เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| การสำรวจผ่านมือถือ× | การเก็บรวบรวมข้อมูลด้วย API× | |
|---|---|---|
| สาขาวิชา | ระเบียบวิธีสำรวจ | ระเบียบวิธีสำรวจ |
| ตระกูล | Process / pipeline | Process / pipeline |
| ปีกำเนิด≠ | Late 2000s–2010s (accelerated with smartphone adoption, ~2007–2015) | 2000s–2010s (formalized as a research method) |
| ผู้ริเริ่ม≠ | Emerged from web survey methodology researchers (Couper, Buskirk, Toepoel, and others) | Emerged from computational social science and web 2.0 platform practices |
| ประเภท≠ | Quantitative / mixed data collection technique | Digital data collection technique |
| แหล่งต้นตำรับ≠ | Toepoel, V., & Lugtig, P. (2014). What happens if you offer a mobile option to your web panel? Evidence from a probability-based panel of internet users. Social Science Computer Review, 32(4), 544–560. DOI ↗ | Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 9780691158648 |
| ชื่อเรียกอื่น | smartphone survey, mobile web survey, mobile questionnaire, m-survey | API data harvesting, API-driven data collection, programmatic data retrieval, API research data collection |
| ที่เกี่ยวข้อง≠ | 6 | 5 |
| สรุป≠ | A mobile survey is a self-report questionnaire designed and administered through smartphones or tablets, either via a mobile-optimized web browser or a dedicated app. As mobile devices became the dominant mode of internet access globally, surveys must be built for small screens, touch interaction, and variable connectivity. Mobile surveys are used across social science, public health, market research, and organizational studies when reaching respondents in their natural, everyday context is a priority. | 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. |
| ScholarGateชุดข้อมูล ↗ |
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