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A Gauss-Newton Algorithm for Exploratory Factor Analysis

Published online by Cambridge University Press:  01 January 2025

Robert I. Jennrich
Robert I. Jennrich*
Affiliation:
University of California, Los Angeles
*
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

It is shown that the scoring algorithm for maximum likelihood estimation in exploratory factor analysis can be developed in a way that is many times more efficient than a direct development based on information matrices and score vectors. The algorithm offers a simple alternative to current algorithms and when used in one-step mode provides the simplest and fastest method presently available for moving from consistent to efficient estimates. Perhaps of greater importance is its potential for extension to the confirmatory model. The algorithm is developed as a Gauss-Newton algorithm to facilitate its application to generalized least squares and to maximum likelihood estimation.

Keywords

maximum likelihoodone-step efficiencyFisher scoring
Type
Original Paper
Information
Psychometrika , Volume 51 , Issue 2 , June 1986 , pp. 277 - 284
Copyright
Copyright © 1986 The Psychometric Society

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Footnotes

This research was supported by NSF Grant MCS-8301587.

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