一个潜在空间等级响应模型用于利克特尺度心理评估
Ludovica De Carolis1, Inhan Kang2, Minjeong Jeon3
1Department of Economics, Management and Statistics, Università degli Studi di Milano-Bicocca, Milan, Italy.
Multivariate behavioral research
|December 30, 2025
概括
这项研究引入了一个新的隐性空间分级响应模型 (LSGRM) 用于顺序数据. LSGRM可视化了项目-响应者交互,通过解决心理评估中的条件依赖来改进传统模型.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 心理测量 心理测量
背景情况:
- 传统的心理测量模型通常假设有条件的独立性,这可能不适用于顺序响应数据.
- 现有的模型可能无法完全捕捉受访者和项目之间的复杂相互作用.
- 分析利克特尺度数据需要尊重其顺序性质的方法.
研究的目的:
- 为顺序响应数据引入一种新的隐性空间分级响应模型 (LSGRM).
- 通过结合未观察到的相互作用来扩展单参数分级响应模型的功能.
- 为探索心理评估中的条件依赖提供一个可视化工具.
主要方法:
- 开发了一个潜在空间分级响应模型 (LSGRM),使用二维欧几里德空间进行受访者-项目交互.
- 利用模拟研究来评估LSGRM的性能.
- 对利克特尺度心理评估数据进行实证研究.
- 将LSGRM与其他模型进行比较,包括分析二分法或连续顺序数据的模型.
主要成果:
- 拟议的LSGRM有效地通过计算未观察到的受访者-项目交互来建模顺序响应数据.
- LSGRM解决了传统模型固有的有条件独立性假设的违反问题.
- 交互地图为理解条件依赖提供了一个新的可视化.
- 分析揭示了数据二分化和持续治疗对条件依赖的影响.
结论:
- 潜空间分级响应模型 (LSGRM) 为分析普通心理数据提供了显著的进步.
- 通过捕捉复杂的相互作用和改善条件依赖的处理,LSGRM增强了心理测量建模.
- 该模型的可视化功能有助于解释响应模式和项目特征.
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