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

Using Google Trends Data to Learn More About Survey Participation

“…Consequently, we will refrain from giving a naïve rule of thumb such as "combine samples of one week/month," and instead suggest that researchers start combining multiple samples and continue this approach until adding more samples no longer changes the distribution of search queries for their search term(s) of interest. Alternatively, Fang et al (2021) have suggested a resampling approach using overlapping shifting timeframes that each cover the period of interest. These researchers also cautioned that GT servers would only allow the retrieval of a maximum of 244 daily samples per inquiry and search term.…”

“…Testing this assumption in future research is an important task for better understanding the potential of GT data. We suggest that such studies could apply the approach used by Fang et al (2021)-overlapping samples of real-time data, each covering a shorter timeframe that then is compared between samples. Since different periods likely will have comparability issues due to the different reference values on which the RSVs are scaled, we suggest also considering whether the calibration approach suggested by Fang et al (2021) could be applied.…”

“…Strictly speaking, however, absolute frequencies would be needed to test this assumption and to assess how many potential respondents searched for information about the survey. Absolute frequencies are even more important for studies that follow the approach of Fang et al (2021) who propose collecting data about the salience of societal trends that are relevant for survey participation. Potential solutions for this challenge might be to query additional search terms (i.e., an external baseline) for which GT then adjusts the RSVs.…”

“…To the best of our knowledge, only the study by Fang et al (2021) explored the use of search engine data to learn about survey participation. These authors used search engine data to predict participation in the Dutch Health Surveys.…”

“…In their study, they used Google Trends (GT) to capture the salience of societal issues-which they considered relevant for survey participation-during the fieldwork phase of the survey (i.e., disease outbreaks, privacy concerns, public outdoor engagement, and terrorism salience). While Fang et al (2021) promoted a novel approach to using GT data to capture societal trends that might impact survey participation, to our knowledge, no prior research has explored the use of search engine data to learn about what information potential respondents search about a survey. This gap in research is unfortunate since we recognize several areas of application and use cases in which these data might provide valuable insights.…”