Bayesian Psychometric Modeling

Roy Levy, Robert J. Mislevy

© 2016 - Chapman and Hall/CRC
Published May 23, 2016
Reference - 466 Pages - 88 B/W Illustrations
ISBN 9781439884676 - CAT# K13977
Series: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

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Features

  • Provides a coherent view of how Bayesian inference applies to psychometrics across families of statistical models
  • Starts with simple models and then builds from them to develop more complicated models
  • Presents statistical models using both graphical approaches and systems of mathematical expressions
  • Describes computational steps for the examples, with software code and data included for each example
  • Contains end-of-chapter exercises for practice and further development
  • Offers the code and datasets on a supplementary website

Summary

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

Bayesian Psychometric Modeling presents 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 disparate—and sometimes conflicting—ideas 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.

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