Multiple imputation is a statistical technique used to deal with missing data in research studies. It involves generating multiple sets of complete data by imputing missing values based on the observed data and their relationships. This approach helps to account for uncertainty in the imputed values and produce more accurate and reliable results compared to single imputation methods. Multiple imputation is commonly used in various fields such as social sciences, healthcare, and epidemiology to handle missing data and improve the validity of statistical analyses.