Grant Evaluation Methods: Measuring Success and Impact

Proven evaluation methodologies and tools for assessing grant effectiveness and demonstrating impact to stakeholders.

By Plinth Team

Grant Evaluation Methods: Measuring Success and Impact

Robust evaluation shows what worked, for whom and at what cost – informing better decisions next time.

  • Start with a theory of change: Agree outcomes and indicators before funding begins.
  • Use mixed methods: Combine numbers with stories for a rounded picture.
  • Close the loop: Feed learning back into criteria and programme design.

Choosing the right methods

Common approaches include monitoring against a logic model (simple and comparable across a portfolio but can miss wider effects), before/after outcome measures for projects with direct beneficiaries (shows direction of change but weak attribution), comparison or matched groups for larger programmes (stronger causal claims but more complex), qualitative case studies to explain how change happened (rich insight but less comparable), and cost‑effectiveness analysis when options share similar outcomes (links resource use to results but needs consistent measures and cost data).

Indicators and data collection

Pick a small set of indicators per outcome, make definitions precise, and pre‑build simple tools for grantees to use. Where appropriate, signpost to validated scales for wellbeing or skills.

Portfolio learning and reporting

Aggregate results across grants using common tags for theme, geography and population. Pair charts with short case studies so trustees see both scale and stories.

How Plinth supports evaluation

Plinth helps applicants define outcomes up‑front, then reads narrative reports to extract results, tag themes and locations, and generate case studies automatically. Dashboards show expected vs actual outcomes across the portfolio, helping you spot what works and adjust criteria accordingly.

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Citations and trusted sources

About the author

Written by the Plinth Editorial Team, with input from independent evaluators. Updated August 2025.

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Frequently asked questions

How many indicators should we require?

Keep it lean: 3–5 per outcome is usually enough for comparability without overburdening grantees.

Do we need control groups?

Not always. For most grants, a clear logic model and before/after measures suffice. Use comparisons for larger programmes seeking stronger evidence.

How do we use qualitative data well?

Collect short beneficiary quotes or mini‑case studies with consent; pair with quantitative indicators for a rounded view. Plinth can generate case studies from reports.

Can monitoring be proportionate?

Yes – tailor frequency and depth to risk and grant size. Build expectations into the agreement and automate reminders.

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