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A Feasible Method for Standard Errors of Estimate in Maximum Likelihood Factor Analysis

Published online by Cambridge University Press:  01 January 2025

R. I. Jennrich  and
D. B. Clarkson
R. I. Jennrich*
Affiliation:
University of California at Los Angeles
D. B. Clarkson
Affiliation:
University of Missouri at Saint Louis
*
Requests for reprints should be sent to Robert I. Jennrich, Department of Mathematics, University of California, Los Angeles, CA 90024.
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Abstract

A jackknife-like procedure is developed for producing standard errors of estimate in maximum likelihood factor analysis. Unlike earlier methods based on information theory, the procedure developed is computationally feasible on larger problems. Unlike earlier methods based on the jackknife, the present procedure is not plagued by the factor alignment problem, the Heywood case problem, or the necessity to jackknife by groups. Standard errors may be produced for rotated and unrotated loading estimates using either orthogonal or oblique rotation as well as for estimates of unique factor variances and common factor correlations. The total cost for larger problems is a small multiple of the square of the number of variables times the number of observations used in the analysis. Examples are given to demonstrate the feasibility of the method.

Keywords

maximum likelihood factor analysisstandard errorsjackknifepseudo valuescomputer algorithmsdifferentialsalignment problemHeywood case
Type
Original Paper
Information
Psychometrika , Volume 45 , Issue 2 , June 1980 , pp. 237 - 247
Copyright
Copyright © 1980 The Psychometric Society

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Footnotes

The research done by R. I. Jennrich was supported in part by NSF Grant MCS 77-02121. The research done by D. B. Clarkson was supported in part by NSERC Grant A3109.

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