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Exploratory factor analysis (EFA) is a multivariate statistical technique to model the covariance structure of the observed variables by three sets of parameters: (a) factor loadings associated with latent (i.e., unobserved) variables called factors, (b) residual variances called unique variances, and (c) factor correlations. EFA aims at explaining the relationship of many observed variables by a relatively small number of factors. Thus, EFA is considered one of the data reduction techniques. Historically, EFA dates back to Charles Spearman's work in 1904, and the theory behind EFA has been developed along with the psychological theories of intelligence, such as L. L. Thurstone's multiple factor model. Today, EFA is among the most frequently used statistical techniques by researchers in the social sciences and education.

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