Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Mobil erfarenhetsampling från flera källor× | Insamling av sensordata× | |
|---|---|---|
| Ämnesområde | Surveymetodik | Surveymetodik |
| Familj | Process / pipeline | Process / pipeline |
| Ursprungsår≠ | 2000s–2010s | 1990s–2000s (widespread deployment with IoT ~2000s) |
| Upphovsperson≠ | 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 |
| Typ≠ | Intensive longitudinal multi-informant data collection technique | Quantitative / mixed data collection technique |
| Ursprungskälla≠ | 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. DOI ↗ |
| Alias | multi-informant ESM, dyadic ESM, multi-respondent ecological momentary assessment, MSESM | sensor measurement, instrumented data collection, physical sensor logging, IoT data collection |
| Närliggande≠ | 6 | 5 |
| Sammanfattning≠ | 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. |
| ScholarGateDatamängd ↗ |
|
|