Data Cleaning Basics: Best Practices in Dealing with Extreme Scores

References 

1. Osborne JW. Sweating the small stuff in educational psychology: how effect size and power reporting failed to change from 1969 to 1999, and what that means for the future of changing practices. Educ Psychol. 2008;28:1–10
2. Zimmerman DW. A note on the influence of outliers on parametric and nonparametric tests. J Gen Psychol. 1994;121:391–401
   • CrossRef
3. Zimmerman DW. Increasing the power of nonparametric tests by detecting and downweighting outliers. J Exper Educ. 1995;64:71–78
4. Jarrell MG. A comparison of two procedures, the Mahalanobis Distance and the Andrews-Pregibon Statistic, for identifying multivariate outliers. Res Sch. 1994;1:49–58
5. Rasmussen JL. Evaluating outlier identification tests: Mahalanobis D Squared and Comrey D. Multivariate Behav Res. 1988;23:189–202
6. Stevens JP. Outliers and influential data points in regression analysis. Psychol Bull. 1984;95:334–344
   • CrossRef
7. Hawkins DM. Identification of Outliers. New York: Chapman and Hall; 1980;
8. Dixon WJ. Analysis of extreme values. Ann Math Stat. 1950;21:488–506
9. Wainer H. Robust statistics: a survey and some prescriptions. J Educ Stat. 1976;1:285–312
10. Schwager SJ, Margolin BH. Detection of multivariate outliers. Ann Stat. 1982;10:943–954
11. Miller J. Reaction time analysis with outlier exclusion: bias varies with sample size. Q J Exp Psychol. 1991;43:907–912
12. Van Selst M, Jolicoeur P. A solution to the effect of sample size on outlier elimination. Q J Exp Psychol. 1994;47:631–650
13. Newton RR, Rudestam KE. Your Statistical Consultant: Answers to Your Data Analysis Questions. Thousand Oaks, CA: Sage.; 1999;
14. Barnett V, Lewis T. Outliers in Statistical Data. New York: Wiley; 1994;
15. Huck SW, Sutton CO. Some comments concerning the use of monotonic transformations to remove the interaction in two-factor ANOVA's. Educ Psychol Meas. 1975;35:789–791
16. Osborne JW, Blanchard MR. Random responding from students is a threat to the validity of educational research results. Educational Psychology. in press.
17. Brewer CS, Nauenberg E, Osborne JW. Differences among hospital and non-hospital RNs participation, satisfacton, and organizational committment in western New York. In: Paper presented at: National meeting of the Association for Health Service Research; June, 1998; Washington DC. 1998;
18. Iglewicz B, Hoaglin DC. How to Detect and Handle Outliers. Wilwaukee, WI: ASQC Quality Press; 1993;
19. Osborne JW. In: Notes on the use of data transformations. Practical assessment, research, and evaluation. 2002;p. 8;Available online at http://ericae.net/pare/getvn.asp?v=8&n=6
20. Evans VP. Strategies for detecting outliers in regression analysis: an introductory primer. In:  Thompson B editors. Advances in Social Science Methodology. Vol. 5:Stamford, CT: JAI Press.; 1999;p. 213–233
21. Sachs L. Applied Statistics: A Handbook of Techniques. 2nd ed. New York: Springer-Verlag; 1982;
22. Rowland-Jones S, Sutton J, Ariyoshi K, et al. HIV-specific cytotoxic T-cells in HIV-exposed but uninfected Gambian women. Nat Med. 1995;1:59–64
    • MEDLINE
    • CrossRef
23. Judd CM, McClelland GH. Data analysis: A Model Comparison Approach. San Diego, CA: Harcourt Brace Jovanovich; 1989;
24. Osborne JW, Overbay A. The power of outliers (and why researchers should ALWAYS check for them). In: Practical Assessment, Research, and Evaluation. 2004;p. 9
25. Anscome FJ. Rejection of outliers. Technometrics. 1960;2:123–147
26. Rousseeuw P, Leroy A. Robust Regression and Outlier Detection. New york: Wiley; 1987;
27. Lane K. What Is Robust Regression and How Do You Do It?. In: Annual meeting of the southwest educational research association. Austin, TX. 2002;
28. Zimmerman DW. Invalidation of parametric and nonparamteric statistical tests by concurrent violation of two assumptions. J Exp Educ. 1998;67:55–68
29. Cole JC. How to deal with missing data. In:  Osborne JW editors. Best Practices in Quantitative Methods. Thousand Oaks, CA: Sage Publishing; 2008;