Linking survey data with digital trace data: error sources and best practices |
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| Coordinator 1 | Professor Mark Trappmann (Institute for Employment Research (IAB)) |
| Coordinator 2 | Dr Valerie Hase (Ludwig-Maximilians Universität München (LMU)) |
| Coordinator 3 | Professor Florian Keusch (Universität Mannheim) |
| Coordinator 4 | Professor Frauke Kreuter (Ludwig-Maximilians Universität München (LMU)) |
While survey data and digital trace data alone are each powerful and frequently used data sources in social science research, an even more compelling opportunity lies in the combination of the two . Digital trace data can be used to enrich existing survey data with nonreactive behavioral data of high velocity, precision and frequency. At the same time, the combination with survey data based on probability samples can increase the external validity and facilitate statistical inference of the digital trace data. Furthermore, self-reports add context to the digital trace data which may help us to better understand them and avoid misinterpretations.
In this session, we are looking for contributions that investigate any combination – both in the collection of these data sources and in their combined analysis – of survey data with digital trace data. These can, for example, result from the request for survey participants to install research apps on their smartphones or to donate their data from their social media profiles or other data sources . We are mainly interested in research that investigates error sources like coverage error, nonresponse error and measurement error in studies using such combined data. Furthermore, we are interested in studies that aim at improving measurement quality by combining these two sources. Studies that help identify best practices (e.g., how to optimize consent or donation rates, how to statistically address resulting errors) are also in the scope of the session.