Robust and efficient multiple imputation of complex data sets

• Applicants: Professor Dr. Jost Reinecke; Professor Dr. Martin Spieß
• Subject Area: Empirical Social Research
• Term: from 2012 to 2017
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 220421560

Missing data occur even in carefully conducted scientific surveys. However, valid inferences based on incompletely observed data sets are only possible if the missing data problem is handled properly. One increasingly accepted method supported by data base producers to compensate for missing data is the method of multiple imputation. Available model-based techniques of generating multiple imputations are restricted to fully parametric models, which, if misspecified, may produce unnecessarily imprecise or even biased inferences. Furthermore, most of the available software is not designed to efficiently handle large complex clustered or panel data sets. In this project, multiple imputation procedures will be extended to enable efficient and robust imputation of complex data sets based on an approximate Bayesian approach, thus allowing valid and more precise inferences. Guidelines for the use of the multiple imputation method, based on currently available software and on functions and modules to be developed (callable in R), will be published, particularly with regard to possible limitations discussed in the literature. The need of the extensions to be developed will be illustrated through substantive applications and through analyses of real data sets. The imputation programs will be made available to the scientific community.

• DFG Programme: Research Grants