Linkage Analysis Revised – Linking Digital Traces and Survey Data

ABSTRACT

Linkage analysis, i.e. linking media exposure, content, and surveys, has been a powerful tool to assess media effects. However, the development of online communication and the advent of social media brings about many challenges for traditional linkage designs. In this paper, we explain the three steps of linkage designs for online communication effects and the usage of computational approaches to capture communication exposure and content. We then review recent designs and studies that use different forms of digital trace data to link digital communication exposure, content, and surveys: Tracking data, data donations, and screenshots/screen recordings. We describe (practical) challenges and opportunities when linking digital communication traces with self-reports and show how these data could be analyzed to establish different media effects.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ For linkage designs, media effects are primarily defined as (changes in) a particular outcome variable (e.g., attitudes, behavioral intentions, emotions, knowledge that correlate with media content a participant was exposed to (Schuck et al., 2016). When using longitudinal designs to measure media content and the outcome variable, researchers tend to state that exposure to the media content caused the (changes in) the

² https://blog.esper.io/android-13-sideloading-restriction-harder-malware-abuse-accessibility-apis/

³ Data donation approaches are able to capture mobile digital traces, so in principle this notion only holds for tracking. However, data donations are also limited to information from a few platforms, apps, and services that a user logs into and can, in turn, download their data. For a significant amount of activities on mobile devices, data donation approaches are not available.

⁴ It would be possible to measure the outcome at the same resolution, e.g., when applying physiological measures or real-time-response designs (Maier et al., 2016)

Additional information

Notes on contributors

Lukas P. Otto

Lukas P. Otto (PhD, U of Koblenz-Landau) is a senior researcher at GESIS – Leibniz Institute for the Social Sciences, Computational Social Science Department. His research interests include dynamics of communication effects, political communication and emotion, and mobile approaches to communication research

Felicia Loecherbach

Felicia Loecherbach (Ph.D., Vrije Universiteit Amsterdam) is a postdoctoral researcher at the Center for Social Media and Politics, New York University. Her research focuses on news diversity and how it is impacted by selection and personalization as well as the collection and modeling of digital trace data

Rens Vliegenthart

Rens Vliegenthart is a professor in Strategic Communication, Wageningen University and Research. His research focuses on (social) media content and effects on citizens as well as on political decision making processes