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Hierarchical Bayesian Modeling for Test Theory Without an Answer Key

Published online by Cambridge University Press:  01 January 2025

Zita Oravecz ,
Royce Anders  and
William H. Batchelder
Zita Oravecz*
Affiliation:
University of California, Irvin
Royce Anders
Affiliation:
University of California, Irvin
William H. Batchelder
Affiliation:
University of California, Irvin
*
Requests for reprints should be sent to Zita Oravecz, UCI, Department of Cognitive Sciences, 3213 Social & Behavioral Sciences Gateway Building, Irvine, CA 92697-5100, USA. E-mail: [email protected]
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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.

Keywords

Cultural Consensus TheoryBayesian statisticshierarchical modelcovariate modeling
Type
Original Paper
Information
Psychometrika , Volume 80 , Issue 2 , June 2015 , pp. 341 - 364
Copyright
Copyright © 2013 The Psychometric Society

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