A note on the influence of outliers on parametric and nonparametric tests

Journal of General Psychology

Volumn 121, Issue 4, 1994, Pages 391-401

  Zimmerman, Donald W ^a  

^a CARLETON UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0009308007     PISSN: 00221309     EISSN: 19400888     Source Type: Journal    
DOI: 10.1080/00221309.1994.9921213     Document Type: Article

References (12)

1. Barnetl, V., & Lewis, T. (1978). Outliers in statistical data. New York: Wiley.
2. Box, G. E. P., & Muller, M. (1958). A note on the generation of random normal deviates. Annals of Mathematical Statistics. 29, 610-611.
3. Bradley, J. V. (1977). A common situation conducive to bizarre distribution shapes. American Statistician. 31, 147-150.
4. Conover, W. J., & Iman, R. L. (1981). Rank transformations as a bridge between parametric and nonparametric statistics. The American Statistician, 35, 124-129.
5. Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J., & Stahel, W. A. (1986). Robust statistics: The approach based on influence functions. New York: Wiley.
6. Hawkins, D. M. (1980). Identification of outliers. London: Chapman & Hall.
7. Hodges, J., & Lehmann, E. (1956). The efficiency of some nonparametric competitors of the l test. Annals of Mathematical Statistics. 27, 324-335.
8. Neave, H. R., & Granger, C. W. J. (1968). A Monte Carlo study comparing various two-sample tests for differences in means. Technometrics, 10, 509-522.
9. Randies, R. H., & Wolfe, D. A. (1979). Introduction to the theory of nonparametric statistics. New York: Wiley.
10. Rasmussen, J. L. (1985). The power of Student’s t and Wilcoxon’s W statistics: A comparison. Evaluation Review, 9, 505-510.
11. Rasmussen, J. L. (1986). An evaluation of parametric and nonparametric tests on modified and non-modified data. British Journal of Mathematical and Statistical Psychology, 39, 213-220.
12. Zimmerman, D. W, & Zumbo, B. D. (1993). The relative power of parametric and nonparametric statistical methods. In G. Keren & C. Lewis (Eds.), A handbook for data analysis in the behavioral sciences: Methodological issues (pp. 481-517). Hillsdale, NJ: Erlbaum.