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Consensus Analysis for Populations With Latent Subgroups: Applying Multicultural Consensus Theory and Model-Based Clustering With CCTpack
Cross-Cultural Research ( IF 2.178 ) Pub Date : 2017-09-14 , DOI: 10.1177/1069397117727500 Royce Anders 1 , F.-Xavier Alario 1 , William H. Batchelder 2
Cross-Cultural Research ( IF 2.178 ) Pub Date : 2017-09-14 , DOI: 10.1177/1069397117727500 Royce Anders 1 , F.-Xavier Alario 1 , William H. Batchelder 2
Affiliation
Advanced consensus analyses are made available with the cultural consensus theory (CCT) framework, which is a model-based methodology used to closely derive the consensus knowledge of a group or population (e.g., a culture). The relevant data are their questionnaire responses about a subject domain. CCT has been established as a main methodology for ethnographic studies in social and cultural anthropology, and it has also been incorporated into other areas of the social and behavioral sciences as an effective information-pooling methodology. Recently, there are major advances in CCT, including (a) the multicultural extension, which can detect latent subgroups of a population, each with their own consensus answers to the questionnaire items; (b) the development of new models for several questionnaire designs, true/false, ordered category (Likert) scales, and continuous scales; (c) the estimation of important parameters that affect the response process; and (d) the development of Bayesian hierarchical inference for these CCT models. The joint analysis of these features positions such CCT approaches as some of the most advanced consensus analysis methods currently available. That is, they jointly (and hierarchically) estimate the consensus answers to the questionnaire items, the degree of knowledge (cultural competence) of each individual, the response biases of each individual, the difficulty (cultural salience) of each questionnaire item, and the subcultural group of the individual. In this article, we provide an overview of these major advancements in CCT, and we introduce CCTpack, which is currently the only software package available that handles all these extensions, especially (a) and (b).
中文翻译:
具有潜在子群的群体的共识分析:使用 CCTpack 应用多元文化共识理论和基于模型的聚类
文化共识理论 (CCT) 框架提供了高级共识分析,该框架是一种基于模型的方法,用于密切推导出群体或人口(例如文化)的共识知识。相关数据是他们关于某个主题领域的问卷答复。CCT 已被确立为社会和文化人类学民族志研究的主要方法,并且它也被纳入社会和行为科学的其他领域,作为一种有效的信息汇集方法。最近,CCT 取得了重大进展,包括 (a) 多元文化扩展,它可以检测一个群体的潜在亚群,每个亚群对问卷项目都有自己的共识;(b) 为几种问卷设计开发新模型,对/错,有序范畴 (Likert) 量表和连续量表;(c) 影响响应过程的重要参数的估计;(d) 这些 CCT 模型的贝叶斯分层推理的发展。对这些特征的联合分析将此类 CCT 方法定位为目前可用的一些最先进的共识分析方法。也就是说,他们联合(并分层)估计问卷项目的共识答案、每个人的知识程度(文化能力)、每个人的回答偏差、每个问卷项目的难度(文化显着性),以及个人的亚文化群体。在本文中,我们概述了 CCT 的这些主要进步,并介绍了 CCTpack,
更新日期:2017-09-14
中文翻译:
具有潜在子群的群体的共识分析:使用 CCTpack 应用多元文化共识理论和基于模型的聚类
文化共识理论 (CCT) 框架提供了高级共识分析,该框架是一种基于模型的方法,用于密切推导出群体或人口(例如文化)的共识知识。相关数据是他们关于某个主题领域的问卷答复。CCT 已被确立为社会和文化人类学民族志研究的主要方法,并且它也被纳入社会和行为科学的其他领域,作为一种有效的信息汇集方法。最近,CCT 取得了重大进展,包括 (a) 多元文化扩展,它可以检测一个群体的潜在亚群,每个亚群对问卷项目都有自己的共识;(b) 为几种问卷设计开发新模型,对/错,有序范畴 (Likert) 量表和连续量表;(c) 影响响应过程的重要参数的估计;(d) 这些 CCT 模型的贝叶斯分层推理的发展。对这些特征的联合分析将此类 CCT 方法定位为目前可用的一些最先进的共识分析方法。也就是说,他们联合(并分层)估计问卷项目的共识答案、每个人的知识程度(文化能力)、每个人的回答偏差、每个问卷项目的难度(文化显着性),以及个人的亚文化群体。在本文中,我们概述了 CCT 的这些主要进步,并介绍了 CCTpack,