评估数据的网络方法的最近发展:情报研究的潜在空间项目响应建模
1Department of Psychology, College of Liberal Arts, Yonsei University, Seoul 03722, Republic of Korea.
Journal of Intelligence
|April 26, 2024
概括
隐藏空间物体响应模型 (LSIRM) 通过整合心理测量和网络数据来增强情报研究. 它揭示了隐藏的模式,并允许超越传统模式的个性化反.
科学领域:
- 心理测量 心理测量 心理测量
- 网络分析 网络分析
- 情报研究 情报研究 情报研究
背景情况:
- 像拉什模型这样的传统物品响应理论 (IRT) 模型捕捉了人的能力和物品困难.
- 这些模型通常假定项目响应是条件独立的,这在复杂的数据集中可能不成立.
- 网络数据分析提供了模拟关系和依赖关系的工具,但将其与心理测量数据集成是具有挑战性的.
研究的目的:
- 为情报研究引入和探索隐藏空间物体响应模型 (LSIRM).
- 通过结合潜在空间概念来证明LSIRM与传统Rash模型的优势.
- 展示LSIRM在发现条件依赖和产生个性化见解方面的实用性.
主要方法:
- LSIRM将Rasch IRT模型与潜空间建模相结合.
- 它将人员和物品映射到一个共享的度量空间 (隐藏空间).
- 潜空间中的距离量化了额外的响应精度下降和条件依赖.
主要成果:
- 隐藏空间提供了关于受访者和拉什模型之外的项目独特的信息.
- LSIRM可以量化和可视化不同个体物品难度的变化.
- 隐藏的维度和个人和物品的集群可以使用它们的隐藏位置来识别.
- 个性化反可以根据隐藏空间中的人-物体距离生成.
结论:
- 在情报研究中,LSIRM提供了一个强大的框架来分析复杂的心理测量数据.
- 该模型有效地解释了条件依赖,并提供了比标准IRT模型更丰富的见解.
- 未来的研究可以探索将潜在空间建模与其他先进的心理测量和网络模型集成在一起.
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