Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Ukusanyaji wa Data kwa Kutumia API za Simu za Mkononi× | Ukusanyaji Data kwa kutumia API× | |
|---|---|---|
| Nyanja | Metodolojia ya Dodoso | Metodolojia ya Dodoso |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 2007–2010 (mainstream smartphone era) | 2000s–2010s (formalized as a research method) |
| Mwanzilishi≠ | Emerged from mobile computing and REST/web API proliferation (Fielding, 2000; widespread adoption ~2007–2010 with smartphone ecosystem) | Emerged from computational social science and web 2.0 platform practices |
| Aina | Digital data collection technique | Digital data collection technique |
| Chanzo asilia≠ | 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 ↗ | Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 9780691158648 |
| Majina mbadala | mobile API data collection, smartphone API data harvesting, mobile app API research data collection, API-driven mobile data collection | API data harvesting, API-driven data collection, programmatic data retrieval, API research data collection |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | 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. | 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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