互模型充满活力作为一种镜头,用于理解 (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 Kanopka2, Radhika Kapoor2
1Graduate School of Education, Stanford University, Santa Clara, USA. ben.domingue@gmail.com.
Psychometrika
|June 3, 2024
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
- 数据分析 数据分析
背景情况:
- 统计模型需要指数来评估特定数据背景的适当性.
- 项目响应理论模型被广泛使用,但需要强大的评估指标.
研究的目的:
- 引入InterModel Vigorish (IMV) 作为一种用于量化模型准确性的新型指数.
- 评估IMV的实用性,以比较二元化的项目响应模型.
主要方法:
- IMV通过测量两组预测之间的预测改进来量化准确性.
- 使用模拟和实证数据,将IMV与AIC和RMSEA等现有指数进行比较.
- 分析了89个分类项目响应数据集,以证明IMV的实际应用.
主要成果:
- IMV展示了包括可移植性和可泛化性在不同数据环境中的可取性特征.
- 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在实际心理测量分析中的实用性.
结论:
- 跨模型活力 (IMV) 是一个有价值的新指数,用于评估心理测量中的统计模型准确性.
- IMV 便于在各种模型和环境中轻松比较预测.
- 该索引的可移植性和通用性提高了它对研究人员的有用性.
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