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一个IRTree模型的插图,用于脱离参与

Brian C Leventhal¹, Dena Pastor¹

¹James Madison University, Harrisonburg, VA, USA.

Educational and psychological measurement

|July 26, 2024

概括

本研究引入了一种IRTree模型,以调整受试者的快速猜测能力,而不需要删除参与者. 该模型准确地估计了脱离,并改善了能力测量,为快速响应者提供了不那么有偏见的结果.

科学领域:

教育测量教育的测量
心理测量心理测量心理测量
隐性特征理论隐性特征理论

背景情况:

低风险测试的表现受考生能力和努力的影响.
快速猜测行为表明努力较低,但现有的调整方法有局限性.
以前的方法通常依赖于强有力的假设,或者排除了低力度的受试者.

研究的目的:

展示IRTree (物件响应理论树) 模型在快速猜测的情况下调整考生能力得分的实用性.
提出一种灵活的方法,不假设快速猜测和能力之间的独立性.
为了同时建模脱潜伏的特征与主要的特征的兴趣.

主要方法:

利用IRTree模型分析受试者响应数据,特别是识别和计算快速猜测.
开发了一个建模框架,以适应快速猜测和考生能力之间的潜在依赖.
结合了脱离特征和主要感兴趣特征的同时估计.

主要成果:

IRTree模型有效地估计了脱离隐藏特征的个体差异.
与忽视或以不同的方式适应快速猜测的模型相比,拟议的方法提供了更精确的测量受试者的能力.
模拟研究证实,该模型对快速反应的个体产生了对感兴趣特征的不太偏见的估计.

结论:

在存在快速猜测行为时,IRTree模型提供了一个灵活有效的解决方案来调整能力估计.
这种方法提高了能力测量的准确性,而不需要取消考生或强烈的独立性假设.
未来的研究可以探索这种同时建模技术的进一步扩展和应用.

关键词:

这是一个IRTree树.解除干预解除干预努力适度的努力适度的快速猜测快速猜测快速猜测快速猜测

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