Effects of survey administration mode on response profiles are predictable, and robust across countries: Evidence from 29 countries using machine-learning models

调查管理方式对回答特征的影响是可预测的,并且在各国之间具有稳健性:来自 29 个国家/地区的机器学习模型证据

阅读:2

Abstract

Survey data collection can be administered in different modes, including face-to-face interviews and self-completion modes such as paper-and-pencil or web-based surveys. Do data collected in these different modes reliably differ across countries? We address this question using responses from 145,361 respondents in 29 countries to 46 questions in Rounds 8-10 of the European Social Survey (ESS), a large-scale social research project that conducts cross-national surveys in Europe and whose data has been used in thousands of publications. The ESS is typically administered in face-to-face interviews, but due to the COVID-19 pandemic data from nine countries in the tenth round were collected using self-completion methods. In line with previous findings demonstrating differences between administration modes, we show that machine-learning models can predict how surveys were administered, suggesting that data collected in the different modes are not comparable. More critically, we show that even when these models are trained on data from a set of countries, they can predict how surveys were administered in a completely novel country, which indicates that responses in different administration modes reliably differ across countries. Finally, we investigate extreme response styles as one difference in the response profiles of the two different modes. In addition to addressing concerns of data comparability in the ESS, these findings reveal that administration modes of surveys lead to reliable cross-national differences in response profiles.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。