Performance of Some Factor Analysis Techniques

D. F. Nwosu, V. U. Ekhosuehi () and J. I. Mbegbu
Additional contact information
D. F. Nwosu: Federal Polytechnic, Nekede
V. U. Ekhosuehi: University of Benin
J. I. Mbegbu: University of Benin

Annals of Data Science, 2020, vol. 7, issue 2, No 2, 209-242

Abstract: 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.

Keywords: Exploratory factor analysis; Heywood case; MATLAB; Minitab; Multivariate analysis; SPSS (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40745-020-00260-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:aodasc:v:7:y:2020:i:2:d:10.1007_s40745-020-00260-6

Ordering information: This journal article can be ordered from
https://www.springer ... gement/journal/40745

DOI: 10.1007/s40745-020-00260-6

Access Statistics for this article

Annals of Data Science is currently edited by Yong Shi

More articles in Annals of Data Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().