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Monte Carlo research has demonstrated that there are many applications of the rank transformation that result in an invalid procedure. Examples include the two dependent samples, the factorial analysis of variance, and the factorial analysis of covariance layouts. However, the rank transformation has been shown to be a valid and powerful test in the two independent samples layout. This study demonstrates that the rank transformation is also a robust and powerful alternative to the Hotellings T2 test when the data are on a likert scale. Hotelling's T2 is a specific case of multivariate analysis of variance (MANOVA) having one independent variable with two levels and multiple dependent variables. It is the mul­ tivariate analogue of the independent samples t-test. In­ stead of examining differences in means between two samples on one dependent variable, Hotelling's T2 tests for the equality of centroids (mean vectors) between two groups. T2 is a parametric test having the usual assump­ tions that theoretically must be met for its valid applica­ tion. For example, T2 assumes that the data of the underly­ ing distributions is normally distributed, (i.e., assumes multivariate normality), that there is independence of ob­ servations, and that the variance/covariance matrices are equal. The Rank Transform (RT) is a set of nonpara- metric-like (and sometimes referred to as conditionally distribution-free) procedures. The RT is performed by re­ placing original observations with their respective ranks, computing a parametric statistic on these ranks, and then referring the test statistic to the usual table of critical val­ ues (Conover & Iman, 1981). It has been suggested that RT procedures have robustness and power properties simi­ lar to their parametric counterparts when normal theory assumptions are met, and have superior robustness and power properties when assumptions are not met (Conover & Iman, 1981). Purpose of Current Study The purpose of this study is to examine the ro­ bustness and power properties of the two independent sample Hotelling's T2 test and the RT using Monte Carlo techniques with samples drawn from real data sets that are

Hotelling's T2 vs. the Rank Transform With Real Likert Data