from estimation of posterior intervals and that from Bayes factors. We provide an …

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 …