You probably have more of an M&E framework than you realise. Somewhere there is a Theory of Change, written during a strategy process a few years back. There is a logic model built for a funding proposal, an indicator table inside a reporting template, and a data collection plan your programme team relies on. Each document was developed to meet a particular need.

In our work at Fundamental, we find that a framework’s value lies in how these documents connect. A Theory of Change, a logic model, indicators and a data collection plan describe different aspects of the same programme. When they align, M&E becomes a practical way of understanding your work. When they are scattered, you can end up with a great deal of information and no clear sense of what it is telling you.

Beginning with the change

Imagine a programme that prepares young people for the workplace. It runs employability workshops, offers one-to-one coaching and helps participants with job applications. Its broader hope is that young people will be better prepared to find their way into work.

Its Theory of Change might start from a simple line of reasoning: when young people have practical skills, personal support and chances to apply what they learn, they are more likely to look for work with confidence and take up the opportunities that come their way. This reasoning helps the programme decide what to measure, rather than basing measurement decisions on what is easiest to record.

Making the pathway visible

The logic model sets out the activities and expected results. The activities are the workshops, coaching, CV preparation and application support. These produce outputs: workshops delivered, young people reached, coaching sessions completed, CVs written.

Outputs are important evidence of programme delivery, but they do not show whether participants have benefited. Outcomes describe the changes the programme aims to support, such as stronger job-search skills, more confidence in recruitment processes and, further along, more applications, interviews and jobs.

Each step needs its own evidence. Knowing a workshop took place says something about delivery. It does not say whether participants learned anything, or whether they were able to use it. As we discussed in our earlier piece on evidence-based reporting, the risk is claiming more than your data can support.

Indicators as questions worth asking

Instead of asking what you can measure, ask what you need to know to tell whether each part of the programme is working as you expect.

For the employability programme, an output indicator might be the number of participants who complete the workshops. An outcome indicator might look at whether participants understand job-search strategies better afterwards, or feel more confident about handling job interviews. A longer-term indicator could track how many secure interviews within a set period. Each one is tied to a specific part of the programme’s thinking.

Someone also has to gather the information behind each indicator. Who collects it, and when? Which tool do they use? Suppose you adopt an indicator on employment six months after the programme ends. It is a sound idea, but if there is no realistic way of staying in touch with participants once they leave, you will struggle to report on it. The indicator may be appropriate, but it needs a feasible data collection plan.

Keeping your framework aligned

In most frameworks we review, there is already a strong foundation to build on. As programmes evolve, funder requirements change, indicators are added and responsibility is shared across teams, the documents naturally need refreshing so that they continue to reflect one another.

A programme might describe its purpose as helping young people into work, while nearly all its indicators count workshops and participants. Those figures matter, but they show what the programme did, not the change it set out to make. In another organisation, teams may spend each month gathering data that no longer connects to any outcome or decision. In both cases, the documents and data are available, but they no longer clearly reflect the programme’s intended outcomes.

Reading the framework both ways

A useful way to check whether your framework fits together is to read it in both directions. Start with the change you hope to support, then follow the connections: which outcomes would show progress towards that change? What activities would help bring those outcomes about? Which indicators would show progress, and what information would you need to collect?

Then work backwards from the data you collect. Why are you collecting it? Which indicator does it inform? Which outcome does that indicator help you understand? If you struggle to answer any of these questions, revisit the link between the data, the indicator and the intended outcome.

The thinking that connects

A framework should be reviewed as the programme develops and the team learns from experience. When its components are consistent, the evidence helps you explain your programme’s work and results to funders, rather than relying solely on their reporting templates.

An M&E framework brings the Theory of Change, logic model, outcomes, indicators, sources of evidence and data collection plan together through a shared understanding of how the programme is expected to work. Much of this understanding may already inform your team’s day-to-day practice. Making it explicit helps you assess progress and make informed decisions about what to do next.

If you would like to work through this with your own framework, Fundamental’s courses and mentoring programmes can help. Explore our courses.