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Survey Guidelines

Data Linking - Linking survey data with geospatial, social media, and sensor data

While survey data are still the most commonly used type of data in the quantitative social sciences, not everything that is of interest to social scientists can be measured via surveys, and the self-report data they provide have certain limitations, such as recollection or social desirability bias. Hence, social scientists have increasingly used other types of data that are not specifically created for research. These data are often called “found data” or “non-designed data” and encompass a variety of different data types, such as geospatial data, digital trace data, or other types of transactional data. Naturally, these data have their own sets of limitations. One way of combining the unique strengths of survey data and these other data types and dealing with some of their respective limitations is to link them. This guideline describes the benefits of linking survey data with other types of data as well as the challenges in the process, and how to deal with them, focusing on three types of data that are becoming increasingly popular in the social sciences: geospatial data, social media data, and sensor data.

Beuthner, Christoph, Johannes Breuer, and Jünger, Stefan (2021). Data Linking - Linking survey data with geospatial, social media, and sensor data. Mannheim, GESIS - Leibniz Institute for the Social Sciences (GESIS Survey Guidelines). DOI: 10.15465/gesis-sg_en_039

Bensmann, Felix, Lars Heling, Stefan Jünger, Loren Mucha, Maribel Acosta, Jan Goebel, Gotthard Meinel, Sujit Sikder, York Sure-Vetter, und Benjamin Zapilko. 2020. „An Infrastructure for Spatial Linking of Survey Data“. Data Science Journal 19 (1): 27. https://doi.org/10.5334/dsj-2020-027.

Breuer, Johannes, Tarek A. Baghal, Luke Sloan, Libby Bishop, Dimitra Kondyli, and Apostolos Linardis. 2021 (Forthcoming). "Informed consent for linking survey and social media data: Differences between platforms and data types." IASSIST Quarterly.

Breuer, Johannes, Libby Bishop, and Katharina E. Kinder-Kurlanda. 2020. "The practical and ethical challenges in acquiring and sharing digital trace data: negotiating public-private partnerships." New Media & Society 22 (11): 2058-2080. doi: http://dx.doi.org/10.1177/1461444820924622

Jünger, Stefan. 2019. Using Georeferenced Data in Social Science Survey Research. The Method of Spatial Linking and Its Application with the German General Social Survey and the GESIS Panel. GESIS-Schriftenreihe 24. Köln: GESIS - Leibniz-Institut für Sozialwissenschaften. https://doi.org/10.21241/ssoar.63688.

Jünger, Stefan, Kerrin Borschewski, und Wolfgang Zenk-Möltgen. 2019. „Documenting Georeferenced Social Science Survey Data:  Limits of Metadata Standards and Possible Solutions“. Journal of Map & Geography Libraries 15 (1): 68–95. https://doi.org/10.1080/15420353.2019.1659903

Müller, Stefan. 2019. „Räumliche Verknüpfung georeferenzierter Umfragedaten mit Geodaten: Chancen, Herausforderungen und praktische Empfehlungen“. In Forschungsdatenmanagement sozialwissenschaftlicher Umfragedaten. Grundlagen und praktische Lösungen für den Umgang mit quantitativen Forschungsdaten, herausgegeben von Uwe Jensen, Sebastian Netscher, und Katrin Weller, 211–29. Opladen, Berlin, Toronto: Verlag Barbara Budrich. https://doi.org/10.2307/j.ctvbkk1p8.15.

Stier, Sebastian, Johannes Breuer, Pascal Siegers, and Kjerstin Thorson. 2020. "Integrating survey data and digital trace data: Key issues in developing an emerging field." Social Science Computer Review 38 (5): 503-516. doi: http://dx.doi.org/10.1177/0894439319843669

Silber, Henning; Breuer, Johannes: Beuthner, Christoph; Gummer, Tobias; Keusch, Florian; Siegers, Pascal; Stier, Sebastian; Weiß, Bernd. 2021. “Linking surveys and digital trace data: Insights from two studies on determinants of data sharing behavior.” SocArXiv. https://doi.org/10.31235/osf.io/dz93u