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Sběr longitudinálních senzorových dat×Metoda vzorkování zážitků v mobilním prostředí×
OborMetodologie dotazníkových šetřeníMetodologie dotazníkových šetření
RodinaProcess / pipelineProcess / pipeline
Rok vzniku1990s–2000s (accelerated with IoT and wearable devices from ~2010)1983–1987
TvůrceEmerging from ambulatory assessment and wearable technology research communitiesMihaly Csikszentmihalyi & Reed Larson
TypLongitudinal quantitative/mixed data collection techniqueIntensive longitudinal data collection technique
Původní zdrojLanza, 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 ↗Csikszentmihalyi, M., & Larson, R. (1987). Validity and reliability of the Experience-Sampling Method. Journal of Nervous and Mental Disease, 175(9), 526–536. DOI ↗
Další názvylong-term sensor monitoring, longitudinal sensing, continuous sensor logging, repeated-measures sensor collectionESM, ecological momentary assessment, EMA, daily diary via mobile
Příbuzné34
ShrnutíLongitudinal 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.The Mobile Experience Sampling Method (ESM) collects repeated, time-stamped self-reports from participants in their natural environment using a smartphone app. By signaling participants multiple times per day over days or weeks, researchers capture psychological states, behaviors, and contexts as they occur — eliminating retrospective bias and revealing within-person dynamics that single-session surveys cannot detect.
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ScholarGatePorovnat metody: Longitudinal Sensor Data Collection · Mobile Experience Sampling Method. Získáno 2026-06-15 z https://scholargate.app/cs/compare