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Related Experiment Video

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High Density Event-related Potential Data Acquisition in Cognitive Neuroscience
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One Direction? A Tutorial for Circular Data Analysis Using R With Examples in Cognitive Psychology.

Jolien Cremers1, Irene Klugkist1

  • 1Department of Methodology and Statistics, Faculty of Social and Behavioural Sciences, Utrecht University, Utrecht, Netherlands.

Frontiers in Psychology
|November 15, 2018
PubMed
Summary

This tutorial introduces analyzing circular data, which has a 360° periodicity, for researchers in cognitive psychology and social sciences. It provides practical guidance using R packages for data inspection, model fitting, and hypothesis testing.

Keywords:
circular datageneral linear modelsmixed-effects modelsprojected normal distributiontutorial

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Area of Science:

  • Psychology
  • Social Sciences
  • Statistics

Background:

  • Circular data, characterized by its 360° periodicity, is prevalent across diverse scientific fields including ecology, medicine, and social sciences.
  • Cognitive and experimental psychology frequently encounter circular data, yet specialized analytical methods and literature are not widely disseminated.

Purpose of the Study:

  • To provide a tutorial on the analysis of circular data tailored for researchers in cognitive psychology and the broader social sciences.
  • To enhance the understanding and application of circular data analysis techniques within these disciplines.

Main Methods:

  • Focuses on practical data inspection, model fit assessment, parameter estimation, and hypothesis testing for circular data.
  • Utilizes specific statistical models designed for circular data analysis.
  • Employs packages from the R statistical programming language for implementation.

Main Results:

  • Demonstrates the application of R packages for analyzing circular data.
  • Provides a framework for researchers to confidently apply circular data methods.

Conclusions:

  • Addresses the scarcity of accessible literature on circular data analysis in psychology and social sciences.
  • Empowers researchers with the knowledge and tools to effectively analyze circular data, improving the rigor of studies in these fields.