Hypothetical data sets are used to demonstrate how canonical correlation methods subsume other commonly utilized parametric methods. Analysis of variance, analysis of covariance, multiple analysis of variance, and multiple analysis of covariance are heavily used by educational researchers. It is concluded that researchers would do well to consider using general linear model (GLM) techniques as opposed to analysis of variance and its analogs. GLM and analysis of variance are equivalent when the ways in an analysis of variance design each have exactly two levels. In other cases, the GLM analytic approach yields more specific information regarding effects, and greater power against Type II error, because the comparisons are planned a priori rather than post hoc. Multivariate canonical correlation methods are superior to univariate techniques in providing important information on the interrelationships among variables. Furthermore, multiple univariate procedures tend to inflate the probability of a Type I error. (GDC)