Evaluation · An ongoing process over months; a single selection panel takes 1-3 hours.

Most Significant Change (MSC) Technique

Most Significant Change is a participatory monitoring and evaluation technique based on collecting stories of change from people involved in a programme and having groups deliberately select the most significant ones. It captures outcomes nobody predicted and makes the values behind judgements explicit.

When to use Most Significant Change (MSC) Technique

When a programme's outcomes vary between people, are hard to predict in advance, or are not captured by indicators, and when you want stakeholders to discuss what success means.

How Most Significant Change (MSC) Technique works

Build an MSC question into follow-up sessions or check-ins after a programme, and run a short selection panel with stakeholders as part of a review workshop. The selection discussion is itself a learning activity.

Phases of Most Significant Change (MSC) Technique

  1. Raise interest and define domains: Agree why you are using MSC and set a few broad domains of change, such as changes in people's work or relationships.
  2. Set the reporting period: Decide how often stories are collected, for example monthly or after each programme phase.
  3. Collect stories of change: Ask participants and stakeholders: 'Looking back, what was the most significant change?' and why it matters to them. Record the story and the reason.
  4. Select the most significant stories: Panels at different levels read the stories and discuss which is most significant and why, recording their reasons.
  5. Feed back the results: Tell storytellers and stakeholders which stories were chosen and why, so values and priorities become visible.
  6. Verify, quantify and review: Check selected stories where needed, add any counts, and review what the process reveals about the programme.

Key principles

  • Let participants define what change matters
  • Stories plus the reason they matter
  • Transparent selection with recorded reasons
  • Feedback closes the loop

Best for

  • Development and community programmes
  • Leadership and long-term learning programmes
  • Complex change initiatives
  • Programme reviews with stakeholders

Considerations

  • Deliberately biased towards significant, often positive, stories; combine with other methods for a full picture.
  • Story collection and selection take time and facilitation skill.
  • Not designed to measure average effects or attribute causality on its own.

Attribution & sources

Developed by Rick Davies, mid-1990s; guide co-authored with Jess Dart, 2005

Primary source

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