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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.

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概括
此摘要是机器生成的。

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.

关键词:
项目响应树模型占主导地位的过程.极端的响应风格 极端的响应风格理想点的过程是理想点的过程.隐藏的特征 隐藏的特征利克尔特尺度是一个尺度.展开的树模型模型

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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.