Abstract

Canonical correlation analysis (CCA) is a multivariate statistical technique that can be used in research scenarios in which there are several correlated outcomes of interest. Instead of separating analyses of these outcomes into several univariate analyses, a single application of CCA can capture the relationship across variables while honoring the fact that variables are correlated within sets. In CCA, the variability shared between two variable sets is partitioned into independent relationships and these relationships are characterized by the variables that contribute most in their formation. CCA is described here in detail, connecting the multivariate procedure to simple bivariate correlation and multiple regression and highlighting its position in the general linear model. After reviewing the procedure and important terminology, an accessible example is provided. The example is reproducible with data and syntax available online.