以后的预测检查用于检测极端响应风格的检测
Martijn Schoenmakers1, Jesper Tijmstra2, Jeroen Vermunt2
1Department of Methodology and Statistics, Tilburg University, Warandelaan 2, 5037AB, Tilburg, The Netherlands. m.schoenmakers@tilburguniversity.edu.
Behavior research methods
|July 27, 2025
概括
检测极端响应风格 (ERS) 对于有效的问卷结果至关重要. 贝叶斯后期预测检查 (PPC) 提供了一种灵活的新方法,用于在没有强有力的假设的情况下在统计模型中识别ERS.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
- 心理测量 心理测量
背景情况:
- 极端的响应风格 (ERS) 可以损害利克特类问卷数据的有效性.
- 现有的检测ERS的方法存在局限性,包括将ERS与测量特征相结合,或需要复杂的统计模型.
- 需要可访问和可靠的方法来识别团体和个人层面的ERS.
研究的目的:
- 引入贝叶斯后部预测检查 (PPC) 作为检测极端响应风格 (ERS) 的可行替代方案.
- 在一般化部分信贷模式框架内应用PPC,以识别与ERS相关的模型不合适.
- 评估各种针对ERS检测的PPC的性能.
主要方法:
- 贝叶斯后置预测检查 (PPCs) 用于模型不合适检测.
- 用一个通用的部分信用模型来分析利克尔特类问卷数据.
- PPCs被特别调整和测试,以检测它们在群体和个人层面上检测ERS的能力.
主要成果:
- 该研究表明,PPC可以有效地检测与ERS有关的模型不适合.
- 为ERS量身定制的各种PPC被提议并用实证示例说明.
- 在不同的条件下检查了这些PPC的性能,显示了它们在识别ERS中的实用性.
结论:
- 贝叶斯后期预测检查提供了一个灵活而强大的工具,用于检测心理测量数据中的极端响应风格.
- PPC提供了一个优势,因为它不需要关于ERS性质 (例如,连续或分类) 的强有力的假设.
- 拟议的PPC适用于常用的单维物件响应理论模型,为研究人员提供实用解决方案.
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