Is It Really Robust?: Reinvestigating the robustness of ANOVA against violations of the normal distribution assumption

Methodology

Volumn 6, Issue 4, 2010, Pages 147-151

  Schmider, Emanuel ^a   Ziegler, Matthias ^a   Danay, Erik ^a   Beyer, Luzi ^a   Bühner, Markus ^b  

^a HUMBOLDT UNIVERSITY   (Germany)

^b UNIVERSITY OF GRAZ   (Austria)

Author keywords

ANOVA; Assumption violation; High quality samples; Monte Carlo; Normal distribution

Indexed keywords

EID: 78649647534     PISSN: 16141881     EISSN: 16142241     Source Type: Journal    
DOI: 10.1027/1614-2241/a000016     Document Type: Article

References (15)

1. Cumming, G., & Finch, S. (2005). Inference by eye: Confidence intervals and how to read pictures of data. American Psychologist, 60, 170-180.
2. Edgington, E. S. (1995). Randomization tests. New York: M. Dekker.
3. Erdfelder, E., Faul, F., & Buchner, A. (1996). G*power: A general power analysis program. Behavior Research Methods, Instruments, & Computers, 28, 1-11.
4. Glass, G. V., Peckham, P. D., & Sanders, J. R. (1972). Consequences of failure to meet the assumptions underlying the fixed effects analysis of variance and covariance. Review of Educational Research, 42, 237-288.
5. Gough., B. (2003). GNU Scientific Library Reference Manual, (2nd ed.) Network Theory Ltd..
6. Harwell, M. R., Rubinstein, E. N., Hayes, W. S., & Olds, C. C. (1992). Summarizing Monte Carlo results in methodological research: The one- and two-factor fixed effects ANOVA cases. Journal of Educational and Behavioral Statistics, 17, 315-339.
7. Keselman, H. J., Algina, J., Lix, L. M., Wilcox, R. R., & Deering, K. N. (2008). A generally robust approach for testing hypotheses and setting confidence intervals for effect sizes. Psychological Methods, 13, 110-129.
8. Keselman, H. J., Huberty, C., Lix, L. M., Olejnik, S., Cribbie, R. A., Donahue, R. A. B., et al. (1998). Statistical practices of educational researchers: An analysis of their ANOVA, MANOVA, and ANCOVA analyses. Review of Educational Research, 68, 350-386.
9. Lix, L. M., Keselman, J. C., & Keselman, H. J. (1996). Consequences of assumption violations revisited: A quantitative review of alternatives to the one-way analysis of variance F test. Review of Educational Research, 66, 579-619.
10. Micceri, T. (1989). The unicorn, the normal curve, and other improbable creatures. Psychological Bulletin, 105, 156-166.
11. Park, S. K., & Miller, K. W. (1988). Random number generators: good ones are hard to find. Communications of the ACM, 31, 10.
12. Rothstein, H. R, Borenstein, M., Cohen, J., & Pollack, S. (1990). Statistical power analysis for multiple regression/correlation: A computer program. Educational and Psychological Measurement, 50, 819-830.
13. Tanizaki, H. (1997). Power comparison of non-parametric tests: Small-sample properties from Monte Carlo experiments. Journal of Applied Statistics, 24, 603-632.
14. Ware, M. E. (2000). Demonstrations and activities in the teaching of psychology, Vol. I, Erlbaum.
15. Yu, C. H. (2002). An overview of remedial tools for the violation of parametric test assumptions in the SAS system. Proceedings of 2002 Western Users of SAS Software Conference.