This page contains supplementary material for "Bayesian Cultural Consensus Theory" by Zita Oravecz, Joachim Vandekerckhove, and William H. Batchelder ().

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— Zita Oravecz' website (with BCCT)


In this paper we present a Bayesian inference framework for Cultural Consensus Theory (CCT) models for dichotomous (True/False) response data, and we provide an associated, user friendly software package to carry out the inference. We believe that the time is ripe for Bayesian statistical inference to become the default choice in the field of CCT. Unfortunately, a lack of publications presenting a practical description of the Bayesian framework in the context of CCT models , as well as a dearth of accessible software to apply Bayesian inference to CCT data has prevented this from happening. In the present article, we provide an introduction to the Bayesian treatment of several CCT models for dichotomous (True/False) response data, with a focus on the various merits of Bayesian parameter estimation and interpretation of results. At the same time, we introduce the Bayesian Cultural Consensus Toolbox (BCCT): a user-friendly and comprehensive software package to fit these models to field data.

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