Abstract
Missing data frequently complicates data analysis for scientific investigations. The development of statistical methods to address missing data has been an active area of research in recent decades. Multiple imputation, originally proposed by Rubin in a public use dataset setting, is a general purpose method for analyzing datasets with missing data that is broadly applicable to a variety of missing data settings. We review multiple imputation as an analytic strategy formissing data. Wedescribe and evaluate a number of software packages that implement this procedure, and contrast the interface, features, and results. We compare the packages, and detail shortcomings and useful features. The comparisons are illustrated using examples from an artificial dataset and a study of child psychopathology. We suggest additional features as well as discuss limitations and cautions to consider when using multiple imputation as an analytic strategy for incomplete data settings.
Keywords
Missing dataImputation (statistics)Computer scienceData miningData scienceMachine learning
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Publication Info
- Year
- 2001
- Type
- article
- Volume
- 55
- Issue
- 3
- Pages
- 244-254
- Citations
- 597
- Access
- Closed
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Cite This
Nicholas J. Horton,
Stuart R. Lipsitz
(2001).
Multiple Imputation in Practice.
The American Statistician
, 55
(3)
, 244-254.
https://doi.org/10.1198/000313001317098266
Identifiers
- DOI
- 10.1198/000313001317098266