Handling missing data using multiple imputation

4 days 3–4 hours daily

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.

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Upcoming sessions

Currently, there are no scheduled sessions of this course.

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Course leader

Meghan Cain

Assistant Director, Educational Services

Meghan Cain is the Assistant Director of Educational Services at StataCorp LLC. She earned her PhD in quantitative psychology from the University of Notre Dame, where her research focused on structural equation modeling, multilevel modeling, and Bayesian statistics. At Stata, she oversees training courses, webinars, and videos, and develops and presents many of them herself. She also reviews Stata Press books and provides statistical expertise for marketing content.

Prerequisite

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.

Course topics

Multiple-imputation overview

MI as a statistical procedure
Stages of MI: Imputation, analysis, and pooling
MI in Stata—the mi suite of commands

Imputation

Imputation techniques
Univariate imputation
Multivariate imputation
Checking the sensibility of imputations

Data management

Storing multiply imputed data
Importing existing multiply imputed data
Verification of multiply imputed data
Variable management (passive variables)
Merging, appending, and reshaping multiply imputed data
Exporting multiply imputed data to a non-Stata application

Estimation

Using mi estimate to perform the analysis and pooling stages of MI in one easy step
Estimating linear and nonlinear functions of coefficients
Testing linear and nonlinear hypotheses

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