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Rotation to Simple Loadings Using Component Loss Functions: The Oblique Case

Published online by Cambridge University Press:  01 January 2025

Robert I. Jennrich*
Affiliation:
University of California at Los Angeles
*
Requests for reprints should be sent to Robert I. Jennrich, Department of Mathematics, University of California, Los Angeles, CA 90095, USA. E-mail: rij@stat.ucla.edu

Abstract

Component loss functions (CLFs) similar to those used in orthogonal rotation are introduced to define criteria for oblique rotation in factor analysis. It is shown how the shape of the CLF affects the performance of the criterion it defines. For example, it is shown that monotone concave CLFs give criteria that are minimized by loadings with perfect simple structure when such loadings exist. Moreover, if the CLFs are strictly concave, minimizing must produce perfect simple structure whenever it exists. Examples show that methods defined by concave CLFs perform well much more generally. While it appears important to use a concave CLF, the specific CLF used is less important. For example, the very simple linear CLF gives a rotation method that can easily outperform the most popular oblique rotation methods promax and quartimin and is competitive with the more complex simplimax and geomin methods.

Type
Original Paper
Copyright
Copyright © 2006 The Psychometric Society

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Footnotes

The author would like to thank the editor and three reviewers for helpful suggestions and for identifying numerous errors.

References

Browne, M.W. (2001). An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research, 36, 111150.CrossRefGoogle Scholar
Carroll, J.B. (1953). An analytical solution for approximating simple structure in factor analysis. Psychometrika, 18, 2328.CrossRefGoogle Scholar
Cureton, E.E., & Mulaik, S.A. (1975). The weighted varimax rotation and the promax rotation. Psychometrika, 40, 183185.CrossRefGoogle Scholar
Eber, H.W. (1966). Toward oblique simple structure: Maxplane. Multivariate Behavioral Research, 1, 112125.CrossRefGoogle Scholar
Harman, H.H. (1976). Modern factor analysis, (3rd ed.). Chicago: University of Chicago Press.Google Scholar
Hendrickson, A.E., & White, P.O. (1964). A quick method for rotation to oblique simple structure. British Journal of Statistical Psychology, 17, 6570.CrossRefGoogle Scholar
Holzinger, K.J., & Swineford, F. (1939). A study in factor analysis: The stability of the bi-factor solution, Supplementary Educational Monographs, Vol. 48. Chicago: University of Chicago, Department of Education.Google Scholar
Jennrich, R.I. (2004a). Rotation to simple loadings using component loss functions: The orthogonal case. Psychometrika, 69, 257273.CrossRefGoogle Scholar
Jennrich, R.I. (2004b). Derivative free gradient projection algorithms for rotation. Psychometrika, 69, 475480.CrossRefGoogle Scholar
Jennrich, R.I., & Sampson, P.F. (1966). Rotation for simple loadings. Psychometrika, 31, 313323.CrossRefGoogle ScholarPubMed
Kaiser, H.F. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23, 187200.CrossRefGoogle Scholar
Katz, J.O., & Rohlf, F.J. (1974). FUNCTIONPLANE—A new approach to simple structure rotation. Psychometrika, 39, 3751.CrossRefGoogle Scholar
Kiers, H.A.L. (1994). SIMPLIMAX: Oblique rotation to an optimal target with simple structure. Psychometrika, 59, 567579.CrossRefGoogle Scholar
Rozeboom, W.W. (1991). Theory and practice of analytic hyperplane optimization. Multivariate Behavioral Research, 26, 179197.CrossRefGoogle ScholarPubMed
Thurstone, L.L. (1935). Vectors of the mind, Chicago: University of Chicago Press.Google Scholar
Thurstone, L.L. (1947). Multiple factor analysis, Chicago: University of Chicago Press.Google Scholar
Yates, A. (1987). Multivariate exploratory data analysis: A perspective on exploratory factor analysis, Albany, NY: State University of New York Press.Google Scholar