Bayesian Psychometric Modeling [Hardcover]

A Single Cohesive Framework of Tools and Procedures for Psychometrics and Assessment

Bayesian Psychometric Modelingpresents a unified Bayesian approach across traditionally separate families of psychometric models. It shows that Bayesian techniques, as alternatives to conventional approaches, offer distinct and profound advantages in achieving many goals of psychometrics.

Adopting a Bayesian approach can aid in unifying seemingly disparateand sometimes conflictingideas and activities in psychometrics. This book explains both how to perform psychometrics using Bayesian methods and why many of the activities in psychometrics align with Bayesian thinking.

The first part of the book introduces foundational principles and statistical models, including conceptual issues, normal distribution models, Markov chain Monte Carlo estimation, and regression. Focusing more directly on psychometrics, the second part covers popular psychometric models, including classical test theory, factor analysis, item response theory, latent class analysis, and Bayesian networks. Throughout the book, procedures are illustrated using examples primarily from educational assessments. A supplementary website provides the datasets, WinBUGS code, R code, and Netica files used in the examples.

Foundations
Overview of Assessment and Psychometric Modeling
Assessment as Evidentiary Reasoning
The Role of Probability
The Role of Context in the Assessment Argument
Evidence-Centered Design
Summary and Looking Ahead

Introduction to Bayesian Inference
Review of Frequentist Inference via Maximum Likelihood
Bayesian Inference
Bernoulli and Binomial Models
Summarizing Posterior Distributions
Graphical Model Representation
Analyses Using Wlã$