A clear set of indicators is essential when measuring the impact of any project, especially in a complex situation. Someone in a recent webinar asked the speakers how to tell whether an indicator is actually helping you measure the right changes, which inspired this post – like so many webinar conversations do!

First of all, I like to design indicators to measure at least one level of change in a project – using the levels logic of a logframe. In other words, it should help answer both the success of an output, and the contributions to change in an outcome statement. The number of people that attend a training is a good simple indicator for the success of a training session (the output) where the outcome could be something like building awareness among funders of best practices in emergency grantmaking.

Here are some tips I’ve used that can help make your indicators multipurpose:

Link them with your project objectives

Follow the causal path of your TOC, or any logic modern, to ensure they effectively measure outputs, outcomes, and objectives. To do that, you may need to define clear, measurable indicators for each objective. These should reflect the changes or results expected from the project. This alignment helps ensure the data you gather will measure not just your work and impact, but also help test the logic flow. If the line indicator > output > outcome > intermediate result > objective is clear, then your logic thread also holds.

Use SMART or SPICED Indicators

I’ve blogged about this elsewhere so check out these other posts:

Checklist: the Pros of SMART Goals

Resources for Standard Indicators

Worksheet: SPICED Indicators

Choose the Right Type of Indicators

Pick indicators at the right level, to help answer a specific question. For example, an output indicator measures the immediate results of an output – like the number of attendees in the training session. It can also measure specific elements of an activity, the development of products like a training guide, etc. Often, an output indicator can be immediately measured – or soon after you’ve conducted an activity.

An outcome indicator is outside the scope of your organisation’s direct control since outputs measure changes in states or conditions, like improved knowledge or awareness – which may not be within your project’s scope to address fully. So wording your indicator differently, and having specific criteria and data to speak to each level, can help answer different but related questions.

Validation

It’s useful to test your indicator so that it can accurately reflect these changes, and also test the validity of your project’s assumptions. You may want to test it with partner organisations who’ve done similar work, with other project teams in your organisation, or your community depending on the relationship you have with them. Each group will bring different expertise and can help you refine the ideas to ensure you’re testing at the right level, and that the indicators consider key assumptions – like the interest among grantmakers to attend a training session.

Review and Adaptation

Once validated, check their alignment with your logic model, project plans, and work plan. Make sure it all still makes sense, and add new assumptions and ideas that emerge. I recommend monthly meetings to help adapt your project to meet emerging needs.