Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Ukusanyaji wa Hati Mtandaoni× | Ukusanyaji Data kwa kutumia API× | |
|---|---|---|
| Nyanja | Metodolojia ya Dodoso | Metodolojia ya Dodoso |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 1990s–2000s (digital / web era) | 2000s–2010s (formalized as a research method) |
| Mwanzilishi≠ | Adapted from traditional document analysis; digital form emerged with widespread internet adoption | Emerged from computational social science and web 2.0 platform practices |
| Aina≠ | Qualitative / mixed-methods data collection technique | Digital data collection technique |
| Chanzo asilia≠ | Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27–40. DOI ↗ | Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 9780691158648 |
| Majina mbadala | digital document collection, web document gathering, online archival data collection, digital records collection | API data harvesting, API-driven data collection, programmatic data retrieval, API research data collection |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | Online document collection is the systematic process of identifying, retrieving, and compiling digital documents — including web pages, institutional publications, social media posts, policy documents, and digital archives — as primary or supplementary research data. It extends classical document analysis into internet-mediated environments, enabling researchers to access large, geographically dispersed corpora without fieldwork travel or physical archive access. | 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. |
| ScholarGateSeti ya data ↗ |
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