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Hierarchical Bayesian Modeling for Test Theory Without an Answer Key

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  • Zita Oravecz
  • Royce Anders
  • William Batchelder

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. Copyright The Psychometric Society 2015

Suggested Citation

  • Zita Oravecz & Royce Anders & William Batchelder, 2015. "Hierarchical Bayesian Modeling for Test Theory Without an Answer Key," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 341-364, June.
    • Handle: RePEc:spr:psycho:v:80:y:2015:i:2:p:341-364
    DOI: 10.1007/s11336-013-9379-4
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    References listed on IDEAS

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    Cited by:

    1. Michael D. Lee, 2018. "Bayesian methods for analyzing true-and-error models," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 13(6), pages 622-635, November.
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    3. Florian Wickelmaier & Achim Zeileis, 2016. "Using Recursive Partitioning to Account for Parameter Heterogeneity in Multinomial Processing Tree Models," Working Papers 2016-26, Faculty of Economics and Statistics, Universität Innsbruck.

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