Four Covariance Structure Models for Canonical Correlation Analysis: A COSAN Modeling Approach

Abstract

The mathematical connection between canonical correlation analysis (CCA) and covariance structure analysis was first discussed through the Multiple Indicators and Multiple Causes (MIMIC) approach. However, the MIMIC approach has several technical and practical challenges. To address these challenges, a comprehensive COSAN modeling approach is proposed. Specifically, we define four COSAN-CCA models to correspond with four possible combinations of the data to be analyzed and the unique parameters to be estimated. In terms of the data, one can analyze either the unstandardized or standardized variables. In terms of the unique parameters, one can estimate either the weights or loadings. Besides the unique parameters of each COSAN-CCA model, all four COSAN-CCA models also estimate the canonical correlations as their common parameters. Taken together, the four COSAN-CCA models provide the correct point estimates and standard error estimates for all commonly used CCA parameters. Two numeric examples are used to compare the standard error estimates obtained from the MIMIC approach and the COSAN modeling approach. Moreover, the standard error estimates from the COSAN modeling approach are validated by a simulation study and the asymptotic theory. Finally, software implementation and future extensions are discussed.