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首页> 外文期刊>Applied Psychological Measurement >Parameter Drift Detection in Multidimensional Computerized Adaptive Testing Based on Informational Distance/Divergence Measures
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Parameter Drift Detection in Multidimensional Computerized Adaptive Testing Based on Informational Distance/Divergence Measures

机译：基于信息距离/散度测度的多维计算机自适应测试中的参数漂移检测

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摘要

An informational distance/divergence-based approach is proposed to detect the presence of parameter drift in multidimensional computerized adaptive testing (MCAT). The study presents significance testing procedures for identifying changes in multidimensional item response functions (MIRFs) over time based on informational distance/divergence measures that capture the discrepancy between two probability functions. To approximate the MIRFs from the observed response data, the k-nearest neighbors algorithm is used with the random search method. A simulation study suggests that the distance/divergence-based drift measures perform effectively in identifying the instances of parameter drift in MCAT. They showed moderate power with small samples of 500 examinees and excellent power when the sample size was as large as 1,000. The proposed drift measures also adequately controlled for Type I error at the nominal level under the null hypothesis.
机译：提出了一种基于信息距离/差异的方法来检测多维计算机自适应测试（MCAT）中参数漂移的存在。这项研究提出了重要的测试程序，用于基于信息距离/差异度量来识别多维项目响应函数（MIRF）随时间的变化，该度量捕获了两个概率函数之间的差异。为了从观察到的响应数据中近似MIRF，k最近邻算法与随机搜索方法一起使用。仿真研究表明，基于距离/散度的漂移测量可以有效地识别MCAT中参数漂移的实例​​。他们在500名受检者的小样本中表现出中等能力，而在样本量大至1,000项时，他们表现出优异的能力。在零假设下，建议的漂移措施也可以将名义上的I型误差充分控制在名义水平上。

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