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On-site training

Handling Missing Data Using Multiple Imputation

Description

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. 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.

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Course topics

Prerequisites

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.

Notes

This is a two-day course. All training courses generally run for eight hours per day and include morning and afternoon breaks and a lunch break. You can arrange a convenient schedule with your instructor and training coordinator.

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