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Handling Missing Data Using Multiple Imputation


This course 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 focus on how to perform MI in Stata using the mi command. The three stages of MI (imputation, complete-data analysis, and pooling) will be discussed in detail with accompanying Stata examples. Various imputation techniques will be discussed, including multivariate normal imputation (MVN) and multiple imputation using chained equations (MICE). Also, a number of examples demonstrating how to efficiently manage multiply imputed data within Stata will be provided. Linear and logistic regression analysis of multiply imputed data as well as several postestimation features will be presented.

Price: $1,295   Enroll now

We offer a 15% discount for group enrollments of three or more participants.

Course topics


Working knowledge of Stata and standard statistical techniques, such as linear/logistic regression, is required for the interactive parts of the course. The overview of the concepts of multiple imputation will be presented software-free.

Next session

Course Dates Location Cost Enroll
Handling Missing Data Using Multiple Imputation April 21–22, 2016 Washington, DC $1,295 Enroll

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Enrollment is limited. Computers with Stata installed are provided at all public training sessions. All training courses run from 8:30 a.m. to 4:30 p.m. each day. A continental breakfast, lunch, and an afternoon snack will also be provided; the breakfast is available before the course begins.

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