A Decision Tree Approach to the Interpretation of Multivariate Statistical Techniques

Abstract

Abstract Multivariate statistical techniques have become increasingly popular over the last decade in many fields, especially management. However, management researchers, faculty, and students often do not have sufficient statistical training to appreciate the practical application of multivariate techniques. This article discusses the nature, power, and limitations of four of the most widely used multivariate techniques: factor analysis, MANOVA, multiple regression, and multiple discriminant analysis. Decision trees serve as road maps to guide users through the techniques and assist them in interpreting the results.