Abstract
Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) are both variable reduction techniques
and sometimes mistaken as the same statistical method. However, there are distinct differences between PCA and
EFA. Similarities and differences between PCA and EFA will be examined. Examples of PCA and EFA with
PRINCOMP and FACTOR will be illustrated and discussed.
Introduction
You want to run a regression analysis with the data you’ve collected. However, the measured (observed) variables
are highly correlated. There are several choices

 use some of the measured variables in the regression analysis (explain less variance)

 create composite scores by summing measured variables (explain less variance)

 create principal component scores (explain more variance).
The choice seems simple. Create principal component scores, uncorrelated linear combinations of weighted
observed variables, and explain a maximal amount of variance in the data.
What if you think there are underlying latent constructs in the data? Latent constructs

 cannot be directly measured

 influence responses on measured variables

 include unreliability due to measurement error.
Observed (measured) variables could be linear combinations of the underlying factors (estimated underlying latent
constructs and unique factors). EFA describes the factor structure of your data.