Recent developments in parallel analysis with unities in the diagonal are reviewed, and the application of the parallel analysis criterion is illustrated with three examples. It is shown that the results of various approaches do not always agree. Investigators are encouraged to employ the parallel analysis criterion, along with one or more other criteria, in deciding on the number of principal components.
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References
1.
AllenS. J.HubbardR. (1986) Regression equations for the latent roots of random data correlation matrices with unities on the diagonal. Multivariate Behavioral Research, 21, 393–398.
2.
GorsuchR. L. (1983) Factor analysis. (2nd ed.) Hillsdale, NJ: Erlbaum.
3.
HaysR. D. (1987) PARALLEL: A program for performing parallel analysis. Applied Psychological Measurement, 11, 58.
4.
LautenschlagerG. J. (1987) PARANAL: A program for estimating parallel analysis criteria for principal components analysisz. (Author, University of Georgia).
5.
LautenschlagerG. J. (1989) A comparison of alternatives to conducting Monte Carlo analyses for determining parallel analysis criteria. Multivariate Behavioral Research, 24, 365–395.
6.
LautenschlagerG. J.LanceC. E.FlahertyV. L. (1989) Parallel analysis criteria: Revised equations for estimating the latent roots of random data correlation matrices. Educational and Psychological Measurement, 49, 339–345.
7.
LongmanR. S.CotaA. A.HoldenR. R.FekkenG. C. (1989a) PAM: A double-precision FORTRAN routine for the parallel analysis method in principal components analysis. Behavior Research Methods, Instruments, & Computers, 21, 477–480.
8.
LongmanR. S.CotaA. A.HoldenR. R.FekkenG. C. (1989b) A regression equation for the parallel analysis criterion in principal components analysis: Means and 95th percentile eigenvalues. Multivariate Behavioral Research, 24, 59–69.
9.
MontanelliR. G.Jr.HumphreysL. G. (1976) Latent roots of random data correlation matrices with squared multiple correlations on the diagonal: A Monte Carlo study. Psychometrika, 41, 341–348.
10.
SilversteinA. B. (1987) Note on the parallel analysis criterion for determining the number of common factors or principal components. Psychological Reports, 61, 351–354.
