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| 종단적 센서 데이터 수집× | 스마트폰을 이용한 모바일 경험 표집법× | |
|---|---|---|
| 분야 | 조사방법론 | 조사방법론 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1990s–2000s (accelerated with IoT and wearable devices from ~2010) | 1983–1987 |
| 창시자≠ | Emerging from ambulatory assessment and wearable technology research communities | Mihaly Csikszentmihalyi & Reed Larson |
| 유형≠ | Longitudinal quantitative/mixed data collection technique | Intensive longitudinal data collection technique |
| 원전≠ | Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L. (2005). PROC LCA: A SAS procedure for latent class analysis. Structural Equation Modeling, 14(4), 671–694. [For longitudinal intensive repeated-measures designs context, see also: Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32.] link ↗ | 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 | long-term sensor monitoring, longitudinal sensing, continuous sensor logging, repeated-measures sensor collection | ESM, ecological momentary assessment, EMA, daily diary via mobile |
| 관련≠ | 3 | 4 |
| 요약≠ | Longitudinal sensor data collection deploys physical or digital sensors to record phenomena continuously or at regular intervals across an extended study period — days, months, or years. Unlike one-shot measurement, the repeated temporal structure captures change, trajectory, and variability in outcomes such as physical activity, environmental exposure, sleep, or physiological state. The approach combines the ecological validity of real-world sensing with the analytical power of longitudinal design. | 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. |
| ScholarGate데이터셋 ↗ |
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