title: Hierarchical Bayesian Modeling for Test Theory Without an Answer Key.
abstract: 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.
authors: Department of Cognitive Sciences, UCI, 3213 Social & Behavioral Sciences Gateway Building, Irvine, CA, 92697-5100, USA, zoravecz@uci.edu.
authors: Zita
authors: Z
authors: Oravecz
authors: Royce
authors: R
authors: Anders
authors: William H
authors: WH
authors: Batchelder
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D015233:Models, Statistical; D011594:Psychometrics