Interpreting CCA Results
- Interpreting the results of CCA involves examining the canonical correlations, the canonical variates, and the loadings of the variables on the canonical variates.
- The canonical correlations indicate the strength of the relationship between the two sets of variables. A high canonical correlation suggests a strong relationship between the two sets of variables.
- The canonical variates are the vectors that best represent the relationship between the two sets of variables. They are interpreted in a similar way to factors in factor analysis.
- The loadings of the variables on the canonical variates indicate the contribution of each variable to the canonical variate. They are interpreted in a similar way to factor loadings in factor analysis.
What is Canonical Correlation Analysis?
Canonical Correlation Analysis (CCA) is an advanced statistical technique used to probe the relationships between two sets of multivariate variables on the same subjects. It is particularly applicable in circumstances where multiple regression would be appropriate, but there are multiple intercorrelated outcome variables. CCA identifies and quantifies the associations among these two variable groups. It computes a set of canonical variates, which are orthogonal linear combinations of the variables within each group, that optimally explain the variability both within and between the groups.