A Test of Order-Constrained Hypotheses for Circular Data With Applications to Human Movement Science

ABSTRACT

Researchers studying the movements of the human body often encounter data measured in angles (e.g., angular displacements of joints). The evaluation of these circular data requires special statistical methods. The authors introduce a new test for the analysis of order-constrained hypotheses for circular data. Through this test, researchers can evaluate their expectations regarding the outcome of an experiment directly by representing their ideas in the form of a hypothesis containing inequality constraints. The resulting data analysis is generally more powerful than one using standard null hypothesis testing. Two examples of circular data from human movement science are presented to illustrate the use of the test. Results from a simulation study show that the test performs well.

Keywords:

• circular data
• directional statistics
• order constrained hypotheses
• resampling methods

ACKNOWLEDGMENTS

The authors invite any interested parties to contact them for the R code of the functions. They hope to provide an R package with the functions in the near future.