  Circular data are data measured in angles and occur in a variety of
scientific disciplines. Bayesian methods promise to allow for flexible analysis
of circular data. Three existing MCMC methods (Gibbs, Metropolis-Hastings, and
Rejection) for a single group of circular data were extended to be used in a
between-subjects design, providing a novel procedure to compare groups of
circular data. Investigating the performance of the methods by simulation
study, all methods were found to overestimate the concentration parameter of
the posterior, while coverage was reasonable. The rejection sampler performed
best. In future research, the MCMC method may be extended to include
covariates, or a within-subjects design.
