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dc.contributor.authorPuth, M.-T.
dc.contributor.authorNeuhäuser, M.
dc.contributor.authorRuxton, G.D.
dc.date.accessioned2017-02-06T00:32:26Z
dc.date.available2017-02-06T00:32:26Z
dc.date.issued2015-04
dc.identifier.citationPuth , M-T , Neuhäuser , M & Ruxton , G D 2015 , ' Effective use of Spearman's and Kendall's correlation coefficients for association between two measured traits ' , Animal Behaviour , vol. 102 , pp. 77-84 . https://doi.org/10.1016/j.anbehav.2015.01.010en
dc.identifier.issn0003-3472
dc.identifier.otherPURE: 168621453
dc.identifier.otherPURE UUID: 2f56e92c-369d-4715-91ef-8c10cd7aa598
dc.identifier.otherScopus: 84922309693
dc.identifier.otherWOS: 000351058700009
dc.identifier.otherORCID: /0000-0001-8943-6609/work/60427524
dc.identifier.urihttp://hdl.handle.net/10023/10233
dc.description.abstractWe examine the performance of the two rank order correlation coefficients (Spearman's rho and Kendall's tau) for describing the strength of association between two continuously measured traits. We begin by discussing when these measures should, and should not, be preferred over Pearson's product-moment correlation coefficient on conceptual grounds. For testing the null hypothesis of no monotonic association, our simulation studies found both rank coefficients show similar performance to variants of the Pearson product-moment measure of association, and provide only slightly better performance than Pearson's measure even if the two measured traits are non-normally distributed. Where variants of the Pearson measure are not appropriate, there was no strong reason (based on our results) to select either of our rank-based alternatives over the other for testing the null hypothesis of no monotonic association. Further, our simulation studies indicated that for both rank coefficients there exists at least one method for calculating confidence intervals that supplies results close to the desired level if there are no tied values in the data. In this case, Kendall's coefficient produces consistently narrower confidence intervals, and might thus be preferred on that basis. However, if there are any ties in the data, irrespective of whether the percentage of ties is small or large, Spearman's measure returns values closer to the desired coverage rates, whereas Kendall's results differ more and more from the desired level as the number of ties increases, especially for large correlation values.
dc.format.extent8
dc.language.isoeng
dc.relation.ispartofAnimal Behaviouren
dc.rights© 2015. The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved. This is the author’s version of a work that was accepted for publication in Animal Behaviour. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Animal Behaviour, 102, April 2015 DOI 10.1016/j.anbehav.2015.01.010en
dc.subjectConfidence intervalsen
dc.subjectNull hypothesis testingen
dc.subjectPearson's product–moment correlation coefficienten
dc.subjectPoweren
dc.subjectStatisticsen
dc.subjectType 1 erroren
dc.subjectQH301 Biologyen
dc.subjectT-NDASen
dc.subject.lccQH301en
dc.titleEffective use of Spearman's and Kendall's correlation coefficients for association between two measured traitsen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Biologyen
dc.contributor.institutionUniversity of St Andrews.Centre for Biological Diversityen
dc.identifier.doihttps://doi.org/10.1016/j.anbehav.2015.01.010
dc.description.statusPeer revieweden
dc.date.embargoedUntil2017-02-05


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