Jia Bing, Zhang Xue, Zhu Zhemin
Teacher Training Center, Beihua University, Jilin City, China.
China Institute of Rural Education Development, Northeast Normal University, Changchun, China.
Front Psychol. 2019 Jan 31;10:43. doi: 10.3389/fpsyg.2019.00043. eCollection 2019.
Item response models often cannot calculate true individual response probabilities because of the existence of response disturbances (such as guessing and cheating). Many studies on aberrant responses under item response theory (IRT) framework had been conducted. Some of them focused on how to reduce the effect of aberrant responses, and others focused on how to detect aberrant examinees, such as person fit analysis. The purpose of this research was to derive a generalized formula of bias with/without aberrant responses, that showed the effect of both non-aberrant and aberrant response data on the bias of capability estimation mathematically. A new evaluation criterion, named aberrant absolute bias (|ABIAS|), was proposed to detect aberrant examinees. Simulation studies and application to a real dataset were conducted to demonstrate the efficiency and the utility of |ABIAS|.
由于存在反应干扰（如猜测和作弊），项目反应模型往往无法计算真正的个体反应概率。在项目反应理论（IRT）框架下，已经开展了许多关于异常反应的研究。其中一些研究关注如何减少异常反应的影响，另一些则关注如何检测异常考生，如个体拟合分析。本研究的目的是推导一个有/无异常反应情况下偏差的广义公式，该公式从数学上显示了正常和异常反应数据对能力估计偏差的影响。提出了一种名为异常绝对偏差（|ABIAS|）的新评估标准来检测异常考生。进行了模拟研究并将其应用于一个真实数据集，以证明|ABIAS|的有效性和实用性。
