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| Pilot-testad datainsamling med sensorer× | API-baserad datainsamling× | |
|---|---|---|
| Ämnesområde | Surveymetodik | Surveymetodik |
| Familj | Process / pipeline | Process / pipeline |
| Ursprungsår≠ | 1990s–2000s (formalized with proliferation of digital sensing technologies) | 2000s–2010s (formalized as a research method) |
| Upphovsperson≠ | General research methods practice; sensor pilot testing codified through IoT and environmental monitoring literature | Emerged from computational social science and web 2.0 platform practices |
| Typ≠ | Data collection procedure with pre-deployment validation phase | Digital data collection technique |
| Ursprungskälla≠ | Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Sage Publications. ISBN: 978-1506386706 | Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 9780691158648 |
| Alias | sensor pilot study, sensor pre-deployment testing, instrument validation with sensors, sensor calibration pilot | API data harvesting, API-driven data collection, programmatic data retrieval, API research data collection |
| Närliggande≠ | 6 | 5 |
| Sammanfattning≠ | Pilot-tested sensor data collection is a structured data gathering approach in which sensor instruments — hardware or software-based devices that measure physical, environmental, physiological, or behavioral signals — are deployed in a small-scale trial before the main study. The pilot phase verifies sensor accuracy, communication reliability, data format consistency, and placement adequacy, allowing researchers to identify and correct technical problems before full-scale data collection begins. | 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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