Common Factor Analysis versus Principal Component Analysis: A Comparison of Loadings by Means of Simulations. Issue 1 (2nd January 2016)

• Record Type: Journal Article
• Authors: de Winter, Joost C. F.; Dodou, Dimitra
• Is Part Of: Communications in statistics. Volume 45:Issue 1(2016)
• Page Start: 299
• Page End: 321
• Publication Date: 2016-01-02
• Subjects: Data reduction -- Exploratory factor analysis -- Latent variables.; 62H25; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2013.862274
• Languages: English
• ISSNs: 0361-0918

Abstract

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.