Multivariate data analysis in empirical research. A look on the bright side

The interpretive benefits of employing multivariate analysis methods on experimental data with more than one dependent variable are described heuristically and illustrated on a set of data from a simply designed experiment in physiological psychology. Multivariate analysis of variance (MANOVA) is performed on the 9 dependent variables contained in the sample data and on the four composites derived from a principal components analysis (PCA) of the variability of the nine. A linear discriminant analysis (LDA) is conducted following both MANOVA results, and 5 methods of determining the "important" dependent variables in the experimental-control group difference are presented and discussed in terms of the data at hand.