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An Empirical Comparison of Multiple Imputation Methods for Categorical Data

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  • Olanrewaju Akande
  • Fan Li
  • Jerome Reiter

Abstract

Multiple imputation is a common approach for dealing with missing values in statistical databases. The imputer fills in missing values with draws from predictive models estimated from the observed data, resulting in multiple, completed versions of the database. Researchers have developed a variety of default routines to implement multiple imputation; however, there has been limited research comparing the performance of these methods, particularly for categorical data. We use simulation studies to compare repeated sampling properties of three default multiple imputation methods for categorical data, including chained equations using generalized linear models, chained equations using classification and regression trees, and a fully Bayesian joint distribution based on Dirichlet process mixture models. We base the simulations on categorical data from the American Community Survey. In the circumstances of this study, the results suggest that default chained equations approaches based on generalized linear models are dominated by the default regression tree and Bayesian mixture model approaches. They also suggest competing advantages for the regression tree and Bayesian mixture model approaches, making both reasonable default engines for multiple imputation of categorical data. Supplementary material for this article is available online.

Suggested Citation

  • Olanrewaju Akande & Fan Li & Jerome Reiter, 2017. "An Empirical Comparison of Multiple Imputation Methods for Categorical Data," The American Statistician, Taylor & Francis Journals, vol. 71(2), pages 162-170, April.
  • Handle: RePEc:taf:amstat:v:71:y:2017:i:2:p:162-170
    DOI: 10.1080/00031305.2016.1277158
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    Citations

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    Cited by:

    1. Svetlana Zhuchkova & Aleksei Rotmistrov, 2022. "How to choose an approach to handling missing categorical data: (un)expected findings from a simulated statistical experiment," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(1), pages 1-22, February.
    2. Llano, Carlos & Pardo, Juan & Pérez-Balsalobre, Santiago & Pérez, Julián, 2023. "Estimating multicountry tourism flows by transport mode," Annals of Tourism Research, Elsevier, vol. 103(C).
    3. Tessmann, R. & Elbert, R., 2022. "Multi sided platforms in competitive B2B networks with varying governmental influence – a taxonomy of Port and Cargo Community System business models," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 132320, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    4. Humera Razzak & Christian Heumann, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 33-58, December.
    5. Ruben Tessmann & Ralf Elbert, 2022. "Multi-sided platforms in competitive B2B networks with varying governmental influence – a taxonomy of Port and Cargo Community System business models," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(2), pages 829-872, June.
    6. Razzak Humera & Heumann Christian, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 33-58, December.
    7. Zachary K. Collier & Minji Kong & Olushola Soyoye & Kamal Chawla & Ann M. Aviles & Yasser Payne, 2024. "Deep Learning Imputation for Asymmetric and Incomplete Likert-Type Items," Journal of Educational and Behavioral Statistics, , vol. 49(2), pages 241-267, April.

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