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| Échantillonnage mobile triangulé des expériences× | Échantillonnage expérientiel mobile multi-sources× | |
|---|---|---|
| Domaine | Méthodologie d'enquête | Méthodologie d'enquête |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 2000s–present (as an integrated mobile ESM variant) | 2000s–2010s |
| Auteur d'origine≠ | Csikszentmihalyi & Larson (ESM, 1983); Denzin (triangulation, 1978); integrated in HCI/health informatics research from the 2000s onward | Developed from ESM (Csikszentmihalyi & Larson, 1983) and extended to multi-informant intensive longitudinal designs by Bolger, Laurenceau, and colleagues |
| Type≠ | Mixed/multi-source data collection technique | Intensive longitudinal multi-informant data collection technique |
| Source fondatrice≠ | Csikszentmihalyi, M., & Larson, R. (1983). The Experience Sampling Method. In H. T. Reis (Ed.), Naturalistic Approaches to Studying Social Interaction (pp. 41–56). Jossey-Bass. link ↗ | Bolger, N., & Laurenceau, J.-P. (2013). Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research. Guilford Press. ISBN: 978-1462506781 |
| Alias | triangulated ESM, multi-source mobile ESM, triangulated ecological momentary assessment, triangulated mobile EMA | multi-informant ESM, dyadic ESM, multi-respondent ecological momentary assessment, MSESM |
| Apparentées≠ | 4 | 6 |
| Résumé≠ | Triangulated Mobile Experience Sampling combines the Experience Sampling Method (ESM) — repeated, real-time self-reports delivered via smartphone — with deliberate triangulation across two or more data sources, instruments, or methods. By converging mobile survey prompts with passive sensor streams, behavioral logs, or complementary qualitative probes, the technique strengthens construct validity and enables cross-verification of findings collected in participants' natural environments. | 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. |
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