- Definition from Wikipedia
Canonical correlation
In statistics, canonical correlation analysis, introduced by Harold Hotelling, is a way of making sense of cross-covariance matrices.
A typical use for canonical correlation in the psychological context is to take a two sets of variables and see what is common amongst the two sets. For example you could take two well established multidimensional personality tests such as the MMPI and the NEO. By seeing how the MMPI factors relate to the NEO factors, you could gain insight into what dimensions were common between the tests and how much variance was shared. For example you might find that an extraversion or neuroticism dimension accounted for a substantial amount of shared variance between the two tests.
One can also use canonical correlation analysis to produce a model
equation which relates two sets of variables, for example a set of
performance measures and a set of explanatory variables, or a set of
outputs and set of inputs. Constraint restrictions can be imposed on
such a model to ensure it reflects theoretical requirements or
intuitively obvious conditions. This type of model is known as a
maximum correlation model.
Table of Contents
