理想点还是主导过程? 展开树采用多过程模型对利克特尺度数据的方法
Biao Zeng1, Hongbo Wen1, Minjeong Jeon2
1Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, China.
Multivariate behavioral research
|May 27, 2025
概括
本研究为利克特尺度数据分析提供了新的展开树 (UTree) 模型. 这些模型准确地捕捉了理想点响应过程和潜在特征,当数据与理想点假设保持一致时,它们的性能优于现有方法.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 行为科学 行为科学
背景情况:
- 利克特尺度数据分析通常依赖于对响应过程的假设.
- 现有的项目响应树 (IRTree) 模型可能无法完全捕捉理想点响应行为.
- 开发强大的分析框架对于理解潜在特征至关重要.
研究的目的:
- 介绍和评估三种新的展开树 (UTree) 模型,用于利克特尺度数据.
- 将UTree模型的性能与已建立的项目响应树 (IRTree) 模型进行比较.
- 调查受访者的决策过程和潜在的特征结构.
主要方法:
- 根据理想点假设开发了三种新的展开树 (UTree) 模型.
- 进行模拟研究以评估不同条件下的模型性能.
- 将UTree和IRTree模型应用于实证数据进行比较分析.
主要成果:
- 合适度指数有效地区分了正确和不正确的模型.
- 无论UTree还是IRTree模型都在正确指定时准确地恢复参数,对于更大的样本大小和更多项目的精度提高.
- 错误指定的模型产生了偏差的个别参数估计,特别是在理想点响应过程中.
- 经验数据支持了优势过程中的理想点响应过程.
- 受访者的极端反应选择主要是由目标特征驱动的,而不是极端反应风格.
- 确定了两个不同的,中度相关的目标特征,影响跨阶段的决策.
结论:
- 提出的展开树 (UTree) 模型为分析利克特尺度数据提供了有效的框架,特别是在存在理想点过程时.
- 理想点假设比主导过程更好地解释了受访者的决策和反应模式.
- 研究结果强调了选择适当模型的重要性,以准确估计潜在特征并了解反应行为.
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