PDF(Pubmed)
Abstract：
Diagnostic classification models (DCMs) have been used to classify examinees into groups based on their possession status of a set of latent traits. In addition to traditional item-based scoring approaches, examinees may be scored based on their completion of a series of small and similar tasks. Those scores are usually considered as count variables. To model count scores, this study proposes a new class of DCMs that uses the negative binomial distribution at its core. We explained the proposed model framework and demonstrated its use through an operational example. Simulation studies were conducted to evaluate the performance of the proposed model and compare it with the Poisson-based DCM.
摘要：
诊断分类模型（DCM）已用于根据受检者对一组潜在特征的拥有状态将受检者分类。除了传统的基于项目的评分方法，考生可以根据他们完成一系列小的和类似的任务来评分。这些分数通常被认为是计数变量。要对计数分数进行建模，这项研究提出了一类新的DCMs，在其核心使用负二项分布。我们解释了所提出的模型框架，并通过一个操作示例演示了其使用。进行了仿真研究以评估所提出的模型的性能，并将其与基于泊松的DCM进行比较。
