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Resources for Further Learning

The pgdcm package is built on a rich foundation of psychometric theory, Bayesian statistics, and cognitive science. This page curates essential reading materials for serious practitioners and researchers who want to learn more about the theory underlying these methods.

Evidence Centered Design

  • Mislevy, R. J., Almond, R. G., & Lukas, J. F. (2003). A brief introduction to evidence‐centered design. ETS Research Report Series, 2003(1), i-29.

  • Sudheesh, A. (2024). The Changing Landscape of Validity-Centered Cognitive Learning-Instructional-Assessment Design Frameworks. https://doi.org/10.35542/osf.io/xe9qn

Diagnostic Classification Models

  • Rupp, A. A. (2023). Primer on diagnostic classification models. Dover, NH: Center for Assessment.

  • Williamson, J. (2023). Cognitive Diagnostic Models and How They Can Be Useful. Research Report. Cambridge University Press & Assessment.

  • Ravand, H., & Baghaei, P. (2020). Diagnostic classification models: Recent developments, practical issues, and prospects. International Journal of Testing, 20(1), 24-56.

  • Wang, F., Gao, W., Liu, Q., Li, J., Zhao, G., Zhang, Z., & Chen, E. (2024). A survey of models for cognitive diagnosis: New developments and future directions. arXiv preprint arXiv:2407.05458.

Bayesian Networks in Educational Assessment

  • Culbertson, M. J. (2016). Bayesian networks in educational assessment: The state of the field. Applied psychological measurement, 40(1), 3-21.

Bayesian Inference & Modeling

  • Zhan, P., Jiao, H., Man, K., & Wang, L. (2019). Using JAGS for Bayesian cognitive diagnosis modeling: A tutorial. Journal of Educational and Behavioral Statistics, 44(4), 473-503.

  • Gelman, A., Vehtari, A., Simpson, D., Margossian, C. C, Carpenter, B., Yao, Y., Kennedy, L., Gabry, J., Bürkner, P.-C, & Modrák, M. (2020). Bayesian workflow. arXiv preprint arXiv:2011.01808.

  • Schad, D. J., Betancourt, M., & Vasishth, S. (2021). Toward a principled Bayesian workflow in cognitive science. Psychological Methods, 26(1), 103–126.

  • Baribault, B., & Collins, A. G. (2025). Troubleshooting Bayesian cognitive models. Psychological Methods, 30(1), 128.

NIMBLE

  • de Valpine, P., Turek, D., Paciorek, C. J., Anderson-Bergman, C., Temple Lang, D., & Bodik, R. (2017). Programming with models: writing statistical algorithms for general model structures with NIMBLE. Journal of Computational and Graphical Statistics, 26(2), 403–417.

Textbooks

Bayesian Modeling & Inference

  • Levy, R., & Mislevy, R. J. (2017). Bayesian psychometric modeling. Chapman and Hall/CRC.

  • McElreath, R. (2018). Statistical rethinking: A Bayesian course with examples in R and Stan. Chapman and Hall/CRC.

  • Vehtari, A., Gelman, A., Dunson, D., Rubin, D., Stern, H., & Carlin, J. B. (2014). Bayesian data analysis.

Bayesian Networks & Educational Assessments

  • Almond, R. G., Mislevy, R. J., Steinberg, L. S., Yan, D., & Williamson, D. M. (2015). Bayesian networks in educational assessment. Springer.

NIMBLE Tutorials & User Manual