|Title:||Model-based Multiple Imputation for Multilevel Data: Methodological Extensions and Software Enhancements|
|Principal Investigator:||Enders, Craig||Awardee:||University of California, Los Angeles|
|Program:||Statistical and Research Methodology in Education [Program Details]|
|Award Period:||3 years (07/01/19 – 06/30/22)||Award Amount:||$868,046|
|Type:||Methodological Innovation||Award Number:||R305D190002|
Co-Principal Investigators: Du, Han; Keller, Brian
The purpose of this grant is to develop, implement, and evaluate new methodologies that will substantially increase the range of substantive applications of the Blimp multiple e imputation software package. Blimp was initially developed by the research team through a previous IES grant, Multiple Imputation Procedures for Multilevel Data, and the software in its current form is freely available at http://www.appliedmissingdata.com/multilevel-imputation . Through this grant, the research team will expand Blimp's capabilities in two ways. First, they will render Blimp able to accommodate a comprehensive set of not missing at random (NMAR) missing data mechanisms. Second, they will further develop Blimp so that it is a general-use Bayesian analysis package.
The expansion of Blimp to account for data that are NMAR is important for education research. For example, it is quite likely that low-achieving students are more likely to opt out of state and district standardized tests, thus creating a situation where the reason for the missing value is related to a student's unobserved achievement score. Adding Bayesian analysis capabilities to Blimp will allow researchers to impute missing data and run statistical analyses in the same software package, rather than having to export the imputed datasets to a different software package. The specific tasks for incorporating Bayesian statistical analysis into the software are: 1) redesigning the graphical interface to include an output window that displays basic summaries of all model parameters; 2) graphical tools such a trace plots and kernel density plots; 3) a comprehensive set of prior distributions; 4) additional convergence diagnostic measures; and 5) the deviance information criterion (DIC) for evaluating model fit. The research team will provide the updated software as a free package here. In addition, the researchers will disseminate research findings and software enhancements through methodological and applied peer-reviewed journal manuscripts and present at conferences.
Project Website: http://www.appliedmissingdata.com/multilevel-imputation