방법 비교

	종단적 웹 스크래핑	센서 데이터 수집
분야	조사방법론	조사방법론
계열	Process / pipeline	Process / pipeline
기원 연도	2000s–2010s	1990s–2000s (widespread deployment with IoT ~2000s)
창시자	Emergent practice in computational social science; formalized across internet research community	Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward
유형	Automated longitudinal data collection	Quantitative / mixed data collection technique
원전	Salganik, M. J. (2018). Bit by Bit: Social Research in the Digital Age. Princeton University Press. ISBN: 978-0691158648	Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256.
별칭	repeated web scraping, time-series web data collection, longitudinal crawling, panel web scraping	sensor measurement, instrumented data collection, physical sensor logging, IoT data collection
관련	5	5
요약	Longitudinal web scraping is a data collection technique that uses automated scripts to extract content from websites at multiple, predefined time points. By revisiting the same web sources repeatedly, researchers build a time-series dataset that captures how online content, prices, discourse, or behavior evolves. It is widely used in computational social science, economics, political science, health research, and digital humanities to study change without relying on retrospective self-report.	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.