Combining Survey Data and Digital Behavioral Data

GESIS ›Meet the Experts‹

The use of digital behavioral data (DBD) is one of the key features of computational social science. These data have several advantages compared to other types of data, such as survey data. For example, DBD are generally less costly to collect and less prone to being influenced by social desirability than survey data, and they allow for capturing behavior immediately when it occurs instead of ex-post, thus, reducing problems related to recall. At the same time, however, DBD also have specific limitations. These include a lack of information about the individuals (e.g., regarding their demographics, personality traits, or attitudes) or the fact that they provide only limited information about offline activities. Yet the measurement of individual attributes and attitudes as well as self-reported behaviors across domains is what surveys are well-suited for. Combining survey data and DBD therefore leverages the unique strengths of these two data types, while also addressing some of their respective limitations.

This talk discusses the benefits of combining survey data with DBD and the challenges associated with this approach. We will present different ways of linking surveys and DBD and address key challenges regarding linking procedures, informed consent, and data privacy.


Dr. Johannes Breuer is as a senior researcher in the team Data Linking & Data Security at GESIS where his work focuses on data linking and the use of digital trace data. He holds a Ph.D. in psychology and his research interests include the use and effects of digital media, computational methods, data management, and open science.

Dr. Sebastian Stier is a senior researcher in the Computational Social Science department at GESIS. He holds a PhD in political science and conducts research in the fields of political communication, comparative politics, and computational social science.



GESIS - Leibniz-Institut für Sozialwissenschaften


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