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A Rationale and Test for the Number of Factors in Factor Analysis
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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Citations
Cites
Some necessary conditions for common-factor analysis
Guttman, L. (1954) · Psychometrika · doi:10.1007/bf02289162 · Cited by 1,689
Research works printed but not linked: 5
- Anderson, 1958, An introduction to multivariate statistical analysis
- Kendall, 1958, The advanced theory of statistics (Vols. I and II)
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- [2] Dickman, K. W. Factorial validity of a rating instrument. Unpublished doctoral dissertation, Univ. Illinois, 1960.
- Rao, 1952, Advanced statistical methods in biometric research
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Metadata
- 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
- OpenAlex
- W2088477804
- Cited by
- 8,714
- References
- 1
- FWCI
- 10.61
- Type
- journal article
- Language
- en
- Open access
- closed
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