title: Conducting and interpreting canonical correlation analysis in personality research: a user-friendly primer.
abstract: The purpose of this article is to reduce potential statistical barriers and open doors to canonical correlation analysis (CCA) for applied behavioral scientists and personality researchers. CCA was selected for discussion, as it represents the highest level of the general linear model (GLM) and can be rather easily conceptualized as a method closely linked with the more widely understood Pearson r correlation coefficient. An understanding of CCA can lead to a more global appreciation of other univariate and multivariate methods in the GLM. We attempt to demonstrate CCA with basic language, using technical terminology only when necessary for understanding and use of the method. We present an entire example of a CCA analysis using SPSS (Version 11.0) with personality data.
authors.affiliation: Counseling Psychology Program, University of Texas at Austin, USA.
authors.forename: Alissa
authors.initials: A
authors.lastname: Sherry
authors.forename: Robin K
authors.initials: RK
authors.lastname: Henson
mesh_terms: D000704:Analysis of Variance; D035841:Behavioral Research / Q000706:statistics & numerical data; D006801:Humans; D010552:Personality Assessment / Q000706:statistics & numerical data*; D014481:United States