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Erschienen in:
24.04.2020
Performance of Some Factor Analysis Techniques
Erschienen in: Annals of Data Science | Ausgabe 2/2020
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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.