विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| पायलट-परीक्षित एपीआई-आधारित डेटा संग्रह× | मोबाइल एपीआई-आधारित डेटा संग्रह× | |
|---|---|---|
| क्षेत्र | सर्वेक्षण पद्धति | सर्वेक्षण पद्धति |
| परिवार | Process / pipeline | Process / pipeline |
| उद्भव वर्ष≠ | 2000s–2010s | 2007–2010 (mainstream smartphone era) |
| प्रवर्तक≠ | Convergence of survey pilot-testing tradition (Presser et al., 2004) and computational social science API methods (Salganik, 2018) | Emerged from mobile computing and REST/web API proliferation (Fielding, 2000; widespread adoption ~2007–2010 with smartphone ecosystem) |
| प्रकार≠ | Applied data-collection variant | Digital data collection technique |
| मौलिक स्रोत≠ | Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 978-0691158648 | 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 ↗ |
| उपनाम | pilot API data collection, pre-tested API harvesting, API data collection pilot study, pilot-validated API scraping | mobile API data collection, smartphone API data harvesting, mobile app API research data collection, API-driven mobile data collection |
| संबंधित≠ | 4 | 6 |
| सारांश≠ | Pilot-tested API-based data collection is a structured digital data-gathering approach in which a researcher designs an API query or harvesting script and then runs a small-scale trial before executing the full collection. The pilot phase exposes authentication issues, rate-limit constraints, schema inconsistencies, and coverage gaps, enabling targeted refinements that protect the integrity and completeness of the final dataset. It bridges the software-engineering practice of integration testing with the social-science tradition of instrument pre-testing. | 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. |
| ScholarGateडेटासेट ↗ |
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