CE_30T: Handling missing data using multiple imputation in Stata (Added Fee)

This workshop will provide a conceptual and practical introduction to performing multiple imputation in Stata. Multiple imputation (MI) is a simulation-based approach for analyzing incomplete data. Often considered the most flexible approach for missing data, MI involves imputing the missing values over and over again based on a given model, performing the analysis on each of the resulting datasets, and combining the results. This workshop will include several demonstrations with real datasets, including an investigation of missing-data patterns, multiple imputation, analysis, and data management of multiply imputed datasets. No prior knowledge of Stata nor of multiple imputation is required, but basic familiarity with missing-data concepts will prove useful.