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ERIC Number: EJ814008
Record Type: Journal
Publication Date: 2008-Oct
Pages: 11
Abstractor: As Provided
Reference Count: 63
ISSN: ISSN-0003-066X
Modern Robust Statistical Methods: An Easy Way to Maximize the Accuracy and Power of Your Research
Erceg-Hurn, David M.; Mirosevich, Vikki M.
American Psychologist, v63 n7 p591-601 Oct 2008
Classic parametric statistical significance tests, such as analysis of variance and least squares regression, are widely used by researchers in many disciplines, including psychology. For classic parametric tests to produce accurate results, the assumptions underlying them (e.g., normality and homoscedasticity) must be satisfied. These assumptions are rarely met when analyzing real data. The use of classic parametric methods with violated assumptions can result in the inaccurate computation of p values, effect sizes, and confidence intervals. This may lead to substantive errors in the interpretation of data. Many modern robust statistical methods alleviate the problems inherent in using parametric methods with violated assumptions, yet modern methods are rarely used by researchers. The authors examine why this is the case, arguing that most researchers are unaware of the serious limitations of classic methods and are unfamiliar with modern alternatives. A range of modern robust and rank-based significance tests suitable for analyzing a wide range of designs is introduced. Practical advice on conducting modern analyses using software such as SPSS, SAS, and R is provided. The authors conclude by discussing robust effect size indices. (Contains 1 table.)
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Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A