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Imputation of Missing Categorical Data by Maximizing Internal Consistency

Published online by Cambridge University Press:  01 January 2025

Stef van Buuren  and
Jan L. A. van Rijckevorsel
Stef van Buuren*
Affiliation:
Department of Statistics, TNO Institute of Preventive Health Care, Leiden
Jan L. A. van Rijckevorsel
Affiliation:
Department of Statistics, TNO Institute of Preventive Health Care, Leiden
*
Requests for reprints should be sent to Stef van Buuren, TNO Institute of Preventive Health Care, PO Box 124, 2300 AC Leiden, THE NETHERLANDS. Email: buuren@nipg.tno.nl.
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Abstract

This paper suggests a method to supplant missing categorical data by “reasonable” replacements. These replacements will maximize the consistency of the completed data as measured by Guttman's squared correlation ratio. The text outlines a solution of the optimization problem, describes relationships with the relevant psychometric theory, and studies some properties of the method in detail. The main result is that the average correlation should be at least 0.50 before the method becomes practical. At that point, the technique gives reasonable results up to 10–15% missing data.

Keywords

missing datacorrelation ratiooptimal scaling
Type
Original Paper
Information
Psychometrika , Volume 57 , Issue 4 , December 1992 , pp. 567 - 580
Copyright
Copyright © 1992 The Psychometric Society

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