Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Muestreo de Experiencias Móviles Pilotado× | Recopilación de datos de sensores× | |
|---|---|---|
| Campo | Metodología de encuestas | Metodología de encuestas |
| Familia | Process / pipeline | Process / pipeline |
| Año de origen≠ | 2000s–2010s (mobile ESM); pilot-testing practice codified in 2010s | 1990s–2000s (widespread deployment with IoT ~2000s) |
| Autor original≠ | Reed Larson & Mihaly Csikszentmihalyi (ESM); mobile adaptation developed across 2000s–2010s | Multidisciplinary; sensor networks formalized in engineering and computer science from the 1990s onward |
| Tipo≠ | Data collection technique | Quantitative / mixed data collection technique |
| Fuente seminal≠ | Larson, R., & Csikszentmihalyi, M. (1983). The experience sampling method. New Directions for Methodology of Social and Behavioral Science, 15, 41–56. link ↗ | Chong, C.-Y., & Kumar, S. P. (2003). Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8), 1247–1256. DOI ↗ |
| Alias | pilot-tested mobile ESM, pretested mESM, validated mobile experience sampling, mobile ESM with pilot phase | sensor measurement, instrumented data collection, physical sensor logging, IoT data collection |
| Relacionados≠ | 4 | 5 |
| Resumen≠ | Pilot-tested mobile experience sampling (mESM) is a data collection approach that combines smartphone-delivered, real-time self-report prompts — the Experience Sampling Method — with a structured pilot phase to validate the instrument, signal timing, burden level, and response quality before full deployment. The pilot phase is not optional decoration; it is the core quality gate that separates a rigorously validated mESM study from an ad hoc one. | 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. |
| ScholarGateConjunto de datos ↗ |
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