方法对比
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| 纵向传感器数据收集× | 传感器数据收集× | |
|---|---|---|
| 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) | 1990s–2000s (widespread deployment with IoT ~2000s) |
| 提出者≠ | Emerging from ambulatory assessment and wearable technology research communities | Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward |
| 类型≠ | Longitudinal quantitative/mixed data collection technique | Quantitative / mixed 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 ↗ | Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256. DOI ↗ |
| 别名 | long-term sensor monitoring, longitudinal sensing, continuous sensor logging, repeated-measures sensor collection | sensor measurement, instrumented data collection, physical sensor logging, IoT data collection |
| 相关≠ | 3 | 5 |
| 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. | 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. |
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