Progressive statistics for studies in sports medicine and exercise science

Hdl Handle:
http://hdl.handle.net/10149/95707
Title:
Progressive statistics for studies in sports medicine and exercise science
Authors:
Hopkins, W. G. (William); Marshall, S. W. (Stephen); Batterham, A. M. (Alan); Hanin, J. (Juri)
Affiliation:
University of Teesside. School of Health and Social Care.
Citation:
Hopkins, W. G. et. al. (2009) 'Progressive statistics for studies in sports medicine and exercise science', Medicine & Science in Sports & Exercise, 41 (1), pp.3-12.
Publisher:
American College of Sports Medicine
Journal:
Medicine & Science in Sports & Exercise
Issue Date:
Jan-2009
URI:
http://hdl.handle.net/10149/95707
DOI:
10.1249/MSS.0b013e31818cb278
Abstract:
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 nullhypothesis 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.
Type:
Article
Language:
en
Keywords:
analysis; case; design; inference; qualitative; quantitative; sample; sport; exercise
ISSN:
0195-9131
Rights:
Author can archive post-print (ie final draft post-refereeing). for full details see http://www.sherpa.ac.uk/romeo/ [Accessed 06/04/2010]
Citation Count:
14 [Scopus, 06/04/2010]

Full metadata record

DC FieldValue Language
dc.contributor.authorHopkins, W. G. (William)en
dc.contributor.authorMarshall, S. W. (Stephen)en
dc.contributor.authorBatterham, A. M. (Alan)en
dc.contributor.authorHanin, J. (Juri)en
dc.date.accessioned2010-04-06T12:59:38Z-
dc.date.available2010-04-06T12:59:38Z-
dc.date.issued2009-01-
dc.identifier.citationMedicine & Science in Sports & Exercise; 41 (1): 3-12en
dc.identifier.issn0195-9131-
dc.identifier.doi10.1249/MSS.0b013e31818cb278-
dc.identifier.urihttp://hdl.handle.net/10149/95707-
dc.description.abstractStatistical 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 nullhypothesis 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.en
dc.language.isoenen
dc.publisherAmerican College of Sports Medicineen
dc.rightsAuthor can archive post-print (ie final draft post-refereeing). for full details see http://www.sherpa.ac.uk/romeo/ [Accessed 06/04/2010]en
dc.subjectanalysisen
dc.subjectcaseen
dc.subjectdesignen
dc.subjectinferenceen
dc.subjectqualitativeen
dc.subjectquantitativeen
dc.subjectsampleen
dc.subjectsporten
dc.subjectexerciseen
dc.titleProgressive statistics for studies in sports medicine and exercise scienceen
dc.typeArticleen
dc.contributor.departmentUniversity of Teesside. School of Health and Social Care.en
dc.identifier.journalMedicine & Science in Sports & Exerciseen
ref.citationcount14 [Scopus, 06/04/2010]en
or.citation.harvardHopkins, W. G. et. al. (2009) 'Progressive statistics for studies in sports medicine and exercise science', Medicine & Science in Sports & Exercise, 41 (1), pp.3-12.-
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