Exploratory Factor Analysis (EFA) and Principal Component Analysis (PCA) represent distinct multivariate statistical techniques used for dimensionality reduction and latent structure identification within the field of psychometrics and advanced statistics. While PCA operates on an algebraic mechanism to construct orthogonal or oblique summary variables that maximize data variance without assuming hidden causal relationships, EFA employs a probabilistic model positing unmeasured latent factors as underlying causes of correlations among observed indicators. The fundamental theoretical distinction lies in their objective functions: PCA seeks optimal linear combinations for information compression via rotation-invariant components, whereas EFA aims to isolate specific common variances attributable to latent traits while separating unique error variance from the shared systematic structure.
Exploratory Factor Analysis (EFA) and Principal Component Analysis (PCA) represent distinct multivariate statistical techniques used for dimensionality reduction and latent structure identification w…