调查管理模式对响应概况的影响是可以预测的,并且在不同国家都很强大:来自29个国家使用机器学习模型的证据
1Faculty of Data and Decision Sciences, Technion-Israel Institute of Technology, Haifa, Israel.
PloS one
|September 12, 2025
概括
调查数据收集模式,如面对面的面试与自我完成,显示可靠的跨国差异. 机器学习预测了调查管理,表明不同模式的数据在不同国家之间是不可比较的.
科学领域:
- 社会科学 社会科学 社会科学
- 方法论 方法论 方法论
- 跨国研究跨国研究
背景情况:
- 调查数据采集采用了各种模式,包括面对面的采访和自我填写方法 (纸和笔,网络).
- 跨国调查,如欧洲社会调查 (ESS),对于比较社会研究至关重要.
- 由于COVID-19大流行,一些ESS国家需要转向自补模式,这引发了数据可比性问题.
研究的目的:
- 调查通过不同管理模式收集的调查数据是否可靠地在不同国家之间存在差异.
- 评估通过面对面采访收集的数据与跨国研究中的自我完成方法的可比性.
- 为了确定调查模式之间的响应概况的潜在差异,例如极端响应风格.
主要方法:
- 利用了来自29个国家的145,361名受访者在欧洲社会调查 (ESS) 8-10轮的数据.
- 采用机器学习模型,根据受访者数据预测调查管理模式.
- 分析了响应概况,包括极端响应风格,以确定模式特定的差异.
主要成果:
- 机器学习模型成功预测了调查管理模式,表明不同模式的数据不能直接比较.
- 在某些国家的数据上训练的模型可以预测新的国家的管理模式,证实可靠的跨国差异.
- 在面对面和自我完成调查模式之间观察到响应概况的显著差异,包括极端响应风格.
结论:
- 调查管理模式导致可靠的跨国响应配置文件差异,影响数据的可比性.
- 调查结果强调了在跨国研究中需要考虑调查模式影响的必要性,特别是在像ESS这样的项目中.
- 未来的跨国调查研究必须仔细考虑并可能减轻基于模式的数据差异.
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