Item nonresponses are prevalent in standardized testing. They happen either when students fail to reach the end of a test due to a time limit or quitting, or when students choose to omit some items strategically. Oftentimes, item nonresponses are nonrandom, and hence, the missing data mechanism needs to be properly modeled. In this paper, we proposed to use an innovative item response time model as a cohesive missing data model to account for the two most common item nonresponses: not‐reached items and omitted items. In particular, the new model builds on a behavior process interpretation: a person chooses to skip an item if the required effort exceeds the implicit time the person allocates to the item (Lee & Ying, 2015; Wolf, Smith, & Birnbaum, 1995), whereas a person fails to reach the end of the test due to lack of time. This assumption was verified by analyzing the 2015 PISA computer‐based mathematics data. Simulation studies were conducted to further evaluate the performance of the proposed Bayesian estimation algorithm for the new model and to compare the new model with a recently proposed “speed‐accuracy + omission” model (Ulitzsch, von Davier, & Pohl, 2019). Results revealed that all model parameters could recover properly, and inadequately accounting for missing data caused biased item and person parameter estimates.

中文翻译：

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未到达和遗漏物品的响应时间过程模型

项目无响应在标准化测试中很普遍。当学生由于时间限制或退出而未能达到测试的结束时，或者当学生选择策略性地省略某些项目时，就会发生这种情况。通常，项目无响应是非随机的，因此，需要对丢失的数据机制进行正确建模。在本文中，我们建议使用创新的项目响应时间模型作为内聚的缺失数据模型，以解决两种最常见的项目无响应：未到达项目和遗漏项目。特别是，新模型建立在行为过程解释的基础上：如果所需的努力超过了该人分配给项目的隐式时间，则该人选择跳过该项目（Lee＆Ying，2015； Wolf，Smith和Birnbaum，1995）。 ，而一个人由于时间不足而无法到达测试的结尾。通过分析2015年PISA基于计算机的数学数据，验证了该假设。进行了仿真研究，以进一步评估针对新模型提出的贝叶斯估计算法的性能，并将新模型与最近提出的``速度精度+遗漏''模型进行比较（Ulitzsch，von Davier和Pohl，2019年）。结果表明，所有模型参数都可以正确恢复，并且由于数据缺失而导致的项目和人员参数估计值有偏差，因此不能充分考虑。