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Journal Article

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


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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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
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.