This workshop will cover the use of Stata to perform multiple-imputation analysis. Multiple imputation (MI) is a simulation-based technique for handling missing data. The course will provide a brief introduction to multiple imputation and will demonstrate how to perform multiple imputation in Stata. The three stages of MI (imputation, completed-data analysis, and pooling) will be discussed with accompanying Stata examples. Imputation using multivariate normal (MVN) and using chained equations (MICE, FCS) will be discussed. A number of examples demonstrating how to efficiently manage multiply-imputed data within Stata will also be provided. Linear and logistic regression analysis of multiply-imputed data as well as several postestimation features will be presented. No prior knowledge of Stata is required, but basic familiarity with multiple imputation will prove useful.