Exploratory Factor Analysis Link

: Numerical values representing the strength and direction of the relationship between an observed variable and a latent factor.

: Scores representing the amount of variance accounted for by each underlying factor. Factors with eigenvalues greater than 1.0 are often considered important. Exploratory Factor Analysis

: The proportion of variance in each observed variable that is explained by the extracted common factors. The EFA Step-by-Step Procedure Exploratory Factor Analysis EFA in SPSS : Numerical values representing the strength and direction

: Hidden variables that cannot be directly measured (e.g., "Intelligence" or "Extroversion") but influence the observable variables. : The proportion of variance in each observed

Exploratory Factor Analysis (EFA) is a multivariate statistical method used to uncover the underlying structure—or latent constructs—that explain correlation patterns between a set of observed variables. It is primarily used when researchers have no prior hypothesis about the data's nature and want to identify which variables group together to form common themes.