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A Monte Carlo Study of the Robustness and Power Associated with Selected Tests of Variance Equality when Distributions are Non-Normal and Dissimilar in Form

When selecting a method for testing variance equality, a researcher should select a method which is robust to distribution non-normality and dissimilarity. The method should also possess sufficient power to ascertain departures from the equal variance hypothesis. This Monte Carlo study examined the robustness and power of five tests of variance equality under specific conditions. The tests examined included one procedure proposed by O'Brien (1978), two by O'Brien (1979), and two by Conover, Johnson, and Johnson (1981). Specific conditions included assorted combinations of the following factors: k=2 and k=3 groups, normal and non-normal distributional forms, similar and dissimilar distributional forms, and equal and unequal sample sizes. Under the k=2 group condition, a total of 180 combinations were examined. A total of 54 combinations were examined under the k=3 group condition. The Type I error rates and statistical power estimates were based upon 1000 replications in each combination examined. Results of this study suggest that when sample sizes are relatively large, all five procedures are robust to distribution non-normality and dissimilarity, as well as being sufficiently powerful.

Identiferoai:union.ndltd.org:unt.edu/info:ark/67531/metadc332130
Date08 1900
CreatorsHardy, James C. (James Clifford)
ContributorsBrookshire, William K., Haynes, Jack Read, Young, Jon I., Pavur, Robert J.
PublisherUniversity of North Texas
Source SetsUniversity of North Texas
LanguageEnglish
Detected LanguageEnglish
TypeThesis or Dissertation
Formatvi, 90 leaves:ill., Text
RightsPublic, Hardy, James C. (James Clifford), Copyright, Copyright is held by the author, unless otherwise noted. All rights reserved.

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