Common factor analysis versus principal component analysis: a comparison of loadings by means of simulations

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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 languageEnglish
Pages (from-to)299-321
JournalCommunications in Statistics: Simulation and Computation
Volume45
Issue number1
DOIs
Publication statusPublished - 2016

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