Multiple Imputation (MI) is a statistical technique used to handle missing data by creating multiple complete datasets. Each dataset is analyzed separately, and the results are combined to produce estimates and inferences that account for the uncertainty associated with missing data. This method is particularly useful in nursing research where incomplete data can compromise the validity and reliability of study findings.
