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Some Simple Procedures for Handling Missing Data in Multivariate Analysis

Published online by Cambridge University Press:  01 January 2025

James W. Frane
James W. Frane*
Affiliation:
University of California at Los Angeles
*
Requests for reprints should be sent to James W. Frane, Department of Biomathematics, University of California, Los Angeles, California 90024.
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Abstract

For analyses with missing data, some popular procedures delete cases with missing values, perform analysis with “missing value” correlation or covariance matrices, or estimate missing values by sample means. There are objections to each of these procedures. Several procedures are outlined here for replacing missing values by regression values obtained in various ways, and for adjusting coefficients (such as factor score coefficients) when data are missing. None of the procedures are complex or expensive.

Keywords

biascorrelationdiscriminant analysisfactor analysispivotingregressionstepwise
Type
Original Paper
Information
Psychometrika , Volume 41 , Issue 3 , September 1976 , pp. 409 - 415
Copyright
Copyright © 1976 The Psychometric Society

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Footnotes

This research was supported by NIH Special Research Resources Grant RR-3. The author expresses his gratitude to Robert I. Jennrich and the referees for their suggestions.

References

Anderson, T. W. An introduction to multivariate statistical analysis, 1958, New York: Wiley.Google Scholar
Bangert, C. J. & Frane, J. W. SFA41D—Missing data correlation and estimation of missing data, 1971, Lawrence, Kansas: University of Kansas Computation Center.Google Scholar
Gleason, T. C. & Staelin, R. A proposal for handling missing data. Psychometrika, 1975, 40, 229–252.CrossRefGoogle Scholar
Harman, H. H. Modern factor analysis, 2nd ed., Chicago: University of Chicago Press, 1967.Google Scholar
Rao, C. R. Linear statistical inference and its applications, 2nd ed., New York: Wiley, 1973.CrossRefGoogle Scholar