Bayesian psychometric modeling Levy, Roy

Table of Contents:

Section I Foundations

1. Overview of Assessment and Psychometric Modeling
2. Introduction to Bayesian Inference
3. Conceptual Issues in Bayesian Inference
4. Normal Distribution Models
5. Markov Chain Monte Carlo Estimation
6. Regression

Section II Psychometrics

7. Canonical Bayesian Psychometric Modeling
8. Classical Test Theory
9. Confirmatory Factor Analysis
10. Model Evaluation
11. Item Response Theory
12. Missing Data Modeling
13. Latent Class Analysis
14. Bayesian Networks
15. Conclusion

Appendix A: Full Conditional Distributions
Appendix B: Probability Distributions
References
Index

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 Nautica files used in the examples.

(https://www.crcpress.com/Bayesian-Psychometric-Modeling/Levy-Mislevy/p/book/9781439884676)