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Alpha factor analysis

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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.

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Authors and Affiliations

  1. University of Wisconsin, USA

    Henry F. Kaiser

  2. System Development Corporation, USA

    John Caffrey

Authors
  1. Henry F. Kaiser
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  2. John Caffrey
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Additional information

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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Kaiser, H.F., Caffrey, J. Alpha factor analysis. Psychometrika 30, 1–14 (1965). https://doi.org/10.1007/BF02289743

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  • DOI: https://doi.org/10.1007/BF02289743

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