The purpose of principal component analysis is to derive a small number of independent linear combinations (principal components) of a set of measured variables that capture as much of the variability in the original variables as possible. Principal component analysis is an exploratory data analysis tool and is also used for making predictive models.

For factor analysis, see the Factor Analysis chapter in the Consumer Research book.