在混合措辞的尺度中建模不足的努力反应
1Hong Kong Examinations and Assessment Authority, 7/F, Dah Sing Financial Centre, 248 Queen's Road East, Wan Chai, Hong Kong. kyjin@hkeaa.edu.hk.
在调查中忽略不足的努力响应 (IER),即使是反向编码的项目,也会降低可靠性并引入偏见. 这项研究强调了不考虑IER对调查数据准确性的负面影响.
科学领域:
- 心理测量 心理测量 心理测量
- 调查方法 调查方法
- 统计建模 统计建模
背景情况:
- 调查通常使用反向编码项目来检测不足的努力反应 (IER).
- 一个常见的假设是,受访者始终竭尽全力,这往往是错误的.
- 忽视IER可以导致错误的调查数据和不准确的结论.
研究的目的:
- 扩大用于识别IER的混合模型.
- 证明忽视IER对调查数据质量的不利影响.
- 用现实世界的数据集来说明这些方法的实际应用.
主要方法:
- 扩展的混合物模型不足的努力反应 (IERs).
- 使用LatentGOLD软件进行模拟研究.
- 应用于两个公共数据集:马基雅维利主义和自我报告的抑郁症等级.
主要成果:
- 忽视IER会对测试可靠性产生负面影响.
- 不考虑IER引入了对调查数据的偏见.
- 当忽略IER时,观察到斜率和截面参数的精度降低.
结论:
- 扩展混合模型为处理IER提供了一个强大的方法.
- 在调查数据分析中忽视IER会导致显著的心理测量和统计问题.
- 准确的分析需要考虑受访者的努力,无论问题的措辞如何.
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