Canonical Correlation Analysis Multiple Linear Regression Viewpoints, 2010, Vol. 36(2) 29 Canonical Correlation Analysis: A Step-by-Step Example in Commonly Available Software

Canonical Correlation Analysis (CCA) can be conceptualized as a multivariate regression involving multiple outcome variables. CCA compares two sets of variables and is the second-most general application of the General Linear Model (GLM) following Structural Equation Modeling. Structural Equation Modeling software have made conducting CCA feasible for researchers in numerous and disparate disciplines. SPSS and AMOS are two commonly used statistical software packages and both can conduct CCAs. AMOS is particularly useful because it enables statistical significance testing of individual canonical correlations. This article provides background knowledge of CCA as part of the GLM and provides step-by-step instructions for conducting a CCA in AMOS. he general linear model (GLM) comprises an overwhelming majority of parametric statistical procedures. Regression is the univariate GLM (Cohen, 1968) and canonical correlation analysis (CCA) is the multivariate GLM (Knapp, 1978). The GLM includes the t-test, ANOVA and other OVA procedures, descriptive discriminate analysis, CCA and structural equation modeling (SEM