Data_Sheet_1_A Short Note on Aberrant Responses Bias in Item Response Theory.docx

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|.

由于作答干扰因素（如猜测与作弊）的存在，项目反应模型（Item Response Models, IRM）往往无法计算真实的个体作答概率。学界已开展诸多基于项目反应理论（Item Response Theory, IRT）框架的异常作答相关研究。其中部分研究聚焦于如何削弱异常作答的影响，其余则致力于检测异常作答者，例如个人拟合分析。本研究旨在推导存在/不存在异常作答时的偏差通用公式，从数学层面阐明正常作答与异常作答数据对被试能力估计偏差的影响。本研究提出了一种名为异常绝对偏差（|ABIAS|）的全新评价准则，用于检测异常作答者。本研究通过模拟实验与真实数据集应用，验证了|ABIAS|的有效性与实用价值。