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Outcome Harvesting×Contribution Analysis×
OborPublic PolicyPublic Policy
RodinaProcess / pipelineProcess / pipeline
Rok vzniku20122001
TvůrceRicardo Wilson-Grau & Heather BrittJohn Mayne
TypRetrospective, outcome-led evaluation approachTheory-based approach to causal inference about contribution
Původní zdrojWilson-Grau, R., & Britt, H. (2012). Outcome Harvesting. Cairo: Ford Foundation MENA Office (revised November 2013). link ↗Mayne, J. (2012). Contribution analysis: Coming of age? Evaluation, 18(3), 270–280. DOI ↗
Další názvyOH, Wilson-Grau Outcome HarvestingMayne's Contribution Analysis, Contribution Story Analysis, Theory-Based Contribution Analysis
Příbuzné43
ShrnutíOutcome 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.Contribution analysis is a theory-based evaluation approach that addresses the attribution problem — establishing whether and how an intervention made a difference — without relying on an experimental counterfactual. Developed by John Mayne from 2001 onward, it works by articulating the program's theory of change, gathering evidence along that chain, and then assembling a 'contribution story' that is progressively stress-tested against rival explanations. The aim is not statistical attribution but a credible, evidence-based conclusion that the program plausibly contributed to observed results, in the face of other influencing factors.
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ScholarGatePorovnat metody: Outcome Harvesting · Contribution Analysis. Získáno 2026-06-24 z https://scholargate.app/cs/compare