Cognitive diagnostic models provide a framework for classifying individuals into latent proficiency classes, also known as attribute profiles. Recent research has examined the implementation of a Pólya-gamma data augmentation strategy binary response model using logistic item response functions within a Bayesian Gibbs sampling procedure. In this paper, we propose a sequential exploratory diagnostic model for ordinal response data using a logit-link parameterization at the category level and extend the Pólya-gamma data augmentation strategy to ordinal response processes. A Gibbs sampling procedure is presented for efficient Markov chain Monte Carlo (MCMC) estimation methods. We provide results from a Monte Carlo study for model performance and present an application of the model.

中文翻译：

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使用 Pólya-gamma 数据增强策略的顺序探索性诊断模型

认知诊断模型提供了一个框架，用于将个体分类为潜在熟练程度类别，也称为属性配置文件。最近的研究检查了在贝叶斯吉布斯采样过程中使用逻辑项响应函数来实施 Pólya-gamma 数据增强策略二元响应模型。在本文中，我们提出了一种在类别级别使用 logit-link 参数化的顺序响应数据的顺序探索性诊断模型，并将 Pólya-gamma 数据增强策略扩展到顺序响应过程。提出了一种用于有效马尔可夫链蒙特卡罗 (MCMC) 估计方法的吉布斯采样程序。我们提供了模型性能的蒙特卡罗研究结果，并展示了该模型的应用。