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Performance of Some Factor Analysis Techniques

Author

Listed:
  • D. F. Nwosu

    (Federal Polytechnic, Nekede)

  • V. U. Ekhosuehi

    (University of Benin)

  • J. I. Mbegbu

    (University of Benin)

Abstract

This paper is a study on three multivariate data sets using some factor analysis techniques in the literature. The techniques are: the principal factor method (PFM), maximum likelihood factor analysis (MLFA), the classical principal component method (PCM) and the refined principal component method (rPCM). The computations are carried out using the statistical package for the social sciences (SPSS), Minitab and MATLAB. Findings reveal that the rPCM generates results as that of the PCM and that the rPCM and the PCM are more appropriate for exploratory factor analysis than the PFM and MLFA as the PFM and the MLFA may fail to converge or may yield a Heywood case.

Suggested Citation

  • D. F. Nwosu & V. U. Ekhosuehi & J. I. Mbegbu, 2020. "Performance of Some Factor Analysis Techniques," Annals of Data Science, Springer, vol. 7(2), pages 209-242, June.
    • Handle: RePEc:spr:aodasc:v:7:y:2020:i:2:d:10.1007_s40745-020-00260-6
    DOI: 10.1007/s40745-020-00260-6
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    References listed on IDEAS

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    1. Shen, Haipeng & Huang, Jianhua Z., 2008. "Sparse principal component analysis via regularized low rank matrix approximation," Journal of Multivariate Analysis, Elsevier, vol. 99(6), pages 1015-1034, July.
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    3. Torokhti, Anatoli & Friedland, Shmuel, 2009. "Towards theory of generic Principal Component Analysis," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 661-669, April.
    4. Qi, Xin & Luo, Ruiyan & Zhao, Hongyu, 2013. "Sparse principal component analysis by choice of norm," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 127-160.
    5. Boik, Robert J., 2013. "Model-based principal components of correlation matrices," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 310-331.
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