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| Criterion |  UX Research Diary Study |  Agile Affinity Estimation |  Agile NoEstimates |  Delivery Monte Carlo Forecasting |
|---|---|---|---|---|
Purposedifferent | When experiences build up over days or weeks and a single session cannot capture them, Diary Study records the course of everyday life. Recurring triggers, moods, and habits become visible this way, beyond the sharpness of memory. | When many tasks need to be classified quickly, it sorts them by perceived effort and similarity. It reduces the effort of fine-grained estimation for large volumes. | When estimating slows a team down more than it helps, it shifts the focus to flow, small slices, and real lead time. It sorts work by value, risk, and delivery ability. The result is captured as Throughput Data, Flow Forecast, and Slicing Rules. | Helps clarify scope, sequence, and delivery flow in concrete terms. It makes work, boundaries, and order steerable. The result is captured as Forecast Percentiles, Throughput Dataset, and Risk Communication. |
Complexitydifferent | Medium | Medium | Medium | High |
Timedifferent | 1-4 Wochen | 30-90 min | laufend | 30-90 min Setup, danach laufend |
Participantsdifferent | 5-20 | 3-12 | 2-12 | 1-8 |
Formatdifferent | Async | Workshop | Workshop + async | Workshop + async |
Outputdifferent | Diary Entries, Longitudinal Patterns, Experience Timeline | Affinity Size Map, Grouped Estimates, Unclear Items | Throughput Data, Flow Forecast, Slicing Rules | Forecast Percentiles, Throughput Dataset, Risk Communication |
Tagsno overlap | UX researchTrackingBehavior | EstimationBacklogRelative sizing | EstimationForecastingFlow | ForecastingFlowDelivery |