Hostname: page-component-745bb68f8f-l4dxg Total loading time: 0 Render date: 2025-01-08T09:51:20.875Z Has data issue: false hasContentIssue false

Function Invariant and Parameter Scale-Free Transformation Methods

Published online by Cambridge University Press:  01 January 2025

P. M. Bentler*
Affiliation:
University of California, Los Angeles
Joseph A. Wingard
Affiliation:
University of California, Los Angeles
*
Requests for reprints should be sent to P. M. Bentler, Department of Psychology, University of California, Los Angeles, CA 90024.

Abstract

The parameter matrices of factor analysis and principal component analysis are arbitrary with respect to the scale of the factors or components; typically, the scale is fixed so that the factors have unit variance. Oblique transformations to optimize an objective statement of a principle such as simple structure or factor simplicity yield arbitrary solutions, unless the criterion function is invariant with respect to the scale of the factors, or the parameter matrix is scale free with respect to the factors. Criterion functions that are factor scale-free have a number of invariance characteristics, such as being equally applicable to primary pattern or reference structure matrices. A scale-invariant simple structure function of previously studied function components is defined. First and second partial derivatives are obtained, and Newton-Raphson iterations are utilized. The resulting solutions are locally optimal and subjectively pleasing.

Type
Original Paper
Copyright
Copyright © 1977 The Psychometric Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Aspects of this paper were presented at the 1970 and 1974 annual meetings, Society of Multivariate Experimental Psychology, and the 1975 annual meeting, Psychometric Society. This investigation was supported in part by a Research Scientist Development Award (K02-DA00017) and research grants (MH24149 and DA01070) from the U. S. Public Health Service. The assistance of Bonnie Barron, Sik-Yum Lee, and several extremely helpful reviewers is gratefully acknowledged.

References

Reference Notes

Carroll, J. B. IBM704 program for generalized analytic rotation solution in factor analysis. Unpublished manuscript, Harvard University, 1960.Google Scholar
Kaiser, H. F. & Dickman, K. W. Analytic determination of common factors. Unpublished manuscript, University of Illinois, 1959.Google Scholar

References

Aitchison, J., & Silvey, S. C. Maximum likelihood estimation of parameters subject to restraints. Annals of Mathematical Statistics, 1958, 29, 813828.CrossRefGoogle Scholar
Anderson, T. W., & Rubin, H. Statistical inference in factor analysis. Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, 1956, 5, 111150.Google Scholar
Bentler, P. M. Multistructure statistical model applied to factor analysis. Multivariate Behavioral Research, 1976, 11, 322.CrossRefGoogle ScholarPubMed
Bentler, P. M. Factor simplicity index and transformations. Psychometrika, 1977, 277-295.CrossRefGoogle Scholar
Bentler, P. M., & Lee, S. Y. Some extensions of matrix calculus. General Systems, 1975, 20, 145-150.Google Scholar
Cattell, R. B. Parallel proportional profiles and other principles for determining the choice of factors by rotation. Psychometrika, 1944, 9, 267-283.CrossRefGoogle Scholar
Hakstian, A. R., & Abell, R. A. A further comparison of oblique factor transformation methods. Psychometrika, 1974, 39, 429444.CrossRefGoogle Scholar
Harman, H. H. Modern factor analysis, 1967, Chicago: University of Chicago.Google Scholar
Harris, C. W. Some Rao-Guttman relationships. Psychometrika, 1962, 27, 247-263.CrossRefGoogle Scholar
Harris, C. W., & Kaiser, H. F. Oblique factor analytic solutions by orthogonal transformations. Psychometrika, 1964, 29, 347-362.CrossRefGoogle Scholar
Himmelblau, D. M. Applied nonlinear programming, 1972, New York: McGraw-Hill.Google Scholar
Horst, P. Factor analysis of data matrices, 1965, New York: Holt, Rinehart, & Winston.Google Scholar
Jennrich, R. I. Simplified formulae for standard errors in maximum-likelihood factor analysis. British Journal of Mathematical and Statistical Psychology, 1974, 27, 122-131.CrossRefGoogle Scholar
Jennrich, R. I., & Sampson, P. F. Rotation for simple loadings. Psychometrika, 1966, 31, 313-323.CrossRefGoogle ScholarPubMed
Kaiser, H. F. The varimax criterion for analytic rotation in factor analysis. Psychometrika, 1958, 23, 187200.CrossRefGoogle Scholar
Kaiser, H. F. Three comments on oblimax. Psychometrika, 1973, 38, 609-609.CrossRefGoogle Scholar
Kaiser, H. F., & Caffrey, J. Alpha factor analysis. Psychometrika, 1965, 30, 1-14.CrossRefGoogle ScholarPubMed
Kaiser, H. F., & Horst, P. A score matrix for Thurstone’s box problem. Multivariate Behavioral Research, 1975, 10, 17-25.CrossRefGoogle ScholarPubMed
Lawley, D. N. The estimation of factor loadings by the method of maximum likelihood. Proceedings of the Royal Society of Edinburgh, 1940, 60, 64-82.CrossRefGoogle Scholar
Luenberger, D. G. Introduction to linear and non-linear programming, 1973, Reading, Mass.: Addison-Wesley.Google Scholar
McDonald, R. P., Swaminathan, H. A simple matrix calculus with applications to multivariate analysis. General Systems, 1973, 18, 37-54.Google Scholar
Mulaik, S. A. The foundations of factor analysis, 1972, New York: McGraw-Hill.Google Scholar
Saunders, D. R. The rationale for an “oblimax” method of transformation in factor analysis. Psychometrika, 1961, 26, 317-324.CrossRefGoogle Scholar
Silvey, S. D. The Lagrangian multiplier test. Annals of Mathematical Statistics, 1959, 30, 389-407.CrossRefGoogle Scholar