Comparer des méthodes

	Échantillonnage expérientiel mobile multi-sources	Collecte de données par capteurs
Domaine	Méthodologie d'enquête	Méthodologie d'enquête
Famille	Process / pipeline	Process / pipeline
Année d'origine	2000s–2010s	1990s–2000s (widespread deployment with IoT ~2000s)
Auteur d'origine	Developed from ESM (Csikszentmihalyi & Larson, 1983) and extended to multi-informant intensive longitudinal designs by Bolger, Laurenceau, and colleagues	Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward
Type	Intensive longitudinal multi-informant data collection technique	Quantitative / mixed data collection technique
Source fondatrice	Bolger, N., & Laurenceau, J.-P. (2013). Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research. Guilford Press. ISBN: 978-1462506781	Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256.
Alias	multi-informant ESM, dyadic ESM, multi-respondent ecological momentary assessment, MSESM	sensor measurement, instrumented data collection, physical sensor logging, IoT data collection
Apparentées	6	5
Résumé	Multi-source Mobile Experience Sampling extends the standard ESM design by simultaneously collecting repeated momentary self-reports from two or more linked informant types — such as patient and caregiver, employee and supervisor, or partners in a dyad — via their smartphones. Signals are delivered concurrently across sources, enabling researchers to examine convergences and discrepancies between informants' real-time experiences and to model interpersonal dynamics at the moment they unfold in daily life.	Sensor data collection uses physical or digital instruments to automatically capture quantitative measurements from the environment, human bodies, or machines over time. Common sensors measure temperature, motion, heart rate, location, light, sound, or chemical properties. Because the recording is automated and continuous, the method can produce high-frequency datasets with minimal researcher burden, making it central to IoT, environmental monitoring, wearable research, and behavioral studies.