One of the central messages of this dissertation is that (a) unequal variances may be more prevalent than typically recognized in educational and policy research, and (b) when considering tests of equal variances, one needs to be cautious about what is being referred to as “Levene’s test” because Levene’s test is actually a family of techniques. Depending on which of the Levene tests that are being implemented, and particularly the Levene’s test based on means which is found in widely used software like SPSS, one may be using a statistical technique that is as bad (if not worse) than the F test which the Levene test was intended to replace. The primary goals of this dissertation are to (a) demonstrate that the current statistical practice of testing for equality of variances in hypothesis testing (as prescribed by textbooks and statistical software programs) is insufficient, (b) introduce a new non-parametric statistical test for homogeneity of variances, and (c) investigate the Type I error rate and power of the non-parametric Levene test with that of the median version of the Levene test. Under all conditions investigated, both tests maintained their nominal Type I error rates. As population distributions become more skewed, the non-parametric Levene test becomes more powerful than the median version of the Levene test. These results promise to impact applied statistical practice by informing researchers about the relative efficiencies of the two tests. This dissertation concludes with remarks about the implications of the findings, and the future work that has arisen from the results.