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Pengumpulan Data Sensor Bersemuka×Pensampelan Pengalaman Mudah Alih×
BidangMetodologi TinjauanMetodologi Tinjauan
KeluargaProcess / pipelineProcess / pipeline
Tahun asal1990s–2000s (growth with wearable/biosensor technology)1983
PengasasEmerging from ambulatory assessment and wearable computing research communitiesMihaly Csikszentmihalyi & Reed Larson
JenisQuantitative / mixed-methods data collection techniqueIntensive longitudinal data collection technique
Sumber perintisTrull, T. J., & Ebner-Priemer, U. (2013). Ambulatory assessment. Annual Review of Clinical Psychology, 9, 151–176. DOI ↗Csikszentmihalyi, M., & Larson, R. (1987). Validity and reliability of the Experience-Sampling Method. Journal of Nervous and Mental Disease, 175(9), 526–536. DOI ↗
Aliasin-person sensor data collection, proximate biosensor data collection, face-to-face ambulatory assessment, on-site sensor recordingESM, Experience Sampling Method, Ecological Momentary Assessment, EMA
Berkaitan45
RingkasanFace-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.Mobile Experience Sampling (ESM) is an intensive longitudinal data-collection technique in which participants respond to brief, repeated questionnaires delivered to their smartphones at random or scheduled intervals throughout the day. By capturing thoughts, feelings, behaviors, and context at or near the moment they occur, ESM minimises retrospective recall bias and provides a high-resolution picture of psychological and behavioral fluctuations in everyday life.
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ScholarGateBandingkan kaedah: Face-to-face Sensor Data Collection · Mobile Experience Sampling. Dicapai 2026-06-17 daripada https://scholargate.app/ms/compare