Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 3 |
| Since 2017 (last 10 years) | 3 |
| Since 2007 (last 20 years) | 3 |
Descriptor
| Achievement Tests | 3 |
| Bayesian Statistics | 3 |
| Data Analysis | 3 |
| Foreign Countries | 3 |
| International Assessment | 3 |
| Prediction | 3 |
| Secondary School Students | 3 |
| Children | 2 |
| Evaluation Methods | 2 |
| Longitudinal Studies | 2 |
| Simulation | 2 |
| More ▼ | |
Author
| Kaplan, David | 2 |
| Lyu, Weicong | 2 |
| Yavuz, Sinan | 2 |
| Chen, Jianschen | 1 |
| Chen, Jianshen | 1 |
| David Kaplan | 1 |
| Jianshen Chen | 1 |
| Sinan Yavuz | 1 |
| Weicong Lyu | 1 |
Publication Type
| Reports - Research | 3 |
| Journal Articles | 2 |
Education Level
| Secondary Education | 3 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
| Program for International… | 3 |
| Early Childhood Longitudinal… | 2 |
What Works Clearinghouse Rating
Kaplan, David; Chen, Jianschen; Yavuz, Sinan; Lyu, Weicong – Grantee Submission, 2022
The purpose of this paper is to demonstrate and evaluate the use of "Bayesian dynamic borrowing"(Viele et al, in Pharm Stat 13:41-54, 2014) as a means of systematically utilizing historical information with specific applications to large-scale educational assessments. Dynamic borrowing via Bayesian hierarchical models is a special case…
Descriptors: Bayesian Statistics, Models, Prediction, Accuracy
Kaplan, David; Chen, Jianshen; Lyu, Weicong; Yavuz, Sinan – Large-scale Assessments in Education, 2023
The purpose of this paper is to extend and evaluate methods of "Bayesian historical borrowing" applied to longitudinal data with a focus on parameter recovery and predictive performance. Bayesian historical borrowing allows researchers to utilize information from previous data sources and to adjust the extent of borrowing based on the…
Descriptors: Bayesian Statistics, Longitudinal Studies, Children, Surveys
David Kaplan; Jianshen Chen; Weicong Lyu; Sinan Yavuz – Grantee Submission, 2023
The purpose of this paper is to extend and evaluate methods of "Bayesian historical borrowing" applied to longitudinal data with a focus on parameter recovery and predictive performance. Bayesian historical borrowing allows researchers to utilize information from previous data sources and to adjust the extent of borrowing based on the…
Descriptors: Bayesian Statistics, Longitudinal Studies, Children, Surveys

Peer reviewed
Direct link
