Multiple Imputation

Multiple imputation is a statistical method used to handle missing data in research. Instead of just filling in missing values once, it creates several complete datasets by estimating plausible values based on available information. These datasets are then analyzed separately, and the results are combined to produce final estimates that account for the uncertainty caused by missing data. This approach provides more accurate and reliable results than simple methods like ignoring missing data or filling in a single estimate, helping researchers draw better conclusions despite incomplete information.