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

Johané Nienkemper-Swanepoel () and Michael J Maltitz
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Johané Nienkemper-Swanepoel: Stellenbosch University
Michael J Maltitz: University of the Free State

Journal of Classification, 2017, vol. 34, issue 3, No 3, 384-398

Abstract: 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.

Keywords: Incomplete categorical data; Multiple correspondence analysis; Multiple imputation; Principal component analysis; Regularized iterative multiple correspondence analysis (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s00357-017-9238-6

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