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Choosing Imputation Models

Published online by Cambridge University Press:  10 December 2021

Moritz Marbach Open the ORCID record for Moritz Marbach [Opens in a new window]
Moritz Marbach*
Affiliation:
The Bush School of Government & Public Service, Texas A&M University, 4220 TAMU, College Station, TX 77843-4220, USA. Email: [email protected]
*
Corresponding author Moritz Marbach
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Abstract

Imputing missing values is an important preprocessing step in data analysis, but the literature offers little guidance on how to choose between imputation models. This letter suggests adopting the imputation model that generates a density of imputed values most similar to those of the observed values for an incomplete variable after balancing all other covariates. We recommend stable balancing weights as a practical approach to balance covariates whose distribution is expected to differ if the values are not missing completely at random. After balancing, discrepancy statistics can be used to compare the density of imputed and observed values. We illustrate the application of the suggested approach using simulated and real-world survey data from the American National Election Study, comparing popular imputation approaches including random forests, hot-deck, predictive mean matching, and multivariate normal imputation. An R package implementing the suggested approach accompanies this letter.

Keywords

missing dataimputationweighting
Type
Letter
Information
Political Analysis , Volume 30 , Issue 4 , October 2022 , pp. 597 - 605
Copyright
© The Author(s) 2021. Published by Cambridge University Press on behalf of the Society for Political Methodology

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Footnotes

Edited by Daniel Hopkins

References

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https://doi.org/10.7910/DVN/IIXGBM
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