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A Proposal for Handling Missing Data

Published online by Cambridge University Press:  01 January 2025

Terry C. Gleason  and
Richard Staelin
Terry C. Gleason
Affiliation:
Carnegie-Mellon University
Richard Staelin
Affiliation:
Carnegie-Mellon University
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Abstract

A method for dealing with the problem of missing observations in multivariate data is developed and evaluated. The method uses a transformation of the principal components of the data to estimate missing entries. The properties of this method and four alternative methods are investigated by means of a Monte Carlo study of 42 computer-generated data matrices. The methods are compared with respect to their ability to predict correlation matrices as well as missing entries.

The results indicate that whenever there exists modest intercorrelations among the variables (i.e., average off diagonal correlation above .2) the proposed method is at least as good as the best alternative (a regression method) while being considerably faster and simpler computationally. Models for determining the best alternative based upon easily calculated characteristics of the matrix are given. The generality of these models is demonstrated using the previously published results of Timm.

Type
Original Paper
Information
Psychometrika , Volume 40 , Issue 2 , June 1975 , pp. 229 - 252
Copyright
Copyright © 1975 The Psychometric Society

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Footnotes

*

This is an extension and elaboration of a paper read at the Spring 1973 meetings of the Psychometric Society. We wish to express our appreciation to Timothy McGuire for his helpful comments.

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