Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Сбор данных через мобильные API× | Сбор данных с датчиков× | |
|---|---|---|
| Область | Методология опросов | Методология опросов |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 2007–2010 (mainstream smartphone era) | 1990s–2000s (widespread deployment with IoT ~2000s) |
| Автор метода≠ | Emerged from mobile computing and REST/web API proliferation (Fielding, 2000; widespread adoption ~2007–2010 with smartphone ecosystem) | Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward |
| Тип≠ | Digital data collection technique | Quantitative / mixed data collection technique |
| Основополагающий источник≠ | Luce, M. F., Kahn, B. E., & Malhotra, N. K. (2016). Capturing consumer experiences with mobile research methods. Journal of Consumer Research, 42(6), 949–965. link ↗ | Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256. DOI ↗ |
| Другие названия | mobile API data collection, smartphone API data harvesting, mobile app API research data collection, API-driven mobile data collection | sensor measurement, instrumented data collection, physical sensor logging, IoT data collection |
| Связанные≠ | 6 | 5 |
| Сводка≠ | Mobile API-based data collection uses mobile devices (smartphones, tablets) to query application programming interfaces — structured web endpoints that return machine-readable data — enabling researchers to gather behavioral, contextual, sensor-enriched, or platform-generated data in real time from participants in their natural environments. It combines the ubiquity of mobile hardware with the scalability and standardization of RESTful or GraphQL APIs. | Sensor data collection uses physical or digital instruments to automatically capture quantitative measurements from the environment, human bodies, or machines over time. Common sensors measure temperature, motion, heart rate, location, light, sound, or chemical properties. Because the recording is automated and continuous, the method can produce high-frequency datasets with minimal researcher burden, making it central to IoT, environmental monitoring, wearable research, and behavioral studies. |
| ScholarGateНабор данных ↗ |
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