Bayesian Cultural Consensus Theory

Zita Oravecz, Joachim Vandekerckhove, William H. Batchelder

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


In this article, we present a Bayesian inference framework for cultural consensus theory (CCT) models for dichotomous (True/False) response data and provide an associated, user-friendly software package along with a detailed user’s guide 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 so far prevented this from happening. We introduce the Bayesian treatment of several CCT models, focusing on the various merits of Bayesian parameter estimation and interpretation of results, and also introduce the Bayesian Cultural Consensus Toolbox software package.

Original languageEnglish (US)
Pages (from-to)207-222
Number of pages16
JournalField Methods
Issue number3
StatePublished - Aug 1 2014

All Science Journal Classification (ASJC) codes

  • Anthropology


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