互模型充满活力作为一种镜头来理解 (和量化) 对二分歧编码项目的项目响应模型的价值
Benjamin W Domingue1, Klint Kanopka1, Radhika Kapoor1
1Stanford University.
Psychometrika
|February 25, 2026
概括
我们介绍了InterModel Vigorish (IMV),这是一个用于量化心理测量中的统计模型准确性的新指数. IMV提供了一种便携式和可通用的方法,用于在各种环境和模型中比较预测性能.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
背景情况:
- 统计模型,包括项目响应理论中的模型,需要索引来评估它们对特定数据的适当性.
- 现有的指数可能在比较非嵌套模型或跨语境概括方面存在局限性.
研究的目的:
- 引入和评估InterModel Vigorish (IMV) 作为一种新的指数,用于量化二分化的项目响应模型的准确性.
- 证明IMV在不同模拟和实证数据集中比较模型预测性能的实用性.
主要方法:
- 国际模型精力测试 (IMV) 是为了根据两组预测之间的预测改进来量化准确度而开发的.
- 使用模拟数据进行了比较分析,以将IMV与AIC和RMSEA等常见指数进行对比.
- 经验验证涉及将IMV应用于89个分类项目响应数据集.
主要成果:
- IMV展示了一些可取的特征,包括可移植性和通用性,使其适合用于比较非嵌套模型.
- 在IMV和其他常见指数之间观察到行为上的定性差异.
- 经验应用证实了IMV的实际实用性,并证明了IMV的性能.
结论:
- 跨模型活力 (IMV) 是用于评估统计模型准确性的心理测量学中一个有价值的新指标.
- 它能够在各种环境中轻松比较预测,这使得它对研究人员的实用性更大.
- IMV为评估和比较项目响应模型提供了可靠的衡量标准.
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