Encyclopedia of Personality and Individual Differences

2020 Edition
| Editors: Virgil Zeigler-Hill, Todd K. Shackelford

Exploratory Factor Analysis

  • Stella BollmannEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-3-319-24612-3_1302
  • 14 Downloads

The exploratory factor analysis is a statistical method that is used to identify latent variables that underlie a set of a larger number of manifest variables. The term exploratory factor analysis (EFA) stems from the need to differentiate from the confirmatory factor analysis (CFA). Historically, EFA is older than CFA and functions a little differently. What the two have in common is their usage: They examine existing sets of variables to determine their underlying factor model. The difference is that with CFA, a hypothesized factor model can be tested, while EFA develops unknown factor models.

What Is a Factor Model?

The factor model that underlies all factor analytic techniques is determined by some basic assumptions on psychometric measurements that were later summarized under the term classical test theory (Lord and Novick 1968). The main assumption is that the responses to questionnaire items are the result of an individual’s position on a latent variable. The latent variable...

This is a preview of subscription content, log in to check access.

References

  1. Anderson, T. (1958). An introduction to multivariate statistical analysis. New York: Wiley.Google Scholar
  2. Dinno, A. (2009). Exploring the sensitivity of horn’s parallel analysis to the distributional form of random data. Multivariate Behavioral Research, 44(3), 362–388.CrossRefPubMedPubMedCentralGoogle Scholar
  3. Hayton, J. C., Allen, D. G., & Scarpello, V. (2004). Factor retention decisions in exploratory factor analysis: A tutorial on parallel analysis. Organizational Research Methods, 7(2), 191–205.CrossRefGoogle Scholar
  4. Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179–185.CrossRefGoogle Scholar
  5. Lord, F. M., & Novick, M. R. (1968). Statistical theories of mental test scores. Reading: Addison-Wesley.Google Scholar
  6. Rencher, A., & Christensen W. (2012). Methods of multivariate analysis. Wiley.Google Scholar
  7. Spearman, C. (1904). General intelligence objectively determined and measured. The American Journal of Psychology, 15, 201–292.CrossRefGoogle Scholar
  8. Thurstone, L. (1935). The vectors of mind. Chicago: University of Chicago.Google Scholar
  9. Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41(3), 321–327.CrossRefGoogle Scholar
  10. Velicer, W. F., Eaton, C. A., & Fava, J. L. (2000). Construct explication through factor or component analysis: A review and evaluation of alternative procedures for determining the number of factors or components. In R. D. Goffin & E. Helmes (Eds.), Problems and solutions in human assessment: Honoring Douglas N. Jackson at seventy. (pp. 41–71). Boston: KluwerGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Universität ZürichZurichSwitzerland

Section editors and affiliations

  • Matthias Ziegler
    • 1
  1. 1.Humboldt Universität zu BerlinBerlinGermany