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

1965/06/01 by John L. Horn · 99 citations
Agricultural and Biological Sciences · Mathematics · #Sensory Analysis and Statistical Methods #Advanced Statistical Methods and Models

paper · doi:10.1007/bf02289447

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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