The increased availability of time-related information as a result of computer-based assessment has enabled new ways to measure test-taking engagement. One of these ways is to distinguish between solution and rapid guessing behavior. Prior research has recommended response-level filtering to deal with rapid guessing. Response-level filtering can lead to parameter bias if rapid guessing depends on the measured trait or (un-)observed covariates. Therefore, a model based on Mislevy and Wu (1996) was applied to investigate the assumption of ignorable missing data underlying response-level filtering. The model allowed us to investigate different approaches to treating response-level filtered responses in a single framework through model parameterization. The study found that lower-ability test-takers tend to rapidly guess more frequently and are more likely to be unable to solve an item they guessed on, indicating a violation of the assumption of ignorable missing data underlying response-level filtering. Further ability estimation seemed sensitive to different approaches to treating response-level filtered responses. Moreover, model-based approaches exhibited better model fit and higher convergent validity evidence compared to more naïve treatments of rapid guessing. The results illustrate the need to thoroughly investigate the assumptions underlying specific treatments of rapid guessing as well as the need for robust methods.

中文翻译：

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快速猜测的基于模型的处理

由于基于计算机的评估，与时间相关的信息的可用性增加，这使得衡量应试参与度的新方法成为可能。其中一种方法是区分解决方案和快速猜测行为。先前的研究建议使用响应级别过滤来处理快速猜测。如果快速猜测取决于测量的特征或（未）观察到的协变量，则响应级别过滤可能会导致参数偏差。因此，应用基于 Mislevy 和 Wu (1996) 的模型来研究作为响应级别过滤基础的可忽略缺失数据的假设。该模型使我们能够通过模型参数化研究在单个框架中处理响应级别过滤响应的不同方法。研究发现，能力较低的应试者往往会更频繁地快速猜测，并且更有可能无法解决他们猜测的项目，这表明违反了作为响应级别过滤基础的可忽略缺失数据的假设。进一步的能力估计似乎对处理响应级别过滤响应的不同方法很敏感。此外，与更简单的快速猜测处理相比，基于模型的方法表现出更好的模型拟合和更高的收敛有效性证据。结果表明，需要彻底调查快速猜测的特定处理背后的假设以及对稳健方法的需要。进一步的能力估计似乎对处理响应级别过滤响应的不同方法很敏感。此外，与更简单的快速猜测处理相比，基于模型的方法表现出更好的模型拟合和更高的收敛有效性证据。结果表明，需要彻底调查快速猜测的特定处理背后的假设以及对稳健方法的需要。进一步的能力估计似乎对处理响应级别过滤响应的不同方法很敏感。此外，与更简单的快速猜测处理相比，基于模型的方法表现出更好的模型拟合和更高的收敛有效性证据。结果表明，需要彻底调查快速猜测的特定处理背后的假设以及对稳健方法的需要。