Internal and external validity in data donation studies |
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| Coordinator 1 | Mr Ádám Stefkovics (HUN-REN Centre for Social Sciences) |
| Coordinator 2 | Ms Laura Boeschoten (Utrecht University) |
| Coordinator 3 | Mr Johannes Breuer (GESIS) |
| Coordinator 4 | Mr Zoltán Kmetty (HUN-REN Centre for Social Sciences) |
| Coordinator 5 | Mrs Júlia Koltai (HUN-REN Centre for Social Sciences) |
| Coordinator 6 | Ms Bella Struminskaya (Utrecht University) |
Over the last years, the use of digital trace data has grown rapidly across fields and topics in the social sciences. Given the challenges and risks associated with API-based data collection, one of the most promising recent methods for accessing such data is through data donations. Data Download Packages (DDPs) offer an effective approach for collecting detailed and potentially also multimodal individual-level information on the use of social media and other digital services and devices (e.g., messenger apps or fitness trackers). Linking the data from DDPs with survey data is especially valuable for enhancing the depth and accuracy of social-scientific research.
However, according to prior research, one caveat of data donations is that preserving internal and external validity can be challenging in these projects. Systematic bias originating from the data processing and measurement of online behaviour can harm data quality and issues with sampling, coverage and nonresponse may undermine the generalizability of data donation studies. With this session, we want to address these important methodological issues related to data donations. We invite contributions for the session which provide new theoretical or empirical insights and practical solutions to systematic biases that may harm internal and external validity in data donation studies. Contributions may cover the following topics but are not limited to:
· Factors influencing willingness to donate social media data
· Coverage bias and issues with sampling frames in data donation studies
· Sampling bias and random selection in data donation studies
· Nonresponse, compliance, and consent bias in data donation studies
· Data processing and measurement error in data donation studies, preserving data quality