Methoden vergleichen
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| Pilot-getestete Sensordatenerfassung× | Mobile Sensor Data Collection× | |
|---|---|---|
| Fachgebiet | Umfragemethodik | Umfragemethodik |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1990s–2000s (formalized with proliferation of digital sensing technologies) | Mid-2000s (smartphone-era formalization ~2006–2010) |
| Urheber≠ | General research methods practice; sensor pilot testing codified through IoT and environmental monitoring literature | Andrew Campbell, Tanzeem Choudhury, and colleagues (early smartphone sensing research); broader field of ubiquitous computing |
| Typ≠ | Data collection procedure with pre-deployment validation phase | Passive and active quantitative data collection technique |
| Wegweisende Quelle≠ | Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Sage Publications. ISBN: 978-1506386706 | Lane, N. D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. T. (2010). A survey of mobile phone sensing. IEEE Communications Magazine, 48(9), 140–150. DOI ↗ |
| Aliasnamen | sensor pilot study, sensor pre-deployment testing, instrument validation with sensors, sensor calibration pilot | mobile sensing, smartphone sensor data collection, wearable sensor data collection, passive mobile data collection |
| Verwandt≠ | 6 | 4 |
| Zusammenfassung≠ | 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. | Mobile sensor data collection uses the built-in sensors of smartphones, tablets, or wearable devices to capture behavioral, physiological, and environmental data in real-world settings. Sensors such as accelerometers, GPS, heart rate monitors, ambient light detectors, and microphones record data passively or on demand, enabling researchers to study human behavior with high temporal resolution outside the laboratory. |
| ScholarGateDatensatz ↗ |
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