Workshopslot
2,5
Target groups
Researchers and practitioners with a general methodological interest in web-based surveys and digital trace data
Is the workshop geared at an exclusively German or an international audience?
International
Workshop language
English
Description of the content of the workshop
Web surveys frequently run short to accurately measure digital behavior because they are prone to recall error (i.e., biased recalling and reporting of past behavior) and social desirability bias (i.e., misreporting of behavior to comply with social norms and values). New advances in the collection of digital trace (or web tracking) data make it possible to directly measure digital behavior in the form of browser logs (e.g., visited URLs and search terms) and apps (e.g., duration and frequency of their use). Building on these advances, we will introduce participants to web surveys augmented with web tracking data. In this course, we initially give a thorough overview of the manifold new measurement opportunities provided by web tracking. In addition, participants obtain comprehensive insights into the collection, processing, analysis, and error sources of web tracking data as well as its application to substantive research. Importantly, the course includes applied web tracking data exercises in which participants learn how to ...
- ... operationalize and collect web tracking data.
- ... work with and process web tracking data.
- ... analyze and extract information from web tracking data.
Goals of the workshop
At the end of the workshop, participants will be able to 1) independently conceptualize the collection of web tracking data, 2) decide on best practices when it comes to data handling and analysis, and 3) critically reflect upon the opportunities and challenges of web tracking data and its suitability for empirical studies in the context of social and behavioral science.
Necessary prior knowledge of participants
Basic knowledge on web-based surveys, including structured and unstructured datasets, is beneficial but not a pre-requisite.
Literature that participants need to read prior to participation
none
Recommended additional literature
Bosch, O. J., & Revilla, M. (2022). When survey science met web tracking: Presenting an error framework for metered data. Journal of the Royal Statistical Society (Series A), 185, 408-436. https://doi.org/10.1111/rssa.12956
Bach, R. L., & Wenz, A. (2020). Studying health-related internet and mobile device use using web logs and smartphone records. PLoS ONE, 15(6), Article e0234663. https://doi.org/10.1371/journal.pone.0234663
Cernat, A., Keusch, F., Bach, R. L., & Pankowska (2024). Estimating measurement quality in digital trace data and surveys using the MultiTrait MultiMethod Model. Social Science Computer Review. https://doi.org/10.1177/08944393241254464
Revilla, M. (2022). How to enhance web survey data using metered, geolocation, visual and voice data? Survey Research Methods, 16(1). https://doi.org/10.18148/srm/2022.v16i1.8013
Information about the instructors
Jan Karem Höhne (hoehne@dzhw.eu) is junior professor at Leibniz University Hannover in association with the German Center for Higher Education Research and Science Studies (DZHW). He is head of the CS3 Lab for Computational Survey and Social Science. His research focuses on new data forms and types for measuring political and social attitudes.
Joshua Claassen (claassen@dzhw.eu) is PhD student and research associate at Leibniz University Hannover in association with the German Center for Higher Education Research and Science Studies (DZHW). His research focuses on computational survey and social science with an emphasis on digital trace data.
Maximum number of participants
25
Will participants need to bring their own devices in order to be able to access the Internet? Will they need to bring anything else to the workshop?
Yes, they should bring their devices (laptop and smartphone).
