Most Significant Change
Also known as: MSC, MSC Technique, Story-Based Monitoring, Davies-Dart Most Significant Change
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.
Key highlights
- Captures qualitative, unexpected and emergent outcomes that predefined indicators miss.
- Makes stakeholder value judgements about significance explicit, transparent and discussable.
- Generates rich, concrete narratives that communicate impact compellingly to diverse audiences.
- Builds organisational learning and dialogue through the recurring selection conversations.
Intuition
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How it works
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When to use it
Use MSC when programs produce diverse, qualitative or unpredictable outcomes, when stakeholder values about what matters most need to be surfaced and clarified, and when participatory learning is a goal — common in complex social, community and development programs. It works well as a complement to indicator-based monitoring, adding depth and the capacity to detect the unexpected. It assumes participants can articulate meaningful change stories and that panels can be convened to deliberate. It is less appropriate when precise, comparable quantitative measures are required, when representative sampling of outcomes is essential, or when the program's effects are uniform and well captured by predefined indicators. It pairs naturally with outcome harvesting and outcome mapping.
Strengths & limitations
- Captures qualitative, unexpected and emergent outcomes that predefined indicators miss.
- Makes stakeholder value judgements about significance explicit, transparent and discussable.
- Generates rich, concrete narratives that communicate impact compellingly to diverse audiences.
- Builds organisational learning and dialogue through the recurring selection conversations.
- Stories are purposively selected, not representative, so they cannot quantify how widespread a change is.
- Vulnerable to selection bias toward dramatic or favourable stories and away from failures.
- Convening multi-level selection panels and documenting reasoning is time-consuming.
- Provides no measure of magnitude or attribution, so it cannot stand alone as impact evidence.
Common pitfalls
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Applications
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Frequently asked
Why does MSC avoid predefined indicators?
Because in complex programs the most important changes often cannot be foreseen, and imposing indicators in advance forecloses detection of the unexpected. MSC instead lets storytellers and selection panels decide what counts as significant, which surfaces emergent outcomes and makes the underlying value judgements explicit. The deliberation about why one story matters more than another is itself a key output, clarifying for the organisation what kind of impact it is really pursuing.
Are the selected stories representative of the whole program?
No, and they are not meant to be. MSC purposively selects the most significant changes, not a representative sample, so it cannot tell you how common a change is or its average magnitude. This is why it is usually combined with quantitative monitoring: indicators establish reach and scale, while MSC provides depth, meaning and the capacity to catch the unexpected. Interpreting selected stories as typical results is a common and serious misuse.
How does MSC handle verification and bias?
The 2005 guide recommends documenting the reasons for every selection, feeding selected stories back to stakeholders, and verifying important stories by revisiting the events they describe. These steps add transparency and a check on accuracy. They do not eliminate the inherent bias toward dramatic or positive stories, so good practice deliberately seeks accounts of negative or disappointing change as well, and treats MSC as one strand of evidence rather than a stand-alone proof of impact.
Sources
- 1.Davies, R., & Dart, J. (2005). The 'Most Significant Change' (MSC) Technique: A Guide to Its Use.
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Cite this page
ScholarGate. (2026, June 22). Most Significant Change. ScholarGate. https://scholargate.app/public-policy/most-significant-change