Abstract
In order to make the parallel analysis criterion for determining the number of factors easy to use, regression equations for predicting the logarithms of the latent roots of random correlation matrices, with squared multiple correlations on the diagonal, are presented. The correlation matrices were derived from distributions of normally distributed random numbers. The independent variables are log (N−1) and log {[n(n−1)/2]−[(i−1)n]}, whereN is the number of observations;n, the number of variables; andi, the ordinal position of the eigenvalue. The results were excellent, with multiple correlation coefficients ranging from .9948 to .9992.
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This research was supported by the Office of Naval Research under Contract N00014-67-A-0305-0012, Lloyd G. Humphreys, principal investigator, and by the Department of Computer Science of which Richard G. Montanelli, Jr., is a member.
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Montanelli, R.G., Humphreys, L.G. Latent roots of random data correlation matrices with squared multiple correlations on the diagonal: A monte carlo study. Psychometrika 41, 341–348 (1976). https://doi.org/10.1007/BF02293559
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DOI: https://doi.org/10.1007/BF02293559