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Journal Article

The multivariate analysis of variance as a powerful approach for circular data

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Malkemper,  E. Pascal       
Max Planck Research Group Neurobiology of Magnetoreception, Max Planck Institute for Neurobiology of Behavior – caesar, Max Planck Society;

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https://movementecologyjournal.biomedcentral.com/articles/10.1186/s40462-022-00323-8
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Citation

Landler, L., Ruxton, G. D., & Malkemper, E. P. (2022). The multivariate analysis of variance as a powerful approach for circular data. BMC Movement Ecology, 10(1): 21. doi:10.1186/s40462-022-00323-8.


Cite as: https://hdl.handle.net/21.11116/0000-000A-6F66-8
Abstract
Background: A broad range of scientifc 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


ofering extension to multi-factorial modelling that these conventional circular statistical tests do not.


Conclusions: The herein presented MANOVA approach ofers a substantial broadening of the scientifc questions


that can be addressed statistically using circular data.