An empirical study of the type i error rate and power for some selected normalyffleory and nonparameiric tests of the independence of tto sets of variables

Normal-theory tests of the hypothesis of no relationship among two sets of variables require assumptions of independence, homoscedasticity and normality. If, however, the assumption of normality is not tenable, there are few guidelines for properly using these tests. Historically, the lack of a comprehensive hypothesis-testing framework in the nonparametric case has provided few alternatives to normal-theory procedures. Fortuna‘—’y this situation has charged with the introduction of nonparametric, general linear model-based tests that can be used with existing computing packages. Multivar iate-norparametric tests due to Puri and Sen (1969, 1971, 1985) and Conover and Iman (1981) are outlined, and the results of a simulation study of the.