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Exploratory Factor Analysis (EFA) in Quantitative Researches and Practical Considerations

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  • Sürücü, Lütfi
  • YIKILMAZ, İbrahim
  • MASLAKÇI, Ahmet

Abstract

Explanatory factor analysis (EFA) is a multivariate statistical method frequently used in quantitative research and has begun to be used in many fields such as social sciences, health sciences and economics. With EFA, researchers focus on fewer items that explain the structure, instead of considering too many items that may be unimportant and carry out their studies by placing these items into meaningful categories (factors). However, for over sixty years, many researchers have made different recommendations about when and how to use EFA. Differences in these recommendations confuse the use of EFA. The main topics of discussion are sample size, number of items, item extraction methods, factor retention criteria, rotation methods and general applicability of the applied procedures. The abundance of these discussions and opinions in the literature makes it difficult for researchers to decide which procedures to follow in EFA. For this reason, it would be beneficial for researchers to gather different information about the general procedures (sample number, rotation methods, etc.) in the use of EFA. This paper aims to provide readers with an overview of what procedures to follow when implementing EFA and share practical information about the latest developments in methodological decisions in the EFA process. It is considered that the study will be an important guide for the researchers in the development of clear decision paths in the use of EFA, with the aspect of presenting the most up-to-date information collectively.

Suggested Citation

  • Sürücü, Lütfi & YIKILMAZ, İbrahim & MASLAKÇI, Ahmet, 2022. "Exploratory Factor Analysis (EFA) in Quantitative Researches and Practical Considerations," OSF Preprints fgd4e, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:fgd4e
    DOI: 10.31219/osf.io/fgd4e
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    1. I. T. Jolliffe, 1972. "Discarding Variables in a Principal Component Analysis. I: Artificial Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 21(2), pages 160-173, June.
    2. Haitovsky, Yoel, 1969. "Multicollinearity in Regression Analysis: Comment," The Review of Economics and Statistics, MIT Press, vol. 51(4), pages 486-489, November.
    3. Wayne Velicer, 1976. "Determining the number of components from the matrix of partial correlations," Psychometrika, Springer;The Psychometric Society, vol. 41(3), pages 321-327, September.
    4. J. C.F. de Winter & D. Dodou, 2012. "Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(4), pages 695-710, August.
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