Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Méthode d'échantillonnage de l'expérience mobile× | Méthode du journal longitudinal× | |
|---|---|---|
| Domaine | Méthodologie d'enquête | Méthodologie d'enquête |
| Famille | Process / pipeline | Process / pipeline |
| Année d'origine≠ | 1983–1987 | 1942 (diary method); longitudinal variant formalised 1980s–2000s |
| Auteur d'origine≠ | Mihaly Csikszentmihalyi & Reed Larson | Allport (1942); systematic longitudinal extension developed by Bolger, Davis & Rafaeli (2003) |
| Type≠ | Intensive longitudinal data collection technique | Longitudinal qualitative/quantitative data collection |
| Source fondatrice≠ | Csikszentmihalyi, M., & Larson, R. (1987). Validity and reliability of the Experience-Sampling Method. Journal of Nervous and Mental Disease, 175(9), 526–536. DOI ↗ | Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54(1), 579–616. DOI ↗ |
| Alias | ESM, ecological momentary assessment, EMA, daily diary via mobile | diary study (longitudinal), daily diary method, repeated-measures diary, longitudinal self-report diary |
| Apparentées≠ | 4 | 3 |
| Résumé≠ | The Mobile Experience Sampling Method (ESM) collects repeated, time-stamped self-reports from participants in their natural environment using a smartphone app. By signaling participants multiple times per day over days or weeks, researchers capture psychological states, behaviors, and contexts as they occur — eliminating retrospective bias and revealing within-person dynamics that single-session surveys cannot detect. | The Longitudinal Diary Method is a data collection technique in which participants record experiences, thoughts, feelings, or behaviors in structured diary entries repeatedly over an extended period — from days to months or even years. Unlike a one-shot survey, it tracks within-person change, daily fluctuation, and temporal processes in natural settings, making it especially powerful for studying how phenomena evolve over time. |
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