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Alpha Factor Analysis

Published online by Cambridge University Press:  01 January 2025

Henry F. Kaiser  and
John Caffrey
Henry F. Kaiser
Affiliation:
University of Wisconsin
John Caffrey
Affiliation:
System Development Corporation
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Abstract

A distinction is made between statistical inference and psychometric inference in factor analysis. After reviewing Rao's canonical factor analysis (CFA), a fundamental statistical method of factoring, a new method of factor analysis based upon the psychometric concept of generalizability is described. This new procedure (alpha factor analysis, AFA) determines factors which have maximum generalizability in the Kuder-Richardson, or alpha, sense. The two methods, CFA and AFA, each have the important property of giving the same factors regardless of the units of measurement of the observable variables. In determining factors, the principal distinction between the two methods is that CFA operates in the metric of the unique parts of the observable variables while AFA operates in the metric of the common (“communality”) parts.

On the other hand, the two methods are substantially different as to how they establish the number of factors. CFA answers this crucial question with a statistical test of significance while AFA retains only those alpha factors with positive generalizability. This difference is discussed at some length. A brief outline of a computer program for AFA is described and an example of the application of AFA is given.

Information

Type
Original Paper
Information
Psychometrika , Volume 30 , Issue 1 , March 1965 , pp. 1 - 14
Copyright
Copyright © 1965 Psychometric Society

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Footnotes

*

The first version of this paper was prepared while the senior author was a U. S. Public Health Service Fellow at the Center for Advanced Study in the Behavioral Sciences and while the junior author was Director of Research of the Palo Alto Public Schools.

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