In educational assessments and achievement tests, test developers and administrators commonly assume that test-takers attempt all test items with full effort and leave no blank responses with unplanned missing values. However, aberrant response behavior—such as performance decline, dropping out beyond a certain point, and skipping certain items over the course of the test—is inevitable, especially for low-stakes assessments and speeded tests due to low motivation and time limits, respectively. In this study, test-takers are classified as normal or aberrant using a mixture item response theory (IRT) modeling approach, and aberrant response behavior is described and modeled using item response trees (IRTrees). Simulations are conducted to evaluate the efficiency and quality of the new class of mixture IRTree model using WinBUGS with Bayesian estimation. The results show that the parameter recovery is satisfactory for the proposed mixture IRTree model and that treating missing values as ignorable or incorrect and ignoring possible performance decline results in biased estimation. Finally, the applicability of the new model is illustrated by means of an empirical example based on the Program for International Student Assessment.

中文翻译：

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性能下降和不可忽略缺失数据的混合 IRTree 模型

在教育评估和成就测试中，测试开发人员和管理人员通常假设应试者会全力尝试所有测试项目，并且不会留下任何带有计划外缺失值的空白答案。然而，异常的反应行为——例如表现下降、超过某个点的辍学以及在测试过程中跳过某些项目——是不可避免的，尤其是由于低动机和时间限制的低风险评估和快速测试，分别. 在这项研究中，使用混合项目反应理论 (IRT) 建模方法将应试者分为正常或异常，并使用项目响应树 (IRTrees) 描述和建模异常反应行为。使用带有贝叶斯估计的 WinBUGS 进行模拟以评估新类别混合 IRTree 模型的效率和质量。结果表明，所提出的混合 IRTree 模型的参数恢复是令人满意的，并且将缺失值视为可忽略或不正确并忽略可能的性能下降会导致估计有偏差。最后，通过一个基于国际学生评估项目的实证例子来说明新模型的适用性。