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.
Keywords
Guttman scaleMathematicsStatisticsMathematical proofRank (graph theory)Covariance matrixLatent variableComponent (thermodynamics)Upper and lower boundsMatrix (chemical analysis)Factor analysisSampling (signal processing)EconometricsApplied mathematicsCombinatoricsComputer scienceMathematical analysis
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Publication Info
- Year
- 1965
- Type
- article
- Volume
- 30
- Issue
- 2
- Pages
- 179-185
- Citations
- 8259
- Access
- Closed
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Cite This
John L. Horn
(1965).
A Rationale and Test for the Number of Factors in Factor Analysis.
Psychometrika
, 30
(2)
, 179-185.
https://doi.org/10.1007/bf02289447
Identifiers
- DOI
- 10.1007/bf02289447