Methoden vergleichen
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| Mobile Survey× | API-basierte Datenerhebung× | |
|---|---|---|
| Fachgebiet | Umfragemethodik | Umfragemethodik |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | Late 2000s–2010s (accelerated with smartphone adoption, ~2007–2015) | 2000s–2010s (formalized as a research method) |
| Urheber≠ | Emerged from web survey methodology researchers (Couper, Buskirk, Toepoel, and others) | Emerged from computational social science and web 2.0 platform practices |
| Typ≠ | Quantitative / mixed data collection technique | Digital data collection technique |
| Wegweisende Quelle≠ | Toepoel, V., & Lugtig, P. (2014). What happens if you offer a mobile option to your web panel? Evidence from a probability-based panel of internet users. Social Science Computer Review, 32(4), 544–560. DOI ↗ | Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 9780691158648 |
| Aliasnamen | smartphone survey, mobile web survey, mobile questionnaire, m-survey | API data harvesting, API-driven data collection, programmatic data retrieval, API research data collection |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | A mobile survey is a self-report questionnaire designed and administered through smartphones or tablets, either via a mobile-optimized web browser or a dedicated app. As mobile devices became the dominant mode of internet access globally, surveys must be built for small screens, touch interaction, and variable connectivity. Mobile surveys are used across social science, public health, market research, and organizational studies when reaching respondents in their natural, everyday context is a priority. | API-based data collection is a systematic technique in which a researcher sends structured requests to an application programming interface to retrieve data automatically from digital platforms, databases, or services. It is the primary method used in computational social science to gather large-scale social media records, government open data, financial data streams, and scientific repository content in machine-readable formats such as JSON or XML, enabling reproducible and scalable data acquisition that manual collection cannot match. |
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