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Organizational training

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.

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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


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.


This course is available in-person or virtually. In-person training courses generally run for eight hours per day and include morning and afternoon breaks and a lunch break. Virtual training courses are typically divided into three- to four-hour daily sessions. You can arrange a convenient schedule with your instructor.

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