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Asymptotic Comparison of Missing Data Procedures for Estimating Factor Loadings

Published online by Cambridge University Press:  01 January 2025

C. Hendricks Brown
C. Hendricks Brown*
Affiliation:
The Johns Hopkins University
*
Reprint requests should be addressed to C. Hendricks Brown, Department of Biostatistics, School of Hygiene and Public Health, 615 North Wolfe Street, The Johns Hopkins University, Baltimore, Maryland 21205.
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Abstract

Large sample properties of four methods of handling multivariate missing data are compared. The criterion for comparison is how well the loadings from a single factor model can be estimated. It is shown that efficiencies of the methods depend on the pattern or arrangement of missing data, and an evaluation study is used to generate predictive efficiency equations to guide one's choice of an estimating procedure. A simple regression-type estimator is introduced which shows high efficiency relative to the maximum likelihood method over a large range of patterns and covariance matrices.

Keywords

EM algorithm

Information

Type
Original Paper
Information
Psychometrika , Volume 48 , Issue 2 , June 1983, pp. 269 - 291
Copyright
Copyright © 1983 The Psychometric Society

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Footnotes

The author wishes to thank Professor David Wallace of the Statistics Department, University of Chicago, for providing valuable suggestions, guidance, and stimulus during the writing of the dissertation from which this work is drawn.

References

Reference Notes

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