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

Author

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  • Johané Nienkemper-Swanepoel

    (Stellenbosch University)

  • Michael J Maltitz

    (University of the Free State)

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.

Suggested Citation

  • Johané Nienkemper-Swanepoel & Michael J Maltitz, 2017. "Investigating the Performance of a Variation of Multiple Correspondence Analysis for Multiple Imputation in Categorical Data Sets," Journal of Classification, Springer;The Classification Society, vol. 34(3), pages 384-398, October.
    • Handle: RePEc:spr:jclass:v:34:y:2017:i:3:d:10.1007_s00357-017-9238-6
    DOI: 10.1007/s00357-017-9238-6
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    References listed on IDEAS

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    1. Julie Josse & Marie Chavent & Benot Liquet & François Husson, 2012. "Handling Missing Values with Regularized Iterative Multiple Correspondence Analysis," Journal of Classification, Springer;The Classification Society, vol. 29(1), pages 91-116, April.
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