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What are the Characteristics of Respondents using Different Devices in Mixed‐device Online Surveys? Evidence from Six UK Surveys
Author(s) -
Maslovskaya Olga,
Durrant Gabriele B.,
Smith Peter W.F.,
Hanson Tim,
Villar Ana
Publication year - 2019
Publication title -
international statistical review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.051
H-Index - 54
eISSN - 1751-5823
pISSN - 0306-7734
DOI - 10.1111/insr.12311
Subject(s) - bivariate analysis , descriptive statistics , data collection , marital status , logistic regression , the internet , psychology , general social survey , household income , survey data collection , geography , demography , social psychology , sociology , computer science , statistics , population , world wide web , social science , mathematics , archaeology , machine learning
Summary 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 uses all publicly 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.