调查响应策略中的异质性:一种混合多维IRTree方法
Ö Emre C Alagöz1, Thorsten Meiser1
1University of Mannheim, Germany.
Educational and psychological measurement
|September 25, 2024
概括
研究人员可以通过考虑响应风格 (RS) 效应来提高自我报告的有效性. 一个新的混合多维IRTree (MM-IRTree) 模型识别了个体之间的不同反应策略,优于传统模型.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 调查方法 调查方法
背景情况:
- 自我报告措施在研究中至关重要,但可以受到响应风格 (RS) 影响的影响.
- 传统的物品响应理论 (IRT) 模型,如IRTree,在所有受访者中都假设统一的RS.
- 在RS效应的性质和强度的个体差异 (例如,中点RS,极端RS) 往往被忽视.
研究的目的:
- 引入一种新的统计模型,即混合多维IRTree (MM-IRTree),以解决响应策略的异质性.
- 在潜在的类框架内检测和建模响应风格 (中点和极端) 的个体差异.
- 通过考虑各种响应策略来提高自我报告措施的有效性.
主要方法:
- 开发混合多维IRTree (MM-IRTree) 模型,结合基于响应策略配置文件的四个潜在类.
- 特定类别的策略包括:只有极端RS,只有中点RS,两个RS,没有RS.
- 在混合响应策略条件下,模拟研究评估MM-IRTree与传统IRTree模型的性能.
主要成果:
- 在模拟中,MM-IRTree模型在参数恢复和类成员身份识别方面表现强.
- 传统的IRTree模型显示,当响应策略在人群中混合时,性能显著下降.
- 经验数据分析证实了具有实质大小的独特隐性类的存在,支持MM-IRTree的实用性.
结论:
- MM-IRTree模型有效地捕捉了响应策略中的异质性,为分析自我报告数据提供了更有效的方法.
- 承认和建模响应风格的个体差异对于心理和社会科学中准确的测量至关重要.
- 拟议的模型为寻求提高自我报告措施的有效性和可解释性的研究人员提供了有价值的工具.
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