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| 온라인 센서 데이터 수집× | 모바일 경험 표본 추출× | |
|---|---|---|
| 분야 | 조사방법론 | 조사방법론 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | Late 1990s–early 2000s (Internet of Things paradigm formalized ~2000) | 1983 |
| 창시자≠ | Akyildiz et al. (foundational survey); DARPA SensIT programme (~2000) | Mihaly Csikszentmihalyi & Reed Larson |
| 유형≠ | Quantitative / mixed-mode data collection technique | Intensive longitudinal data collection technique |
| 원전≠ | Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4), 393–422. 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 | networked sensor data collection, IoT data collection, remote sensor monitoring, wireless sensor data acquisition | ESM, Experience Sampling Method, Ecological Momentary Assessment, EMA |
| 관련≠ | 6 | 5 |
| 요약≠ | Online sensor data collection is a systematic technique for gathering continuous or event-triggered measurements from physical sensors that transmit readings in real time over a network — the internet, a local wireless network, or a dedicated IoT protocol. It is used widely in environmental monitoring, health informatics, smart-city research, industrial systems, and behavioral science to capture objective, high-frequency data without requiring manual recording by participants or observers. | 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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