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| Pilot-getestete API-basierte Datenerfassung× | API-basierte Datenerhebung× | |
|---|---|---|
| Fachgebiet | Umfragemethodik | Umfragemethodik |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 2000s–2010s | 2000s–2010s (formalized as a research method) |
| Urheber≠ | Convergence of survey pilot-testing tradition (Presser et al., 2004) and computational social science API methods (Salganik, 2018) | Emerged from computational social science and web 2.0 platform practices |
| Typ≠ | Applied data-collection variant | Digital data collection technique |
| Wegweisende Quelle | Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 978-0691158648 | Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 9780691158648 |
| Aliasnamen | pilot API data collection, pre-tested API harvesting, API data collection pilot study, pilot-validated API scraping | API data harvesting, API-driven data collection, programmatic data retrieval, API research data collection |
| Verwandt≠ | 4 | 5 |
| Zusammenfassung≠ | Pilot-tested API-based data collection is a structured digital data-gathering approach in which a researcher designs an API query or harvesting script and then runs a small-scale trial before executing the full collection. The pilot phase exposes authentication issues, rate-limit constraints, schema inconsistencies, and coverage gaps, enabling targeted refinements that protect the integrity and completeness of the final dataset. It bridges the software-engineering practice of integration testing with the social-science tradition of instrument pre-testing. | 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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