In General Linear Model (GLM) analyses, it is important to interpret structure coefficients, along with standardized weights, when evaluating variable contribution to observed effects. Although often used in canonical correlation analysis, structure coefficients are less frequently used in multiple regression and several other multivariate…

According to some researchers canonical correlation results should be interpreted in part by consulting redundancy coefficients (Rd). This paper, however, makes the case that Rd coefficients generally should not be interpreted. Rd coefficients are not multivariate. Furthermore, it makes little sense to interpret coefficients not optimized as part…

This paper illustrates how canonical correlation analysis can be employed to implement all the parametric tests that canonical methods subsume as special cases. The point is heuristic: all analyses are correlational, all apply weights to measured variables to create synthetic variables, and all yield effect sizes analogous to "r"…

This paper discusses the principles underlying discriminant analysis and constructs a simulated data set to illustrate its methods. Discriminant analysis is a multivariate technique for identifying the best combination of variables to maximally discriminate between groups. Discriminant functions are established on existing groups and used to…

Rectifies the unsatisfactory textbook treatment of the finite-sample proprieties of estimators of regression models with a lagged dependent variable and autocorrelated disturbances. Maintains that the bias of the ordinary least squares estimator is determined by the dynamic and correlation effects. (MJP)