Project Activities
People and institutions involved
IES program contact(s)
Products and publications
ERIC Citations: Find available citations in ERIC for this award here.
Project Website: http://bise.wceruw.org/index.html
Additional Online Resources and Information:
Select Publications:
Journal articles
Kaplan, D. & Chen, J. & Yavuz, S. & Lyu, W. (2022). Bayesian dynamic borrowing of historical information with applications to the analysis of large-scale assessments. Psychometrika., 88(1)1-30. doi.org/10.1007/s11336-022-09869-3
Kaplan, D., Chen, J., Lyu, W. and Yavuz, S. (2023) Bayesian historical borrowing with longitudinal large-scale assessments Large-scale Assessments in Education, volume 11(1),2 doi.org/10.1186/s40536-022-00140-w
Viele, K., Berry, S., Neuenschwander, B., Amzal, B., Chen, F., Enas, N., ... & Thompson, L. (2014). Use of historical control data for assessing treatment effects in clinical trials. Pharmaceutical statistics, 13(1), 41-54.
Project website:
Related projects
Supplemental information
Co-Principal Investigator: Chen, Jianshen
Key Outcomes
- The researchers developed an application software program to conduct Bayesian dynamic borrowing called Shiny BHP App, which is available at https://github.com/Bayesian-Methods-for-Education-Research/ShinyBHB
- Details about applying Bayesian dynamic borrowing to large scale and longitudinal assessment were released in peer reviewed publications (Kaplan et al., 2022; Kaplan et al., 2023)
Statistical/Methodological Product: This project developed an application software program to conduct Bayesian dynamic borrowing for complex sampling designs for cross-sectional and longitudinal data found in education research.
Development/Refinement Process: Bayesian dynamic borrowing has been primarily developed in the clinical trials literature, where information is borrowed from control groups of previous studies in order to increase power. This project expanded Bayesian dynamic borrowing to complex sampling designs for cross-sectional and longitudinal data found in education research. The work required development of statistical models for the complexity of the data encountered in education and the use of new probabilistic modeling tools, such as the Stan programming environment, which underlies the application program.
Questions about this project?
To answer additional questions about this project or provide feedback, please contact the program officer.
