Each set may be considered a latent variable based on measured indicator variables in its set. The canonical correlation is optimized such that the linear correlation between the two latent variables is maximized. Whereas multiple regression is used for many-to-one relationships, canonical correlation is used for many-to-many relationships. There may be more than one such linear correlation relating the two sets of variables, with each such correlation representing a different dimension by which the independent set of variables is related to the dependent set. The purpose of canonical correlation is to explain the relation of the two sets of variables, not to model the individual variables. For each canonical variate one can also assess how strongly it is related to measured variables in its own set, or the set for the other canonical variate. Wilks's lambda is commonly used to test the significance of canonical correlation.Īnalogous to ordinary correlation, canonical correlation squared is the percent of variance in the dependent set explained by the independent set of variables along a given dimension (there may be more than one). In addition to asking how strong the relationship is between two latent variables, canonical correlation is useful in determining how many dimensions are needed to account for that relationship. Canonical correlation spss version 25 Pc#.Canonical correlation spss version 25 pdf#.
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