Methods of Multivariate Commonality Analysis.

Advantages of the use of multivariate commonality analysis are discussed and a small data set is used to illustrate the analysis and as a model to enable readers to conduct such an analysis. A noteworthy advantage of commonality analysis is that commonality honors the relationships among variables by determining the degree to which predictors in a set share variance with the criterion variables. Since commonality indicates the extent of overlap of the variables, it is especially useful in the behavioral sciences where predictor variables are often correlated with each other. Commonality also reinforces the recognition that canonical analysis is the most general case of parametric significance testing. The disadvantage that there are no statistical significance tests for commonality analyses is outweighed by the advantages. The illustrative example uses a hypothetical data set of 22 observations, 3 predictor variables, and 2 criterion variables. Seven data tables are provided. (SLD)