ABSTRACT
Principal component analysis (PCA) and factor analysis (FA) are multivariate statistical techniques that can be used to reduce a large number of interrelated variables to a smaller set of variables. The FA is a technique for expressing in the language of mathematics hypothetical variables by using a variety of common indicators that can be measured. The analysis is considered exploratory when the concern is with determining how many factors are necessary to explain the relationships among the indicators. The analysis is considered confirmatory when a preexisting theory directs the search. PCA is a method used to reduce a set of observed variables into a relatively small number of components that account for most of the observed variance. PCA is similar to other multivariate techniques such as discriminant analysis (DA) and canonical correlation (CANCORR). The techniques all involve linear combinations of variables on the basis of maximizing some statistical property.
