The Role of Time, Weather and Google Trends in Understanding and Predicting Web Survey Response

TL;DR

This study investigates how time, weather, and societal trends influence web survey response rates, demonstrating that these factors significantly affect response patterns and can predict responses without privacy concerns.

Contribution

It introduces a novel approach using time-varying contextual factors like weather and Google Trends data to predict survey response rates, expanding beyond traditional static demographic factors.

Findings

Weekends, holidays, and pleasant weather reduce response rates.

Disease outbreaks and terrorism salience increase response rates.

Models using these variables accurately predict future response patterns.

Abstract

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.