Oravecz Zita, Anders Royce, Batchelder William H
Department of Cognitive Sciences, UCI, 3213 Social & Behavioral Sciences Gateway Building, Irvine, CA, 92697-5100, USA,
Psychometrika. 2015 Jun;80(2):341-64. doi: 10.1007/s11336-013-9379-4. Epub 2013 Dec 11.
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.
文化共识理论（CCT）模型已在社会和行为科学的各个研究领域中广泛应用，以探索共享的知识和信念。CCT模型基于反应数据运行，其中答案键是潜在的。本文通过开发用于分层贝叶斯推断的适当规范，来改进这些模型的应用方法。一个主要贡献是将协变量的使用整合到CCT模型中的方法。更具体地说，与人和项目相关的参数都被引入为随机效应，它们可以分别解释个体间和项目间的变异模式。
