The Power and Pitfalls of Combining Survey and Sensor Data 1 |
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| Session Organisers |
Miss Anne Elevelt (Utrecht University) Dr Peter Lugtig (Utrecht University) Dr Vera Toepoel (Utrecht University) |
| Time | Wednesday 17th July, 14:00 - 15:00 |
| Room | D19 |
Sensor data offer great potential for social scientists interested in studying attitudes and behaviors. These kind of data are particularly interesting when they can be linked to and compared with other data sources. With more and more possibilities to collect additional data through smartphones (for example through smartphone apps or activity trackers) large-scale population surveys could rather easily be enriched. Participants carry their smartphone everywhere, enabling scientists to ask respondents to make pictures, or to collect GPS and accelerometer data and track for example how much participants move around and where they go. Opportunities abound.
However, there are still many unsolved and unique methodological questions and issues to collecting and using sensor data. This session invites presentations that investigate the potentials and challenges when combining survey and sensor data. We especially welcome papers that used and collected these kind of data, and address;
The power of sensor data
o Higher data quality?
o Lower respondent burden.
The pitfalls of sensor data
o Implementation issues; nonresponse, willingness, device use.
o Technical problems.
o Issues in collecting and accessing these data across the general population.
o Data storage.
Keywords: Big data; sensor data
Professor Mark Trappmann (Institute for Employment Research (IAB), University of Bamberg) - Presenting Author
Dr Sebastian Bähr (Institute for Employment Research (IAB))
Mr Georg Haas (Institute for Employment Research (IAB))
Professor Florian Keusch (University of Mannheim)
Professor Frauke Kreuter (Institute for Employment Research (IAB), University of Maryland, University of Mannheim)
The integration of individual survey data with sensor data of the respondents offers a high potential for social science research. It enables researchers to combine the advantages of surveys originating from the controlled research design and representative sample with new and detailed forms of measurements that smartphone sensors offer.
Combining individual survey data with mobile app and sensor data, however, usually requires some form of informed consent. In longitudinal surveys with repeated measurement, panel members who disapprove of the extra burden or feel that a line has been crossed by requesting access to potentially highly sensitive data, might decide to withdraw their cooperation for future panel waves.
The German panel study PASS is designed to collect data for research into (un)employment, poverty and social security. PASS is an annual mixed-mode CATI and CAPI panel survey of the general population that completed its 12th wave of data collection in 2018.
In January 2018, we invited randomly selected panelists (n=4,293), who had reported in the previous wave that they owned a smartphone, to participate in the IAB-SMART-Study. The panelists were asked to install an app on their smartphones. They were asked to give access to sensor data, GPS position or app usage data for half a year and were regularly asked to answer short questionnaires during that time.
The random selection of invitees allows us to evaluate the unintended effect that the request for participation to a smartphone study collecting information that might be considered sensitive has on panel retention in the subsequent wave. We will report results on response rates as well as on composition of respondents with respect to target variables of the survey.
Mrs Danielle McCool (Utrecht University)
Dr Peter Lugtig (Utrecht University)
Professor Barry Schouten (Statistics Netherlands/Utrecht University) - Presenting Author
Apps are promising tools for travel surveys and have been explored by various commercial and non-commercial institutions. Statistics Netherlands recently developed a cross-platform travel app that actively measures time-location sensor data. Respondents get daily overviews of their travels and stops and are asked to supplement these with travel motives and transportation modes. Respondents can also indicate for each stop whether it was correctly detected or not. The current version of the app does not allow for adding or removing stops.
The definition of stops and travels depend on the motives of visiting the specific locations. A change of transportation mode, e.g. from train to bus, or a slow traffic light are not real stops, whereas dropping off someone at the train station is a stop. As a consequence, without stop motives, for two stops with similar features one may be real and the other may not be real. In the app, a candidate stop is detected when a respondent remains within a certain specified radius for at least a specified amount of time. The two parameters do not depend on the location or time.
In the paper, we show the results of a large-scale field test in which the two stop detection parameters have been varied and randomly assigned to different sample units. We compare the parameter values to the number of candidate stops and to the number of real stops.
Mr Stephan Schlosser (University of Göttingen) - Presenting Author
Dr Jan Karem Höhne (University of Mannheim)
Mr Daniel Qureshi (University of Frankfurt)
The use of mobile devices, such as smartphones, to participate in web surveys has increased tremendously in recent years. The reasons for this development are a skyrocketing proportion of smartphone owners accompanied by an increase in high-speed mobile Internet access. This development also enables respondents to participate in web surveys without any time and place restrictions. For instance, they can take part on their morning way to work or at home in the afternoon. There are almost no limitations regarding mobile web survey participation. One strategy to investigate respondents’ position and completion conditions is the collection of GPS (Global Positioning System) data. However, a drawback of GPS is that its reception in buildings or beneath the ground is reduced or even impossible. We therefore propose “SurveyMaps (SMaps),” a JavaScript-based tool that combines passive data, such as IP address, acceleration, and compass, to provide a supplement to GPS data. In this usability study, we initially investigate the proper functioning of SMaps in public spaces, such as parks and cities, by using GPS data. To illustrate the functionality of SMaps, we present the results of a study in which we ask respondents to complete a mobile web survey on their smartphone while being outdoor and randomly walk around (informed consent will be obtained). The results of the pretests look very promising and indicate that SMaps reliably gathers respondents outdoor position. A next step is to test the application of SMaps in buildings and beneath the ground.