Computer-generated Monte Carlo techniques were used to assess the relative power of
Wilcoxon's rank-sum test and the independent means t test under a certain mixed normal
distribution. Results showed a very largepoweradvantagefor the Wilcoxon statistic. This
indicates that the very large advantages promised by asymptotic theory may be obtained
with samples of sizes commonly encountered in social science research.
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References
1.
Bhattacharya, C.G. (1967) "A simple method of resolution of a distribution into Gaussian components." Biometrics23: 115-135.
2.
Blair, R.C. (1979) "A comment on the relative usefulness of the two independent means t-test and Wilcoxon's rank-sum test for the analysis of educational data." Florida J. of Educ. Research21: 97-110.
3.
——— J.J. Higgins, and W.D.S. Smitley (1979) "On the relative power of the U and t-tests." Presented at the meeting of the Florida Educational Research Association , Daytona, January.
4.
Blair, R.C. and S.W. Phillippy (1979) "A reexamination of the robustness of the two independent means t-test to population normality " Presented at the meeting of the Florida Educational Research Association, Daytona, January.
5.
Boneau, C.A. (1962) "A comparison of the power of the U and t-tests ." Psych. Rev.69. 246-256.
6.
——— ( 1960) "The effects of violations of assumptions underlying the t-test." Psych. Bull.5749-64
7.
Bradley, J.V. ( 1977) "A common situation conducive to bizarre distribution shapes." Amer. Statistician31: 147-150.
8.
Glass, G.V. and J.C. Stanley (1970) Statistical Methods in Education and Psychology . Englewood Cliffs, NJ. Prentice-Hall
9.
Hodges, J.L. and E.L. Lehmann (1956) "The efficiency of some nonparametric competitors of the t-test." Annals of Mathematical Statistics27. 324-335.
10.
International and Mathematical Statistical Libraries (1977) Library 1. Reference Manual Volume 1. Houston, TX. Author
11.
Johnson, D.E., S.A. McGuire, and G.A. Milliken (1978) "Estimating σ2 in the presence of outliers " Technometrics20. 441-455
12.
Medgyessy, R. (1953) "Valoszinuseg-eloszlasfuggvenyek Kevere-kenek Felbontasa Osszetevoire " Hungarian Academy of Science Communications, Institute of Applied Mathematics 2. 165-177
13.
Neave, H.R. andC.W.J. Granger (1968) "A Monte Carlo study comparing various two-sample tests for differences in mean " Technometrics10509-522.
14.
Quant, R.E. (1972) "A new approach to estimating switching regressions ." J. of the Amer Stat Assn67306-310.
15.
——— and J.B. Ramsey (1978) "Estimating mixtures of normal distributions and switching regressions " J of the Amer Stat. Assn73. 730-738
16.
Ramsey, J.B. ( 1975) "Mixtures of distributions and maximum likelihood estimation of parameters contained in finitely bounded compact spaces " Econometrics Workshop Paper 7501, Michigan State University
17.
Toothaker, L.E. (1972) "An empirical investigation of the permutation t-test compared to Student's t-test and the Mann-Whitney U test." Madison: University of Wisconsin. (ERIC Document Reproduction Service EDO75491)
18.
Yakowitz, S.J. (1970) "Unsupervised learning and the identification of finite mixtures," pp. 330-338 in IEEE Transactions on Information Theory, IT-16.
19.
Young, T.Y. and G. Coraluppi (1970) "Stochastic estimation of a mixture of normal density functions using information criterion," pp. 258-263 in IEEE Transactions on Information Theory, IT-16.
