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Parametric Alternatives to the Student T Test under Violation of Normality and Homogeneity of Variance

Abstract

Introductory statistics textbooks in psychology, education, and social sciences have contributed to the belief that nonparametric tests, such as the Wilcoxon-Mann-Whitney test, are effective against violations of both normality and homogeneity of variance. The present paper emphasizes that, although rank methods often are useful when samples are obtained from heavy-tailed, nonnormal distributions, they are influenced by unequal variances just like parametric tests. Computer programs are now available to perform modified t tests based on unequal sample variances, in which degrees of freedom and critical values are altered from sample to sample. These procedures, although neglected for many years because they are computationally complex, are far more effective than nonparametric methods in protecting against violation of homogeneity of variance.

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References

  1. Blair R. C., Higgins J. J. (1980) A comparison of the power of Wilcoxon's rank-sum statistic to that of Student's t under various non-normal distributions. Journal of Educational Statistics, 5, 309–335.
  2. Crossref
  3. Google Scholar
  4. Box G. E. P., Muller M. (1958) A note on the generation of random normal deviates. Annals of Mathematical Statistics, 29, 610–611.
  5. Crossref
  6. Google Scholar
  7. Cochran W. G., Cox G. M. (1957) Experimental designs. (2nd ed.) New York: Wiley.
  8. Google Scholar
  9. Conover W. J. (1980) Practical nonparametric statistics. (2nd ed.) New York: Wiley.
  10. Google Scholar
  11. Conover W. J., Iman R. L. (1981) Rank transformations as a bridge between parametric and nonparametric statistics. The American Statistician, 35, 124–129.
  12. Crossref
  13. Web of Science
  14. Google Scholar
  15. Freund J. E., Walpole R. E. (1987) Mathematical statistics. Englewood Cliffs, NJ: Prentice-Hall.
  16. Google Scholar
  17. Gibbons J. D., Chakraborti S. (1991) Comparisons of the Mann-Whitney, Student's t, and alternate t tests for means of normal distributions. Journal of Experimental Education, 258–267.
  18. Google Scholar
  19. Glass G., Peckham P, Sanders J. (1972) Consequences of failure to meet assumptions underlying the fixed effects analysis of variance and covariance. Review of Educational Research, 42, 237–288
  20. Crossref
  21. Web of Science
  22. Google Scholar
  23. Hampel F. R., Ronchetti E. M., Rousseeuw P. J., Stahel W. A. (1986) Robust statistics: The approach based on influence functions. New York: Wiley.
  24. Google Scholar
  25. Hodges J., Lehmann E. (1956) The efficiency of some nonparametric competitors of the t-test. Annals of Mathematical Statistics, 21, 324–335.
  26. Crossref
  27. Google Scholar
  28. Hopkins K. D., Glass G. V., Hopkins B. R. (1987) Basic statistics for the behavioral sciences. (2nd ed.) Englewood Cliffs, NJ: Prentice-Hall.
  29. Google Scholar
  30. Howell D. C. (1987) Statistical methods for psychology. (2nd ed.) Boston, MA: Duxbury Press.
  31. Google Scholar
  32. Hsu P. L. (1938) Contributions to the theory of “Student's” t-test as applied to the problem of two samples. Statistical Research Memoirs, 2, 1–24.
  33. Google Scholar
  34. Keselman H. J., Rogan J. C., Feir-Walsh B. J. (1977) An evaluation of some nonparametric and parametric tests for location equality. British Journal of Mathematical and Statistical Psychology, 30, 213–221.
  35. Crossref
  36. Google Scholar
  37. Kirk R. E. (1982) Experimental design. (2nd ed.) Monterey, CA: Brooks/Cole.
  38. Google Scholar
  39. Lewis P. A. W., Goodman A. S., Miller J. M. (1969) A pseudorandom number generator for the System 360. IBM Systems Journal, 8, 136–146.
  40. Crossref
  41. Google Scholar
  42. Lewis P. A. W., Orav E. J. (1989) Simulation methodology for statisticians, operations analysts, and engineers. Vol. 1. Pacific Grove, CA: Wadsworth.
  43. Google Scholar
  44. Loftus G. R., Loftus E. F. (1988) Essence of statistics. (2nd ed.) Monterey, CA: Brooks/Cole.
  45. Google Scholar
  46. Marsaglia G., Bray T. A. (1964) A convenient method for generating normal variables. SIAM Review, 6, 260–264.
  47. Crossref
  48. Web of Science
  49. Google Scholar
  50. Micceri T. (1989) The unicorn, the normal curve, and other improbable creatures. Psychological Bulletin, 105, 156–166.