Project Activities
The research team will first develop the mathematical models and then program them for testing via Monte Carlo simulation studies to evaluate the validity and reliability of the proposed modeling framework in a variety of research contexts. They will also conduct secondary data analyses to demonstrate the utility and added value of the proposed methods, relative to other common approaches. The results of the simulations and the secondary data comparisons will render a synthesis of practical recommendations for researchers interested in applying the proposed methods in intervention studies.
People and institutions involved
IES program contact(s)
Project contributors
Products and publications
The research team will develop user-friendly software for conducting multilevel longitudinal DCM analyses and will provide workshops to applied audiences, in addition to supporting the software with online training materials.
Publications:
ERIC Citations: Find available citations in ERIC for this award here.
Publications:
Jurich, D., & Madison, M. J. (2023) Measuring item influence for diagnostic classification models. Educational Assessment. https://doi.org/10.1080/10627197.2023.2244411.
Maas, L., Madison, M. J., Brinkhuis, M. J. S. (2024). Properties and performance of the one parameter log-linear cognitive diagnosis model. Frontiers in Education. https://doi.org/10.3389/feduc.2024.1287279.
Madison, M.J., Chung, S., Kim, J., and Bradshaw, L.P. (2023). Approaches to Estimating Longitudinal Diagnostic Classification Models. Online first version of article to be published in Behaviormetrika. https://doi.org/10.1007/s41237-023-00202-5.
Madison, M. J., Jeon, M. Cotterell, M. E., Haab, S., & Zor, S. (2025). TDCM: An R package for estimating longitudinal diagnostic classification models. Multivariate Behavioral Research. https://doi.org/10.1080/00273171.2025.2453454.
Madison, M. J., Wind, S. A., Maas, L., Yamaguchi, K., & Haab, S. (2024). A one-parameter diagnostic classification model with familiar properties. Journal of Educational Measurement. https://doi.org/10.1111/jedm.12390.
Ravand, H., Effatpanah, F., Kunina-Habenicht, O., & Madison, M. J. (2025). A didactic illustration of writing skill growth through a longitudinal diagnostic classification model. Frontiers in Psychology, 15. https://doi.org/10.3389/fpsyg.2024.1521808.
Schellman, M., & Madison, M. J. Estimating the reliability of skill transition in longitudinal diagnostic classification models. (2024). Journal of Educational and Behavioral Statistics. https://doi.org/10.3102/10769986241256032.
Additional project information
The team has produced two software packages to date:
Madison, M. J., Haab., S., Jeon, M., & Cotterell, M. E. (2023-2025). TDCM: An R package for estimating longitudinal diagnostic classification models. Available at https://cran.r-project.org/web/packages/TDCM/index.html.
Mardones-Segovia, C., Nadar, Y., Madison, M.J., & Cotterell, M. E. (2025). TDCMApp: A Shiny App for TDCM Estimation and Reporting.
Related projects
Supplemental information
Co-Principal Investigators: Jeon, Minjeong; Cotterell, Michael
Questions about this project?
To answer additional questions about this project or provide feedback, please contact the program officer.
