Modern Robust Statistical Methods: An Easy Way to Maximize the Accuracy and Power of Your Research

Autor/inn/en	Erceg-Hurn, David M.; Mirosevich, Vikki M.
Titel	Modern Robust Statistical Methods: An Easy Way to Maximize the Accuracy and Power of Your Research
Quelle	In: American Psychologist, 63 (2008) 7, S.591-601 (11 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0003-066X
DOI	10.1037/0003-066X.63.7.591
Schlagwörter	Statistical Significance; Least Squares Statistics; Effect Size; Statistical Studies; Statistical Analysis; Robustness (Statistics); Evaluation Methods; Evaluation Research; Test Reliability; Research Methodology; Research Problems + Suchen Sie Ihr Suchwort? Statistische Analyse; Widerstandsfähigkeit; Evaluationsforschung; Testreliabilität; Research method; Forschungsmethode; Forschungskritik
Abstract	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.) (As Provided).
Anmerkungen	American Psychological Association. Journals Department, 750 First Street NE, Washington, DC 20002-4242. Tel: 800-374-2721; Tel: 202-336-5510; Fax: 202-336-5502; e-mail: order@apa.org; Web site: http://www.apa.org/publications
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10