Model-based Multiple Imputation for Multilevel Data: Methodological Extensions and Software Enhancements

Project: Research project

Project Details

Description

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.
StatusActive
Effective start/end date7/1/206/30/22

Funding

  • US Department of Education (DOEd): $25,809.00

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