The aim of canonical correlation analysis is to find the best linear combination between two multivariate datasets that maximizes the correlation coefficient between them. This is particularly useful to determine the relationship between criterion measures and the set of their explanatory factors. This technique involves, first, the reduction of the dimensions of the two multivariate datasets by projection, and second, the calculation of the relationship (measured by the correlation coefficient) between the two projections of the datasets.