Working With Circular Data: A Tutorial for Cognitive and Behavioral Research
Abstract
Introductory statistics courses and conventional methods in behavioral and cognitive science are typically restricted to Euclidean space without explicitly stating this, leaving many researchers unaware of how to handle data with circular or periodic properties (e.g., time of day or orientation on a screen). This tutorial provides an accessible yet rigorous introduction, designed to bridge the gap between statistical theory and practical application. After an overview of common applications, we discuss different ways of representing circular data mathematically and fundamental principles for working with them. We explain angular and vector representations and operations, including key considerations for implementing them in computer code. We then survey methods for visualizing circular data, including linear and polar plots and presentation of summary statistics. Next, we discuss circular measures of central tendency, dispersion, and shape: how they relate to their Euclidean counterparts and why they differ. Finally, we turn to more advanced topics of the kind needed to construct models of circular data. We introduce the most common circular distribution families, including circular analogs of the Gaussian distribution and their properties, as well as flexible distribution families that can describe skewed and heavy-tailed data. The tutorial takes a method-oriented approach, not tied to specific software, but highlighting factors relevant to experimental design, data analysis, and statistical modeling. A consistent theme is the need to rigorously account for the inherent periodicity of circular data. We also provide a consistent notation for circular statistics and parameters, addressing the lack of established conventions in the field.
©American Psychological Association, 2026. This paper is not the copy of record and may not exactly replicate the authoritative document published in the APA journal. The final article is available, upon publication, at: 10.1037/met0000861
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BibTeX
@misc{wernicke2026working,
title = {{Working With Circular Data: A Tutorial for Cognitive and Behavioral Research}},
author = {Stella Wernicke and Julie de Falco and Zahara Gironés and Chris Jungerius and Ivan Tomic and Adam Triabhall and Xiaolu Wang and Paul Bays},
howpublished = {PsyArXiv},
year = {2026},
month = sep,
doi = {10.31234/osf.io/5n64t_v6},
url = {https://osf.io/preprints/psyarxiv/5n64t_v6/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Working With Circular Data: A Tutorial for Cognitive and Behavioral Research
AU - Stella Wernicke
AU - Julie de Falco
AU - Zahara Gironés
AU - Chris Jungerius
AU - Ivan Tomic
AU - Adam Triabhall
AU - Xiaolu Wang
AU - Paul Bays
PY - 2026
DA - 2026/09/10
PB - PsyArXiv
DO - 10.31234/osf.io/5n64t_v6
UR - https://osf.io/preprints/psyarxiv/5n64t_v6/
AB - Introductory statistics courses and conventional methods in behavioral and cognitive science are typically restricted to Euclidean space without explicitly stating this, leaving many researchers unaware of how to handle data with circular or periodic properties (e.g., time of day or orientation on a screen). This tutorial provides an accessible yet rigorous introduction, designed to bridge the gap between statistical theory and practical application. After an overview of common applications, we discuss different ways of representing circular data mathematically and fundamental principles for working with them. We explain angular and vector representations and operations, including key considerations for implementing them in computer code. We then survey methods for visualizing circular data, including linear and polar plots and presentation of summary statistics. Next, we discuss circular measures of central tendency, dispersion, and shape: how they relate to their Euclidean counterparts and why they differ. Finally, we turn to more advanced topics of the kind needed to construct models of circular data. We introduce the most common circular distribution families, including circular analogs of the Gaussian distribution and their properties, as well as flexible distribution families that can describe skewed and heavy-tailed data. The tutorial takes a method-oriented approach, not tied to specific software, but highlighting factors relevant to experimental design, data analysis, and statistical modeling. A consistent theme is the need to rigorously account for the inherent periodicity of circular data. We also provide a consistent notation for circular statistics and parameters, addressing the lack of established conventions in the field. ©American Psychological Association, 2026. This paper is not the copy of record and may not exactly replicate the authoritative document published in the APA journal. The final article is available, upon publication, at: 10.1037/met0000861
ER -