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| 스마트폰을 이용한 모바일 경험 표집법× | 센서 데이터 수집× | |
|---|---|---|
| 분야 | 조사방법론 | 조사방법론 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1983–1987 | 1990s–2000s (widespread deployment with IoT ~2000s) |
| 창시자≠ | Mihaly Csikszentmihalyi & Reed Larson | Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward |
| 유형≠ | Intensive longitudinal data collection technique | Quantitative / mixed data collection technique |
| 원전≠ | Csikszentmihalyi, M., & Larson, R. (1987). Validity and reliability of the Experience-Sampling Method. Journal of Nervous and Mental Disease, 175(9), 526–536. DOI ↗ | Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256. DOI ↗ |
| 별칭 | ESM, ecological momentary assessment, EMA, daily diary via mobile | sensor measurement, instrumented data collection, physical sensor logging, IoT data collection |
| 관련≠ | 4 | 5 |
| 요약≠ | The Mobile Experience Sampling Method (ESM) collects repeated, time-stamped self-reports from participants in their natural environment using a smartphone app. By signaling participants multiple times per day over days or weeks, researchers capture psychological states, behaviors, and contexts as they occur — eliminating retrospective bias and revealing within-person dynamics that single-session surveys cannot detect. | 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. |
| ScholarGate데이터셋 ↗ |
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