Multiple imputation is a method that involves creating multiple complete datasets by filling in missing data with plausible values. Each imputation is done independently and results in multiple datasets that are later analyzed separately. The results of these analyses are then pooled to produce estimates and inferences that account for the uncertainty associated with the missing data.
