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Investigating the Performance of a Variation of Multiple Correspondence Analysis for Multiple Imputation in Categorical Data Sets

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Abstract

Non-response in survey data, especially in multivariate categorical variables, is a common problem which often leads to invalid inferences and inefficient estimates. A regularized iterative multiple correspondence analysis (RIMCA) algorithm in single imputation (SI) has been suggested for the handling of missing categorical data in survey analysis. This paper proposes an adapted version of the SI algorithm for multiple imputation (MI). The SI and MI techniques are compared for both simulated and real questionnaire data. A comparison between RIMCA MI and Sequential Regression Multiple Imputation (SRMI) is shown to establish the success of the proposed MI procedure.

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Authors and Affiliations

  1. Department of Genetics, Faculty of AgriSciences, Stellenbosch University, JC Smuts Building, Block A, Room 220, Van Der Bijl Street, Stellenbosch, 7600, South Africa

    Johané Nienkemper-Swanepoel

  2. University of the Free State, Bloemfontein, South Africa

    Michael J von Maltitz

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  1. Johané Nienkemper-Swanepoel
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  2. Michael J von Maltitz
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Correspondence to Johané Nienkemper-Swanepoel.

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Nienkemper-Swanepoel, J., von Maltitz, M.J. Investigating the Performance of a Variation of Multiple Correspondence Analysis for Multiple Imputation in Categorical Data Sets. J Classif 34, 384–398 (2017). https://doi.org/10.1007/s00357-017-9238-6

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  • DOI: https://doi.org/10.1007/s00357-017-9238-6

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