Methoden vergleichen
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| Longitudinal Mobile Experience Sampling× | Longitudinal Diary Method× | |
|---|---|---|
| Fachgebiet | Umfragemethodik | Umfragemethodik |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1983 (ESM origins); 2000s onward (mobile longitudinal variants) | 1942 (diary method); longitudinal variant formalised 1980s–2000s |
| Urheber≠ | Csikszentmihalyi & Larson (ESM, 1983); Shiffman, Stone & Hufford (EMA, 2008); extended to longitudinal mobile designs by Hamaker and colleagues | Allport (1942); systematic longitudinal extension developed by Bolger, Davis & Rafaeli (2003) |
| Typ≠ | Longitudinal intensive data collection technique | Longitudinal qualitative/quantitative data collection |
| Wegweisende Quelle≠ | Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32. 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 ↗ |
| Aliasnamen | Longitudinal ESM, Longitudinal EMA, Longitudinal Ecological Momentary Assessment, Long-term mESM | diary study (longitudinal), daily diary method, repeated-measures diary, longitudinal self-report diary |
| Verwandt≠ | 4 | 3 |
| Zusammenfassung≠ | Longitudinal Mobile Experience Sampling combines the real-time, in-context signal capture of Experience Sampling Method (ESM) with a longitudinal design spanning weeks, months, or longer. Participants respond to repeated prompts delivered to their smartphones across multiple time waves, enabling researchers to observe within-person change, stability, and dynamic processes as they unfold in daily life rather than in retrospective recall. | 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. |
| ScholarGateDatensatz ↗ |
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