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面对面传感器数据收集×面对面参与式观察×
领域调查方法论调查方法论
方法族Process / pipelineProcess / pipeline
起源年份1990s–2000s (growth with wearable/biosensor technology)Early 20th century (Chicago School ~1920s; Spradley formalisation 1980)
提出者Emerging from ambulatory assessment and wearable computing research communitiesChicago School sociologists (Robert Park, Ernest Burgess); systematised by Raymond Gold (1958) and James Spradley (1980)
类型Quantitative / mixed-methods data collection techniqueQualitative data collection technique
开创性文献Trull, T. J., & Ebner-Priemer, U. (2013). Ambulatory assessment. Annual Review of Clinical Psychology, 9, 151–176. DOI ↗Spradley, J. P. (1980). Participant Observation. Holt, Rinehart and Winston. ISBN: 978-0030445019
别名in-person sensor data collection, proximate biosensor data collection, face-to-face ambulatory assessment, on-site sensor recordingin-person participant observation, direct participant observation, fieldwork participant observation, co-present observation
相关45
摘要Face-to-face sensor data collection involves attaching or deploying sensors — physiological, motion, environmental, or proximity-based — on or around participants during in-person research sessions. The co-present setting allows direct researcher oversight of equipment, real-time signal monitoring, and immediate troubleshooting, yielding high-fidelity continuous or event-triggered data streams that capture objective behavioral and physiological indicators as they unfold.Face-to-face participant observation is a qualitative data collection technique in which the researcher physically enters a setting and engages with participants in real time to document social behaviour, interactions, and meaning-making as they naturally occur. Unlike online or remote variants, the researcher is bodily present, enabling direct sensory access to context, non-verbal cues, and the full texture of everyday life in the setting under study.
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ScholarGate方法对比: Face-to-face Sensor Data Collection · Face-to-face Participant Observation. 于 2026-06-19 检索自 https://scholargate.app/zh/compare