Bandingkan metode
Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.
| Pengumpulan Data Sensor Seluler× | Pengumpulan Data Berbasis API× | |
|---|---|---|
| Bidang | Metodologi Survei | Metodologi Survei |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | Mid-2000s (smartphone-era formalization ~2006–2010) | 2000s–2010s (formalized as a research method) |
| Pencetus≠ | Andrew Campbell, Tanzeem Choudhury, and colleagues (early smartphone sensing research); broader field of ubiquitous computing | Emerged from computational social science and web 2.0 platform practices |
| Tipe≠ | Passive and active quantitative data collection technique | Digital data collection technique |
| Sumber perintis≠ | 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 ↗ | Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 9780691158648 |
| Alias | mobile sensing, smartphone sensor data collection, wearable sensor data collection, passive mobile data collection | API data harvesting, API-driven data collection, programmatic data retrieval, API research data collection |
| Terkait≠ | 4 | 5 |
| Ringkasan≠ | 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. | 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. |
| ScholarGateSet data ↗ |
|
|