Keynote Presentation |
Concurrent Sessions |
Poster Sessions
Short Courses (full day) | Short Courses (half day) | Tutorials | Practical Computing Demos | Closing General Session with Refreshments
Short Courses (full day) | Short Courses (half day) | Tutorials | Practical Computing Demos | Closing General Session with Refreshments
| Saturday, February 17 | ||
| PCD2 Handling Missing Data Using Multiple Imputation |
Sat, Feb 17, 2:00 PM - 4:00 PM
Salons BC |
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| Instructor(s): Yulia Marchenko, StataCorp LLC | ||
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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 hot 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.
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