  In the literature about web survey methodology, significant efforts have been
made to understand the role of time-invariant factors (e.g. gender, education
and marital status) in (non-)response mechanisms. Time-invariant factors alone,
however, cannot account for most variations in (non-)responses, especially
fluctuations of response rates over time. This observation inspires us to
investigate the counterpart of time-invariant factors, namely time-varying
factors and the potential role they play in web survey (non-)response.
Specifically, we study the effects of time, weather and societal trends
(derived from Google Trends data) on the daily (non-)response patterns of the
2016 and 2017 Dutch Health Surveys. Using discrete-time survival analysis, we
find, among others, that weekends, holidays, pleasant weather, disease
outbreaks and terrorism salience are associated with fewer responses.
Furthermore, we show that using these variables alone achieves satisfactory
prediction accuracy of both daily and cumulative response rates when the
trained model is applied to future unseen data. This approach has the further
benefit of requiring only non-personal contextual information and thus
involving no privacy issues. We discuss the implications of the study for
survey research and data collection.
