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| 센서 데이터 수집× | 모바일 경험 표본 추출× | |
|---|---|---|
| 분야 | 조사방법론 | 조사방법론 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1990s–2000s (widespread deployment with IoT ~2000s) | 1983 |
| 창시자≠ | Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward | Mihaly Csikszentmihalyi & Reed Larson |
| 유형≠ | Quantitative / mixed data collection technique | Intensive longitudinal data collection technique |
| 원전≠ | Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256. DOI ↗ | Csikszentmihalyi, M., & Larson, R. (1987). Validity and reliability of the Experience-Sampling Method. Journal of Nervous and Mental Disease, 175(9), 526–536. DOI ↗ |
| 별칭 | sensor measurement, instrumented data collection, physical sensor logging, IoT data collection | ESM, Experience Sampling Method, Ecological Momentary Assessment, EMA |
| 관련 | 5 | 5 |
| 요약≠ | 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. | Mobile Experience Sampling (ESM) is an intensive longitudinal data-collection technique in which participants respond to brief, repeated questionnaires delivered to their smartphones at random or scheduled intervals throughout the day. By capturing thoughts, feelings, behaviors, and context at or near the moment they occur, ESM minimises retrospective recall bias and provides a high-resolution picture of psychological and behavioral fluctuations in everyday life. |
| ScholarGate데이터셋 ↗ |
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