Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Ukusanyaji wa Data kwa Kutumia API za Simu za Mkononi× | Uzoefu wa Simu ya Mkononi× | |
|---|---|---|
| Nyanja | Metodolojia ya Dodoso | Metodolojia ya Dodoso |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 2007–2010 (mainstream smartphone era) | 1983 |
| Mwanzilishi≠ | Emerged from mobile computing and REST/web API proliferation (Fielding, 2000; widespread adoption ~2007–2010 with smartphone ecosystem) | Mihaly Csikszentmihalyi & Reed Larson |
| Aina≠ | Digital data collection technique | Intensive longitudinal data collection technique |
| Chanzo asilia≠ | Luce, M. F., Kahn, B. E., & Malhotra, N. K. (2016). Capturing consumer experiences with mobile research methods. Journal of Consumer Research, 42(6), 949–965. 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 ↗ |
| Majina mbadala | mobile API data collection, smartphone API data harvesting, mobile app API research data collection, API-driven mobile data collection | ESM, Experience Sampling Method, Ecological Momentary Assessment, EMA |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | Mobile API-based data collection uses mobile devices (smartphones, tablets) to query application programming interfaces — structured web endpoints that return machine-readable data — enabling researchers to gather behavioral, contextual, sensor-enriched, or platform-generated data in real time from participants in their natural environments. It combines the ubiquity of mobile hardware with the scalability and standardization of RESTful or GraphQL APIs. | 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. |
| ScholarGateSeti ya data ↗ |
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