Model-based Multiple Imputation for Multilevel Data: Methodological Extensions and Software Enhancements Model-based Multiple Imputation for Multilevel Data: Methodological Extensions and Software Enhancements The project focuses on adding Bayesian capabilities to the Blimp software, which conducts multiple imputation and multilevel modeling when data are missing. The central components of these approaches to modeling missing data, estimation, and related analyses such as conducting model-data fit, relies on Bayesian statistical methodology. Dr. Levy has expertise in Bayesian statistical methods and philosophy and will bring this perspective to the project. In particular, Dr. Levy will assist with the theoretical development of the algorithms to conduct the proposed analyses, and advise Dr. Enders and other project investigators.
|Effective start/end date||7/1/20 → 6/30/22|
- US Department of Education (DOEd): $52,567.00
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