Introduction: The Basics of Principal Component Analysis

Principal component analysis is an appropriate procedure when you have obtained measures on a number of observed variables and want to develop a smaller number of variables (called principal components) that will account for most of the variance in the observed variables. The principal components can then be used as predictors or criterion variables in subsequent analyses.

A Variable Reduction Procedure

Principal component analysis is a variable reduction procedure. It is useful when you obtain data for a number of variables (possibly a large number of variables) and believe that there is redundancy among those variables. In this case, redundancy means that some of the variables are correlated with one another, possibly because they are measuring the same construct. Because of this redundancy, you believe that it should be possible to reduce the observed variables into a smaller number of principal components that account for most of the observed variance in the variables.