Módszerek összehasonlítása

	API-alapú adatgyűjtés	Adatszedés szenzorokkal – Szenzorokon alapuló adatszedés
Tudományterület	Kérdőíves felmérések módszertana	Kérdőíves felmérések módszertana
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	2000s–2010s (formalized as a research method)	1990s–2000s (widespread deployment with IoT ~2000s)
Megalkotó	Emerged from computational social science and web 2.0 platform practices	Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward
Típus	Digital data collection technique	Quantitative / mixed data collection technique
Alapmű	Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 9780691158648	Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256.
Alternatív nevek	API data harvesting, API-driven data collection, programmatic data retrieval, API research data collection	sensor measurement, instrumented data collection, physical sensor logging, IoT data collection
Kapcsolódó	5	5
Összefoglaló	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.	Sensor data collection uses physical or digital instruments to automatically capture quantitative measurements from the environment, human bodies, or machines over time. Common sensors measure temperature, motion, heart rate, location, light, sound, or chemical properties. Because the recording is automated and continuous, the method can produce high-frequency datasets with minimal researcher burden, making it central to IoT, environmental monitoring, wearable research, and behavioral studies.