Hierarchical Bayesian Modeling for Test Theory Without an Answer Key

Zita Oravecz, Royce Anders, William H. Batchelder

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Cultural Consensus Theory (CCT) models have been applied extensively across research domains in the social and behavioral sciences in order to explore shared knowledge and beliefs. CCT models operate on response data, in which the answer key is latent. The current paper develops methods to enhance the application of these models by developing the appropriate specifications for hierarchical Bayesian inference. A primary contribution is the methodology for integrating the use of covariates into CCT models. More specifically, both person- and item-related parameters are introduced as random effects that can respectively account for patterns of inter-individual and inter-item variability.

Original languageEnglish (US)
Pages (from-to)341-364
Number of pages24
Issue number2
StatePublished - Jun 9 2015

All Science Journal Classification (ASJC) codes

  • General Psychology
  • Applied Mathematics


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