Abstract
Common factor analysis (CFA) and principal component analysis (PCA) are widely used multivariate techniques. Using simulations, we compared CFA with PCA loadings for distortions of a perfect cluster configuration. Results showed that nonzero PCA loadings were higher and more stable than nonzero CFA loadings. Compared to CFA loadings, PCA loadings correlated weakly with the true factor loadings for underextraction, overextraction, and heterogeneous loadings within factors. The pattern of differences between CFA and PCA was consistent across sample sizes, levels of loadings, principal axis factoring versus maximum likelihood factor analysis, and blind versus target rotation.
Original language | English |
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Pages (from-to) | 299-321 |
Journal | Communications in Statistics: Simulation and Computation |
Volume | 45 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2016 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
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Supplementary materials for the article: Common factor analysis versus principal component analysis: a comparison of loadings by means of simulations
de Winter, J. C. F. (Creator) & Dodou, D. (Creator), TU Delft - 4TU.ResearchData, 9 Sept 2019
DOI: 10.4121/UUID:84DD2409-CAE0-43A5-AD5E-A7DBC771F0E3
Dataset/Software: Dataset