Toolkit – analysing your data

In order to analyse the data you have collected you need to categorise or ‘test’ it against something
Toolkit – analysing your data

Some areas of work organise research data using standard indicators or methods e.g. Strengths and difficulties questionnaire (SDQ) is often used in schools.

Good from Woods has created a set of wellbeing categories and indicators that reflect the New Economic Foundation and NHS’s 5 steps to wellbeing.

Using Good from Woods indicators

The Good from Woods research project uses five wellbeing categories:

  • psychological
  • emotional
  • physical
  • social
  • biophilic – natural connection.

Each of these categories contains a number of indicators e.g. ‘feelings of being in control’ is an indicator of psychological wellbeing and ‘feeling accepted by others’ is an indicator of social wellbeing.

Assigning indicators to your data

Once you’ve gathered your data, you’ll need to break it down into chunks e.g. an answer to an interview question might become four separate chunks of data.Each of these chunks of data might be coded with a different indicator. So once your data is ‘chunked up’ you can start assigning indicators to each bit.

Our data analysis spreadsheet (xls) shows examples of how to organise, add and analyse your data (the second tab contains the Good from Woods wellbeing indicators).

Positive and negative indicators

Ensure you record negative as well as positive occurrences of the indicators.

Example [from a written observation]: ‘AA seems very under confident physically, walking over rough ground is tricky and he doesn’t like small branches crossing the path’. This is a negative occurrence of ‘confidence in and enjoyment of physical activity’.

Assigning more than one indicator to a chunk of data

Sometimes you might want to use more than one indicator to the same piece of data.

Example 1: ‘All the students enjoyed sitting round the fire and seemed relaxed and happy.’
Indicator 1: Relaxed; Indicator 2: Experiencing positive emotions and moods
Example 2: ‘BB reminded people not to walk too close to the fire’
Indicator 1: Feelings of being competent; Indicator 2: Confidence in supporting others

Creating your own indicators

When you start searching and analysing your data, you may find that you want to add one or more of your own indicators.

Example: In some Good from Woods projects these extra indicators were added as they seemed to recur in the data: feelings of awe or wonder, a feeling of being outside of yourselves or part of a bigger picture.

Try to keep a balance between adding new indicators and sticking to the ones already provided. Too many new indicators means your research won’t be comparable with others and make analysis more difficult. But don’t try to shoehorn data under indicators where it really doesn’t fit, as you may miss an important/interesting theme.

Subjectivity

Using indicators to code your data is not an exact science! Different practitioner-researchers will understand and interpret the indicators in different ways. The important thing is that you are consistent within your own coding. It’s useful to add notes to explain how you’ve used an indicator as well as examples in your report.

How do I really know that’s what people are feeling?

You may feel you are interpreting other people’s feelings based on your own reactions and emotions, especially if you are working with very young children or people who have limited verbal communication. However, all research is about interpreting. It’s good to use a number of different methods to access people’s views and feelings, which can help confirm what you think is happening.

Example: when working with a group of people with learning disabilities, a researcher was concerned that the observations in her reflective diary did not really represent what participants were feeling. Comparison with another researcher’s observations and analysis of videos backed up her choice of indicators.

You could also try taking your findings back to the groups you’ve been working with to check them.

Example: showing participants and their carers some of the video footage that was taken and asking them to discuss what they think of it to help confirm or challenge your ideas.

Who, what, where does wellbeing come from

After you have assigned an indicator to a chunk of data, decide who, what or where is the main contributor to the wellbeing. This can help you to work out how people are accessing wellbeing, from the environment, people or activity.

Example 1: ‘CC seemed to enjoy cooking the popcorn and was really concentrating.’
Indicator: Purposeful; Who,what,where: What – practical task.
Example 2: ‘The rest of the group were very encouraging to DD who was walking through the wood’
Indicator: Safe and supported in social relationships; Who,what,where: Who – whole group.

Good from Woods projects each made up their own Who, What, Where labels and what you use will depend on the setting and your group, so this Toolkit doesn’t contain a list. To give you an idea, some other examples were: What – being alone, the fire, doing something unusual; Who – staff and group leaders, small group, self; Where – in the woods, in a familiar place, in a different place.

Don’t be afraid to chuck out data

Not all the data you collect will generate useful evidence. Do discard anything that you feel doesn’t contribute to the research – you’ll have plenty of other data to analyse!

Example: ‘Our feedback games (playing woodland charades) were fun and people joined in, but we didn’t feel the information collected told us anything about how they had felt about being in woodland.’

Sorting data when you have analysed it

You can use the data analysis spreadsheet (xls) to help you group examples of certain indicators together when you have finished coding. This can help you to see the number of examples of each indicator in one place. But remember that your data is qualitative rather than quantitative, so a high number of occurrences of a particular indicator may not be significant.