对敏感主题调查的交叉模型:对项目选择和统计分析的一般框架
Marco Gregori1, Martijn G De Jong2, Rik Pieters3
1Department of Marketing (Room 3.201), Warwick Business School, University of Warwick, Scarman Road, Coventry, CV4 7AL, UK. Marco.Gregori@wbs.ac.uk.
Psychometrika
|May 28, 2024
概括
这项研究引入了一种新的方法来选择交叉模型中的基线项目,改善对敏感主题的真实调查答案. 改进的方法和贝叶斯估计提供了更有效和可靠的数据收集.
科学领域:
- 调查方法 调查方法
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
背景情况:
- 关于敏感话题的直接问题可能会导致反应偏差.
- 间接询问技术旨在通过隐藏答案来提高数据的准确性.
- 交叉式模型使用对联的敏感和非敏感项目来获得真实的答案.
研究的目的:
- 开发一种综合的方法来选择横向模型中的基线项目.
- 为这些模型提出新的贝叶斯估计方法.
- 与现有的横向模型应用相比,证明提高效率和放松假设.
主要方法:
- 开发了基于概念和统计标准的基线项目选择方法.
- 在这个框架内引入了四种不同的统计模型.
- 为实施模型提出了新的贝叶斯估计技术.
主要成果:
- 新的方法比常见的横向模型应用程序提供了更高的效率.
- 拟议的模型可能会放松之前所需的统计假设.
- 对LGBT态度的实证应用表明了Crosswise模型的潜力.
结论:
- 综合方法和贝叶斯估计提高了横向模型的实用性.
- 提高效率和放宽假设有助于更广泛的应用.
- 可用的工具 (应用程序,代码) 支持更广泛地采用该方法.
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