Pena Carolina Silva, Costa Marcelo Azevedo, Braga Oliveira Rivert Paulo
Department of Production Engineering, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
Pró-Reitoria de Graduação, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil.
PLoS One. 2018 Feb 1;13(2):e0191600. doi: 10.1371/journal.pone.0191600. eCollection 2018.
In a typical questionnaire testing situation, examinees are not allowed to choose which items they answer because of a technical issue in obtaining satisfactory statistical estimates of examinee ability and item difficulty. This paper introduces a new item response theory (IRT) model that incorporates information from a novel representation of questionnaire data using network analysis. Three scenarios in which examinees select a subset of items were simulated. In the first scenario, the assumptions required to apply the standard Rasch model are met, thus establishing a reference for parameter accuracy. The second and third scenarios include five increasing levels of violating those assumptions. The results show substantial improvements over the standard model in item parameter recovery. Furthermore, the accuracy was closer to the reference in almost every evaluated scenario. To the best of our knowledge, this is the first proposal to obtain satisfactory IRT statistical estimates in the last two scenarios.
在典型的问卷测试情境中，由于在获得关于考生能力和试题难度的满意统计估计值方面存在技术问题，考生不被允许选择他们要回答的题目。本文介绍了一种新的项目反应理论（IRT）模型，该模型使用网络分析，将来自问卷数据新表示形式的信息纳入其中。模拟了考生选择部分题目的三种情境。在第一种情境中，满足了应用标准拉施模型所需的假设，从而为参数准确性建立了一个参考。第二和第三种情境包括违反这些假设的五个逐渐增加的程度。结果表明，在项目参数恢复方面，与标准模型相比有显著改进。此外，在几乎每个评估情境中，准确性都更接近参考值。据我们所知，这是在最后两种情境中获得满意的IRT统计估计值的首个提议。
