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.

Data Linking

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