All too often researchers perform a Multivariate Analysis of Variance (MANOVA) on their data and then fail to fully recognize the true multivariate nature of their effects. The most common error is to follow the MANOVA with univariate analyses of the dependent variables. One reason for the occurrence of such errors is the lack of clear pedagogical materials for identifying and testing the multivariate effects from the analysis. The current paper consequently reviews the fundamental differences between MANOVA and univariate Analysis of Variance and then presents a coherent set of methods for plumbing the multivariate nature of a given data set. A completely worked example using genuine data is given along with estimates of effect sizes and confidence intervals, and an example results section following the technical writing style of the American Psychological Association is presented. A number of issues regarding the current methods are also discussed.