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Estimation for the Multiple Factor Model when Data are Missing

Published online by Cambridge University Press:  01 January 2025

Carl Finkbeiner
Carl Finkbeiner*
Affiliation:
The Procter & Gamble Company
*
Requests for reprints should be sent to C. T. Finkbeiner, Ivorydale Technical Center, 3W76, The Procter & Gamble Co., Cincinnati, Ohio 45217.
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Abstract

A maximum likelihood method of estimating the parameters of the multiple factor model when data are missing from the sample is presented. A Monte Carlo study compares the method with 5 heuristic methods of dealing with the problem. The present method shows some advantage in accuracy of estimation over the heuristic methods but is considerably more costly computationally.

Keywords

factor analysismissing data
Type
Original Paper
Information
Psychometrika , Volume 44 , Issue 4 , December 1979 , pp. 409 - 420
Copyright
Copyright © 1979 The Psychometric Society

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Footnotes

This paper is based on the author’s doctoral dissertation at the Department of Psychology, University of Illinois at Urbana-Champaign. The author gratefully acknowledges the aid of Drs. Robert Bohrer, Charles Lewis, Robert Linn, Maurice Tatsuoka, and Ledyard Tucker.

References

Reference Notes

Finkbeiner, C. T. Estimation for the multiple common factor model when data are missing. Unpublished dissertation, University of Illinois at Urbana—Champaign, 1976.Google Scholar
Gruvaeus, G. T. & Jöreskog, K. G. A computer program for minimizing a function of several variables (E. T. S. Res. Bull. RB70-14), 1970, Princeton, N. J.: Educational Testing Service.Google Scholar
Lewis, P. A., Goodman, A. S., & Miller, J. M. A pseudo-random number generator for the System/360. IBM System Journal, No. 2, 1969, pp. 136–146.CrossRefGoogle Scholar

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