对极端响应风格和基于特征的展开响应的项目响应树模型中的方向不变性进行调查
1University of Illinois at Urbana-Champaign, IL, USA.
Applied psychological measurement
|July 26, 2024
概括
项目响应树 (IRT) 模型现在可以分析展开的响应,将协议和强度分开. 这种新方法允许在心理结构中进行定向不变性测试.
科学领域:
- 心理测量 心理测量 心理测量
- 项目响应理论.
- 统计建模 统计建模
背景情况:
- 项目响应树 (IRT) 模型通过分离协议和强度决策来提供细微分析.
- 方向不变性至关重要,因为物品和人参数可能因协议方向而有所不同.
研究的目的:
- 调查一个新的IRT模型家族,以展开反应.
- 在协议和强度决策中纳入过度的共因数和分级响应模型.
- 在这个框架内,检查项目和人级的方向不变性.
主要方法:
- 开发了一系列IRT模型,整合了过度的共弦值和分级响应模型.
- 通过JAGS进行了模拟研究,以评估参数恢复,使用贝叶斯方法.
- 将模型应用于两个真实世界的数据集,比较具有不同方向不变性和过程假设的模型.
主要成果:
- 拟议的IRT模型有效地处理展开的响应过程.
- 通过模拟评估参数恢复,证明了模型的实用性.
- 模型比较揭示了对方向不变和展开与主导过程的洞察力.
结论:
- 新的IRT模型家族为分析展开的响应提供了一个强大的框架.
- 这种方法有助于在心理测量中研究方向不变性.
- 开发了可视化物件响应运作的方法,提高了可解释性.
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