Modern Robust Statistical Methods: An Easy Way to Maximize the Accuracy and Power of Your Research.
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 homos...
| Publicado en: | American Psychologist Vol. 63; no. 7; pp. 591 - 602 |
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| Autores principales: | , |
| Formato: | Artículo |
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American Psychological Association
October 2008
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=508004849&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 508004849 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 0003066X APT jtl: American Psychologist issn: 0003066X maglogo: N pubinfo: dt: October 2008 vid: 63 iid: 7 pid: 34 pub: American Psychological Association artinfo: ui: 508004849 10.1037/0003-066X.63.7.591 ppf: 591 ppct: 11 formats: tig: atl: Modern Robust Statistical Methods: An Easy Way to Maximize the Accuracy and Power of Your Research. aug: au: Erceg-Hurn, David M. Mirosevich, Vikki M. su: Statistical hypothesis testing Statistical power analysis Psychological techniques Psychology -- Statistical methods Effect sizes (Statistics) Confidence intervals Computer software sug: subj: Statistical hypothesis testing Statistical power analysis Psychological techniques Psychology -- Statistical methods Effect sizes (Statistics) Confidence intervals Computer software ab: 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. Reprinted by permission of the publisher. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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