在连续边界响应中建模响应风格:模型开发和验证
Youxiang Jiang1, Biao Zeng1, Siwei Peng2
1Collaborative Innovation Center of Assessment for Basic Education Quality, Beijing Normal University, Beijing, China.
这项研究引入了连续数据的新项目响应模型,有效地测量极端和中点响应风格. 该模型显示了强大的有效性,并减轻了对连续测量的响应风格影响.
科学领域:
- 心理测量
- 统计模型
- 调查方法
背景情况:
- 对利克特尺度数据建立了项目响应理论模型,但对于视觉模拟尺度 (VAS) 和滑动条等连续格式的模型开发较少.
- 响应风格,如极端响应风格 (ERS) 和中点响应风格 (MRS),可以在连续测量格式中偏差结果.
- 现有的模型往往无法在连续数据中充分解决或隔离这些响应风格.
研究的目的:
- 提出一个新的项目响应模型框架来分析连续边界响应格式.
- 灵活地将内容特征,极端响应风格 (ERS) 和中点响应风格 (MRS) 纳入统一模型.
- 验证模型准确估计ERS和MRS的能力,并减轻它们对观察到的反应的影响.
主要方法:
- 使用伪响应的等级项目响应模型框架的开发.
- 使用连续边界响应数据进行实证验证,以评估ERS和MRS估计.
- 使用马尔科夫链蒙特卡洛 (MCMC) 方法来评估参数恢复的模拟研究.
主要成果:
- 与现有方法相比,拟议的模型对连续受限响应数据具有更好的适应性.
- 该模型有效估计了极端响应风格 (ERS) 和中点响应风格 (MRS).
- 马尔科夫链蒙特卡洛 (MCMC) 方法在各种模拟条件中准确地恢复了模型参数.
结论:
- 新的项目响应模型框架为分析连续边界响应数据提供了强大的有效方法.
- 该模型成功地隔离和量化反应风格 (ERS,MRS),减轻它们对观察到的反应的不良影响.
- 这一框架推进了连续测量仪器的心理测量建模,提高了数据的准确性和可解释性.
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