Nonparametric Tests of Interaction in Experimental Design

Abstract

Until recently the design of experiments in the behavioral and social sciences that focused on interaction effects demanded the use of the parametric analysis of variance. Yet, researchers have been concerned by the presence of nonnormally distributed variables. Although nonparametric statistics are recommended in these situations, researchers often rely on the robustness of parametric tests. Further, often it is assumed that nonparametric methods lack statistical power and that there is a paucity of techniques in more complicated research designs, such as in testing for interaction effects. This paper reviewed (a) research in the past decade and a half that addressed concerns in selecting parametric and nonparametric statistics and (b) 10 recently developed nonparametric techniques for the testing of interactions in experimental design. The review shows that these new techniques are robust, powerful, versatile, and easy to compute. An application of selected nonparametric techniques on fabricated data is provided.