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

Investigating the Performance of a Variation of Multiple Correspondence Analysis for Multiple... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Classification Springer Journals

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

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Publisher
Springer US
Copyright
Copyright © 2017 by Classification Society of North America
Subject
Statistics; Statistical Theory and Methods; Pattern Recognition; Bioinformatics; Signal,Image and Speech Processing; Psychometrics; Marketing
ISSN
0176-4268
eISSN
1432-1343
D.O.I.
10.1007/s00357-017-9238-6
Publisher site
See Article on Publisher Site

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