Progressive statistics for studies in sports medicine and exercise science.
Statistical guidelines and expert statements are now available to assist in the analysis and reporting of studies in some biomedical disciplines. We present here a more progressive resource for sample-based studies, meta-analyses, and case studies in sports medicine and exercise science. We offer fo...
| Publicado en: | Medicine & Science in Sports & Exercise Vol. 41; no. 1; pp. 3 - 14 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Jan2009
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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=ccm&AN=105471425&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105471425 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01959131 4DP jtl: Medicine & Science in Sports & Exercise issn: 01959131 maglogo: N pubinfo: dt: Jan2009 vid: 41 iid: 1 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 105471425 2010215215 10.1249/MSS.0b013e31818cb278 NLM19092709 105471425 ppf: 3 ppct: 11 formats: tig: atl: Progressive statistics for studies in sports medicine and exercise science. aug: au: Hopkins WG Marshall SW Batterham AM Hanin J affil: Institute of Sport and Recreation Research, AUT University, Auckland, New Zealand. will@clear.net.nz sug: subj: Data Analysis, Statistical Exercise Physiology Research, Medical Sports Medicine Study Design Sample Size Human ab: Statistical guidelines and expert statements are now available to assist in the analysis and reporting of studies in some biomedical disciplines. We present here a more progressive resource for sample-based studies, meta-analyses, and case studies in sports medicine and exercise science. We offer forthright advice on the following controversial or novel issues: using precision of estimation for inferences about population effects in preference to null-hypothesis testing, which is inadequate for assessing clinical or practical importance; justifying sample size via acceptable precision or confidence for clinical decisions rather than via adequate power for statistical significance; showing SD rather than SEM, to better communicate the magnitude of differences in means and nonuniformity of error; avoiding purely nonparametric analyses, which cannot provide inferences about magnitude and are unnecessary; using regression statistics in validity studies, in preference to the impractical and biased limits of agreement; making greater use of qualitative methods to enrich sample-based quantitative projects; and seeking ethics approval for public access to the depersonalized raw data of a study, to address the need for more scrutiny of research and better meta-analyses. Advice on less contentious issues includes the following: using covariates in linear models to adjust for confounders, to account for individual differences, and to identify potential mechanisms of an effect; using log transformation to deal with nonuniformity of effects and error; identifying and deleting outliers; presenting descriptive, effect, and inferential statistics in appropriate formats; and contending with bias arising from problems with sampling, assignment, blinding, measurement error, and researchers' prejudices. This article should advance the field by stimulating debate, promoting innovative approaches, and serving as a useful checklist for authors, reviewers, and editors. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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