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| 종단적 센서 데이터 수집× | 종단 조사× | |
|---|---|---|
| 분야 | 조사방법론 | 조사방법론 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1990s–2000s (accelerated with IoT and wearable devices from ~2010) | 1940s (panel survey tradition); longitudinal designs codified mid-20th century |
| 창시자≠ | Emerging from ambulatory assessment and wearable technology research communities | Established tradition; formalized in social science by Paul Lazarsfeld and colleagues (1940s panel studies) |
| 유형≠ | Longitudinal quantitative/mixed data collection technique | Quantitative / mixed-methods survey design |
| 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 ↗ | Menard, S. (2002). Longitudinal Research (2nd ed.). Sage Publications. ISBN: 978-0761922292 |
| 별칭 | long-term sensor monitoring, longitudinal sensing, continuous sensor logging, repeated-measures sensor collection | panel survey, repeated-measures survey, longitudinal panel study, wave survey |
| 관련 | 3 | 3 |
| 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. | A longitudinal survey collects structured questionnaire data from the same individuals or units at two or more distinct points in time. By tracking the same respondents across waves, researchers can distinguish genuine change from stable individual differences, establish temporal ordering between variables, and model trajectories of attitudes, behaviors, or outcomes in ways that a single cross-sectional snapshot cannot support. |
| ScholarGate데이터셋 ↗ |
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