a method for addressing missing data in which several possible simulated values are inserted into a data set to replace omitted values, and then the mean and standard deviation of the set are calculated to arrive at an estimate to substitute for the missing value. Multiple imputation is considered less biased than other missing values procedures, such as listwise deletion, pairwise deletion, and single imputation.