Multiple Imputation: A Solution for Nonresponse

Abstract

Missing data is found in virtually every large database, and can be a significant challenge for researchers who want to create analyses on the data. Several approaches have been developed to deal with missing data, but many of them are either ineffective or computationally expensive. However, an approach called Multiple Imputation (MI) is widely used due to its simplicity and effectiveness. MI creates several different sets of full databases (with missing data predicted from existing values of other variables), and then analyzes each one. These results are then combined to create one overall analysis. This seems very straightforward, but is not without problems. There are specific assumptions made when dealing with MI, and ignoring these assumptions can lead to invalid conclusions. Assumptions such as not having missing values in the predictors, not normalizing data, and not looking at missing data problems can all contribute to incorrect conclusions. However, if carefully implemented, MI is a powerful and simple tool that can be applied to almost any missing data problem.