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A Rationale and Test for the Number of Factors in Factor Analysis

Horn, John L.·1965·Psychometrika

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Abstract

It is suggested that if Guttman’s latent-root-one lower bound estimate for the rank of a correlation matrix is accepted as a psychometric upper bound, following the proofs and arguments of Kaiser and Dickman, then the rank for a sample matrix should be estimated by subtracting out the component in the latent roots which can be attributed to sampling error, and least-squares “capitalization” on this error, in the calculation of the correlations and the roots. A procedure based on the generation of random variables is given for estimating the component which needs to be subtracted.

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Some necessary conditions for common-factor analysis
Guttman, L. (1954) · Psychometrika · doi:10.1007/bf02289162 · Cited by 1,689

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Research works printed but not linked: 5

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Authors: Horn, J. L.
Venue: Psychometrika
Publisher: Cambridge University Press (CUP)
Published: 1965-06
Volume: 30
Issue: 2
Pages: 179-185
ISSN: 0033-3123 (ISSN-L)
1860-0980
DOI: 10.1007/bf02289447
OpenAlex: W2088477804
Cited by: 8,714
References: 1
FWCI: 10.61
Type: journal article
Language: en
Open access: closed
Licence: www.cambridge.org/core/terms (unspecified, 21764 d)

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A Rationale and Test for the Number of Factors in Factor Analysis

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