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dc.contributor.authorLandler, Lukas
dc.contributor.authorRuxton, Graeme D.
dc.contributor.authorMalkemper, E. Pascal
dc.identifier.citationLandler , L , Ruxton , G D & Malkemper , E P 2022 , ' The multivariate analysis of variance as a powerful approach for circular data ' , Movement Ecology , vol. 10 , 21 .
dc.identifier.otherPURE: 279307190
dc.identifier.otherPURE UUID: 56d08e94-ea1a-43c3-819f-c1c971aaeef9
dc.identifier.otherRIS: urn:26F57E58FD3DD0AEDFD8A4DF1B79D7E4
dc.identifier.otherRIS: Landler2022
dc.identifier.otherORCID: /0000-0001-8943-6609/work/112333588
dc.identifier.otherScopus: 85128961645
dc.identifier.otherWOS: 000788305600001
dc.descriptionLL is supported by the Austrian Science Fund (FWF, Grant Number: P32586). EPM receives funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant Agreement No. 948728).en
dc.description.abstractBackground A broad range of scientific studies involve taking measurements on a circular, rather than linear, scale (often variables related to times or orientations). For linear measures there is a well-established statistical toolkit based on linear modelling to explore the associations between this focal variable and potentially several explanatory factors and covariates. In contrast, statistical testing of circular data is much simpler, often involving either testing whether variation in the focal measurements departs from circular uniformity, or whether a single explanatory factor with two levels is supported. Methods We use simulations and example data sets to investigate the usefulness of a MANOVA approach for circular data in comparison to commonly used statistical tests. Results Here we demonstrate that a MANOVA approach based on the sines and cosines of the circular data is as powerful as the most-commonly used tests when testing deviation from a uniform distribution, while additionally offering extension to multi-factorial modelling that these conventional circular statistical tests do not. Conclusions The herein presented MANOVA approach offers a substantial broadening of the scientific questions that can be addressed statistically using circular data.
dc.relation.ispartofMovement Ecologyen
dc.rightsCopyright © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.en
dc.subjectRayleigh testen
dc.subjectDirectional dataen
dc.subjectHA Statisticsen
dc.titleThe multivariate analysis of variance as a powerful approach for circular dataen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.contributor.institutionUniversity of St Andrews. Centre for Biological Diversityen
dc.contributor.institutionUniversity of St Andrews. Institute of Behavioural and Neural Sciencesen
dc.description.statusPeer revieweden

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