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.