A classification model for continuous responses: Identifying risk perception groups on health‐related activities
A classification model for continuous responses: Identifying risk perception groups on health‐related activities
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DOI：
10.1002/bimj.202100222
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发表时间：
2023-02
影响因子：
1.7
通讯作者：
Eduardo S B de Oliveira;Xiaojing Wang;Jorge L. Bazán
中科院分区：
文献类型：
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作者：
Eduardo S B de Oliveira;Xiaojing Wang;Jorge L. Bazán
在当前关于潜变量模型的文献中，人们在开发用于评估的二分和多分认知诊断模型（CDM）方面投入了大量精力。最近，人们开始讨论在清洁发展机制中使用连续响应的可能性。但尚未开发出贝叶斯方法来分析响应连续时的 CDM。我们的工作是第一个用于连续确定性输入、噪声和门（DINA）模型的贝叶斯框架。我们还对该 DINA 模型中的项目参数提出了新的解释，这使得分析比以前更具可解释性。此外，我们还进行了多次模拟，通过贝叶斯方法评估连续 DINA 模型的性能。然后，我们将提出的 DINA 模型应用于个人对一系列健康相关活动的风险认知的真实数据示例。应用结果证明了使用所提出的连续 DINA 模型对研究中的个体进行分类的巨大潜力。
In the current literature on latent variable models, much effort has been put on the development of dichotomous and polytomous cognitive diagnostic models (CDMs) for assessments. Recently, the possibility of using continuous responses in CDMs has been brought to discussion. But no Bayesian approach has been developed yet for the analysis of CDMs when responses are continuous. Our work is the first Bayesian framework for the continuous deterministic inputs, noisy, and gate (DINA) model. We also propose new interpretations for item parameters in this DINA model, which makes the analysis more interpretable than before. In addition, we have conducted several simulations to evaluate the performance of the continuous DINA model through our Bayesian approach. Then, we have applied the proposed DINA model to a real data example of risk perceptions for individuals over a range of health‐related activities. The application results exemplify the high potential of the use of the proposed continuous DINA model to classify individuals in the study.