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Part 3: Review

Part 3: Review

Analysing and interpreting your evaluation data

This guidance will help you to...

  • Process and analyse your evaluation data
  • Interpret the findings of your evaluation
  • Think about how to present your findings
  • Share learning and reflect on the entire evaluation process

Introduction to analysis

Evaluation data is unlikely to tell you much in its unprocessed state. Analysis is the process of converting this data into useful information to help you understand your project. The findings will help you draw conclusions about whether your work brought about the intended outcomes.

Before you start your interpretation, you’ll need to go through the following process with the raw data:

  • consolidate it (bring it together into one place)

  • clean it (remove obviously incomplete or inaccurate data) and then analyse it.

Analysing your data

Analysing quantitative data

Quantitative data (e.g. self-assessment scales) help you understand the scale or frequency of something.

Analysing quantitative data involves reviewing all your raw data to look for patterns or trends. It helps you identify if any changes have occurred for the majority of participants or not.

The table below shows young people responses to the question: “coming to the music project has helped me feel more calm and relaxed”. This is part of a wider set of questions investigating the project’s impact on their wellbeing.

Calculating averages can help you understand the overall experience of a group of participants. For the previous example, you could say “We asked how much our music project made people more calm and relaxed. We used a scale of 1 – 5, with 5 being ‘completely agree’. The average score for our four respondents was 4.75.”

Looking at individual responses against the averages will help you understand the range of experiences. If the average score was 4.75 and someone had selected 1, you could consult the other data you collected to try and find out why their score was lower than other people’s.

If you are using baseline and follow up questionnaires, then comparing the average at two points in time can help demonstrate distance travelled.

Putting your data into charts or graphs may help you identify patterns and trends over time. See our guidance on quantitative analysis for further information.

Analysing qualitative data

Qualitative data (e.g. interviews) help you understand the range of opinions and experiences. It can help bring meaning to quantitative data.

Analysing qualitative data involves coding your raw data into themes that are relevant to the outcomes you’re measuring.

Coding is the process of labelling your data so that it can be easily found for further analysis. Like using a hashtag on social media, it can help you to easily find all the data labelled with a particular code and see it in one place. 

There are many ways to carry out this process: some people prefer to do it on paper using coloured highlighters and sticky labels, whereas others find it easier to copy and paste sections of the data into separate documents.

Whichever method you choose, separating your qualitative data into themes will help you to identify similarities between different people or sources. See our guidance on qualitative analysis for further information.

Interpreting the data

In your evaluation plan, you will have data from multiple sources (e.g. young people, music leaders, parents). The process of using multiple viewpoints to draw conclusions is called triangulation. Triangulation helps to make your findings more robust.

When analysing your data, consider how the results from different people or data compliment or contradict each other. As an example, young people have reported being more friendly to others, music leaders have seen an increase in group work, and parents have reported that their child has made new friends. In this case you can be fairly confident that young people’s interpersonal skills have improved.

Unexpected results

Sometimes, your data might reflect a result you aren’t expecting.

This is normal and there are plenty of ways to explain why this might happen.  To understand your data further, ask yourself questions like:

  • “What external factors could be contributing to this?”
  • “Do any other data sources help me explain this result?”
  • “What is surprising about the data?
  • “Is it a common trend or a one-off?”
  • “What is it telling me about the project?”

Presenting your findings

Youth Music is flexible about how you present your findings, provided that it is done within the reporting framework provided. You can decide what format is most appropriate for your audiences.

You may want to consider creative formats such as a presentation deck, video, podcast or webpage. Try and make the information accessible and engaging. If you’re doing a written report, limit yourself to a maximum word count. Use photos and charts to break up chunks of text.

Use your evaluation to continue the dialogue with the people you’ve worked with. Youth-led outputs (such as podcasts, interviews or social media campaigns) can provide interesting content from different voices. Reflecting on the findings by the beneficiaries themselves (i.e. rather than a third party) might also lead you to new learning.

Shared learning and reflections