Step 5. Interpret the data
- Nicole Mc Neilly
We have designed some practical steps to help draw out useful conclusions and to finish off Phase two.
Interpretation is when you go through the data and go through some thinking processes, consciously or unconsciously, to create or assign meaning. This means, for example, looking at the results of a question and using it to answer the bigger research question.
The chances are that you might already have done this unconsciously in the last step of data analysis. But here we set out some tips and a checklist to help you arrive at a point where you have extracted as much information as possible from your data.
Look back at your change pathway
Before you start, it’s always useful to recap and remind yourselves of the main elements of the impact designs you drew up in Phase one.
Lead your team through the data you have gathered and together answer the following questions.
What do you think of these patterns (or their absence)?
What has changed and what has not?
What did you expect to see but not find in these patterns?
Did anything surprise you?
Have you learnt anything about the process of developing your change pathway and the assumptions in this?
Analysing your dataset in this way will naturally lead you to a few conclusions. Which of these conclusions are most important to you? Can you prioritise them? If you look at the patterns you found, where do you draw your accountability line?
Tip.
It can be nice to share your insights and preliminary conclusions as you are working your way through the data. Just make sure that the findings can be easily understood when they are out of context and make it clear that the data can only be shared internally.
Validation and review
Is there anyone else who can take a look at the data and help with the interpretation? Can someone validate your findings? What do they see in the data that maybe you didn’t see?
Who could help test, challenge or support your interpretation?
Tip.
In some social sciences, it is good practice to share the results (the report) with those you have surveyed or observed. This is more possible with qualitative investigations, e.g. focus groups.
Determining and being open about validity and representativeness
Be transparent about the validity of your research and how it is, or isn’t, representative of those who you wanted to survey. Even if you can’t draw representative conclusions, you might have learned something important, for example, about how to collect more or higher quality data in future. You will also be able to report on what you find in your data, though you should always make it clear that this is only an indication rather than proof of an outcome occurring. Future research will help you draw out more concrete observations. Add in some future research recommendations to your report, and don’t lose hope.