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Outcome Harvesting×Most Significant Change×
CampPublic PolicyPublic Policy
FamíliaProcess / pipelineProcess / pipeline
Any d'origen20122005
Autor originalRicardo Wilson-Grau & Heather BrittRick Davies & Jess Dart
TipusRetrospective, outcome-led evaluation approachParticipatory, story-based monitoring and evaluation technique
Font seminalWilson-Grau, R., & Britt, H. (2012). Outcome Harvesting. Cairo: Ford Foundation MENA Office (revised November 2013). link ↗Davies, R., & Dart, J. (2005). The 'Most Significant Change' (MSC) Technique: A Guide to Its Use. link ↗
ÀliesOH, Wilson-Grau Outcome HarvestingMSC, MSC Technique, Story-Based Monitoring, Davies-Dart Most Significant Change
Relacionats44
ResumOutcome Harvesting is a participatory evaluation approach, developed by Ricardo Wilson-Grau and Heather Britt, that identifies outcomes after they have occurred and then works backward to determine whether and how an intervention contributed to them. Instead of measuring progress against predefined targets, evaluators 'harvest' evidence of observable changes in the behaviour, relationships, actions or policies of social actors, then assess the program's contribution to each. It is designed for complex settings where cause-and-effect relationships are not fully understood in advance and outcomes cannot be specified ahead of time.The Most Significant Change (MSC) technique is a participatory, story-based approach to monitoring and evaluation developed by Rick Davies and refined with Jess Dart. It involves the systematic collection of stories of significant change from the field and the deliberative selection of the most significant of these by panels of stakeholders. There are no predefined indicators; instead, value judgements about what change matters most are made transparently by those involved, making MSC especially suited to capturing unexpected and qualitative outcomes in complex programs.
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ScholarGateCompara mètodes: Outcome Harvesting · Most Significant Change. Recuperat el 2026-06-24 de https://scholargate.app/ca/compare