What are the Characteristics of Respondents using Different Devices in Mixed-device Online Surveys? Evidence from Six UK Surveys
What are the Characteristics of Respondents using Different Devices in Mixed-device Online Surveys? Evidence from Six UK Surveys
复制标题
混合设备在线调查中使用不同设备的受访者有何特点？
DOI：
10.1111/insr.12311
复制
发表时间：
2019
影响因子：
2
通讯作者：
Maslovskaya O
中科院分区：
文献类型：
--
作者：
Maslovskaya O
世界各地都在转向在线数据收集。在线调查响应因受访者使用不同设备而变得复杂。到目前为止，英国还没有进行过研究，以研究在混合设备在线调查中使用不同设备的人的特征。这项分析使用了所有公开的英国社会调查与在线组件：了解社会创新小组，社区生活调查，欧洲社会调查，1958年全国儿童发展研究和第二次纵向研究的年轻人在英格兰。描述性分析和逻辑回归用于探索在线调查中器械使用的显著相关性。双变量分析的结果表明，年龄，性别，婚姻状况，就业，宗教信仰，家庭规模，家庭中的孩子，家庭收入，汽车的数量和互联网的使用频率与调查中使用的设备显着相关。与年龄、性别、就业状况、家庭规模和教育程度的关联与其他国家的研究结果一致。了解英国在线调查中使用不同设备的受访者的特征，将有助于更好地了解在线调查的响应过程，并更有效地针对某些亚组。这对在线调查的设计、对数据质量的理解和调查后的调整也很重要。
There is a move towards online data collection across the world. Online survey response is complicated by respondents using different devices. So far, no research has been conducted in the UK to study characteristics of people using different devices in mixed‐device online surveys. This analysis usesallpublicly available UK social surveys with an online component: Understanding Society Innovation Panel, Community Life Survey, European Social Survey, 1958 National Child Development Study and the Second Longitudinal Study of Young People in England. Descriptive analysis and logistic regressions are used to explore significant correlates of device use in online surveys. The results of bivariate analysis suggest that age, gender, marital status, employment, religion, household size, children in household, household income, number of cars and frequency of internet use are significantly associated with device used across surveys. The associations with age, gender, employment status, household size and education are consistent with the findings from other countries. The knowledge about respondents' characteristics using different devices in online surveys in the UK will help to understand better the response process in online surveys and to target certain subgroups more effectively. It is also important for designs of online surveys, understanding of data quality and post‐survey adjustments.