The factor analysis of the image correlation matrix is suggested. Whether to factor the image correlation matrix itself or to proceed via a new model with an alpha (common) factor analysis of it is mentioned, with particular reference to the determinacy problem. The transformation (rotation) problem, in the oblique case, has a new aspect, for which a tentative resolution is proposed. It is pointed out that the distribution of the images is sensibly multivariate normal, making for "better" factor analyses.
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References
1.
Guttman, L.Image theory for the structure of quantitative variates. Psychometrika, 1953, 18, 277-296.
2.
Guttman, L. "Best possible" systematic estimates of communalities. Psychometrika, 1956, 21, 273-285.
3.
Harris, C. W.Some Rao-Guttman relationships . Psychometrika, 1962, 27, 247-263.
4.
Holzinger, K. J. and Harman, H. H.Factor analysis. Chicago: University of Chicago Press, 1941.
5.
Kaiser, H. F.A second generation Little Jiffy . Psychometrika, 1970, 35, 401-415.
6.
Kaiser, H. F.Image and anti-image covariance matrices from a correlation matrix that may be singular. Psychometrika, 1976, 41, 295-300.
7.
Kaiser, H. F. and Caffrey, J.Alpha factor analysis. Psychometrika , 1965, 30, 1-14.
8.
Kaiser, H. F. and Cerny, B. A.Pseudo-images and pseudo-anti-images from the pseudo-inverse of a singular correlation matrix. British Journal of Mathematical and Statistical Psychology, (1978, 31, 99-101. (a)
9.
Kaiser, H. F. and Cerny, B. A.Casey's method for fitting hyperplanes from an intermediate varimax solution. Multivariate Behavioral Research, 1978, 13, 395-401. (b)