Process / pipelinedimension-reduction

Factor Analysis

Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate data.

Apply with StatMindSoonVideoSoon

Read the full method

Members only

Sign in with a free account to read this section.

Sign in

Sources

  1. Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press. DOI: 10.1037/10366-000
  2. Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. DOI: 10.1007/BF02289343
  3. Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141–151. DOI: 10.1177/001316446002000116

Related methods

Referenced by

ScholarGateFactor Analysis (Exploratory and Confirmatory Factor Analysis). Retrieved 2026-06-04 from https://scholargate.app/en/research-statistics/factor-analysis