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| 纵向传感器数据收集× | 移动体验抽样法× | |
|---|---|---|
| 领域 | 调查方法论 | 调查方法论 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1990s–2000s (accelerated with IoT and wearable devices from ~2010) | 1983–1987 |
| 提出者≠ | Emerging from ambulatory assessment and wearable technology research communities | Mihaly Csikszentmihalyi & Reed Larson |
| 类型≠ | Longitudinal quantitative/mixed data collection technique | Intensive longitudinal data collection technique |
| 开创性文献≠ | 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 ↗ |
| 别名 | long-term sensor monitoring, longitudinal sensing, continuous sensor logging, repeated-measures sensor collection | ESM, ecological momentary assessment, EMA, daily diary via mobile |
| 相关≠ | 3 | 4 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
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