Simulation is a powerful approach that plays a significant role in science and technology.
Computational models that simulate learner interactions and data hold great promise for …

This journal recently published a systematic review of simulation studies on the performance
of Bayesian approaches for estimating latent variable models in small samples. The authors …

Integrative data analyses have recently been shown to be an effective tool for researchers
interested in synthesizing datasets from multiple studies in order to draw statistical or …

Recently, advancements in Bayesian structural equation modeling (SEM), particularly
software developments, have allowed researchers to more easily employ it in data analysis …

ABSTRACT A Bayesian statistics-based approach is discussed that can be used for direct
evaluation of the popular Cronbach's coefficient alpha as an internal consistency index for …

To date, only frequentist model-selection methods have been studied with mixed-format data
in the context of IRT model-selection, and it is unknown how popular Bayesian model …

The choice of prior specification plays a vital role in any Bayesian analysis. Prior-data
disagreement occurs when the researcher's prior knowledge is not in agreement with the …

Marginal maximum likelihood, a common estimation method for item response theory
models, is not inherently a Bayesian procedure. However, due to estimation difficulties …

An application of Bayesian factor analysis for evaluation of scale reliability is discussed,
which is developed within the framework of latent variable modeling. The method permits …

We discuss an application of Bayesian factor analysis for estimation of the optimal linear
combination and associated maximal reliability of a multi-component measuring instrument …