, Volume 30, Issue 2, pp 179–185 | Cite as

A rationale and test for the number of factors in factor analysis

  • John L. Horn


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.


Public Policy Correlation Matrix Statistical Theory Latent Root Sampling Error 
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Copyright information

© Psychometric Society 1965

Authors and Affiliations

  • John L. Horn
    • 1
  1. 1.University of DenverUSA

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