: 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 (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. Exploratory Factor Analysis
: 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
: The actual data collected, such as individual survey questions or test scores. It is primarily used when researchers have no
: Hidden variables that cannot be directly measured (e.g., "Intelligence" or "Extroversion") but influence the observable variables.
: 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