Multiple imputation is a sophisticated method for handling missing data. It involves creating several different plausible datasets by imputing missing values multiple times. Each dataset is then analyzed separately, and the results are combined to produce final estimates. This method accounts for the uncertainty due to missing data and often provides more valid and reliable results.
