방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 온라인 센서 데이터 수집× | 종단적 센서 데이터 수집× | |
|---|---|---|
| 분야 | 조사방법론 | 조사방법론 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | Late 1990s–early 2000s (Internet of Things paradigm formalized ~2000) | 1990s–2000s (accelerated with IoT and wearable devices from ~2010) |
| 창시자≠ | Akyildiz et al. (foundational survey); DARPA SensIT programme (~2000) | Emerging from ambulatory assessment and wearable technology research communities |
| 유형≠ | Quantitative / mixed-mode data collection technique | Longitudinal quantitative/mixed 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 ↗ | 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 ↗ |
| 별칭 | networked sensor data collection, IoT data collection, remote sensor monitoring, wireless sensor data acquisition | long-term sensor monitoring, longitudinal sensing, continuous sensor logging, repeated-measures sensor collection |
| 관련≠ | 6 | 3 |
| 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. | 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. |
| ScholarGate데이터셋 ↗ |
|
|