Qualitative data

How can you analyse qualitative data to understand the reasons for change?

Qualitative data is descriptive data that is not numerical; for example, feedback collected through open-ended responses to surveys, interviews, or focus groups. While quantitative data can tell you how much something has changed, and for whom, analysing qualitative data can help you understand the nature of that change, and why it has occurred. It can offer insights into which aspects of your service work well, and why, as well as those that don’t.

This section offers guidance on two approaches to qualitative data analysis.

How to analyse qualitative data

1. Choose your approach

There are two main approaches to analysing qualitative data.

Code and count
This involves coding your data into categories – teachers, workbooks, or peer support, for example – and counting the number of responses. This is helpful for understanding how many people gave a particular response, particularly if you have a larger sample and the data can be separated into distinct categories. However, this does not enable you to capture the strength of feeling associated with responses, and, if your sample size is small, you may not be able to generalise from the data.

A ‘code’ is made up of three parts:

  • The code itself – a number or letter that represents the code
  • The category it represents (e.g. peer support)
  • What is included or excluded (e.g. “Include references to positive interactions with classmates. Do not include negative interactions or interactions with individuals outside of the classroom”)

Theme and explore
This involves identifying themes from your data and exploring how different people have responded to these; for example, the role of social media, education outside of the classroom, or the influence of role models. This is good for smaller sample sizes and more complex subjects. It is particularly helpful when your respondents have different understandings of the same issue and you want to compare them. It can also help develop findings around how your work has contributed to changes compared with other factors.

For this approach, a theme is also a category but may not have rigid inclusion and exclusion criteria. Themes are usually decided on after you’ve read most or all of the responses. For example, if you interviewed people about their attitudes to loneliness, you may find the following themes emerge as you read through the transcripts: social isolation, physical health, and socio-emotional wellbeing.

2. Categorise your data

Now that you have your codes or themes, you can use them to sort your data before summarising what it says. You can categorise data in various ways.

By hand: With a small amount of paper-based data and a small number of codes or themes, you can categorise by hand. Make a note of the codes or themes in the margin. You can then cut up the transcripts and paste them onto larger sheets of paper, one for each code or theme.

Using MS Word or Google Docs: You can take a similar approach to paper-based data. Use the comments feature to make notes in the margin, or copy and paste sections of your transcripts into a new document under each code or theme.

Using a spreadsheet: If you are using code and count, create a column for each code and put a ‘1’ in the column if that code is mentioned in the survey response. You can then use the ‘sum’ formula to count how many times the code is mentioned, and the ‘filter’ function to view all the responses for a particular code.

Using data analysis software: You can use a software package to analyse qualitative data. Quirkos is an affordable option if you are working with text. Atlas.ti enables you to work with text, images, audio, and video data. MAXQDA and NVivo are the market leaders for working with both qualitative and quantitative data. These packages allow you to code data more quickly, search for codes or groups of codes, and visualise your data in graphs or charts. If you analyse qualitative data regularly, then you may wish to invest in them.

Tips for categorising data

  • Data can be categorised into more than one code or theme, but try not to do this too often.
  • If using code and count, you will need to make notes of how often each code appears. You may want to create a table or tally chart to do this.
  • You will need a category for ‘don’t know’, ‘no answer’ or ‘other’ responses. If ‘other’ responses make up more than 5% of your total, look at the data again to identify additional codes or themes. This helps make sure you’re not missing any important themes.
  • It can be helpful to write notes to yourself as you go through your data, and highlight interesting quotes

3. Think critically about your data

Once you have categorised your data, questions you might want to ask of your data include:

  • Are there any links between codes? Are some things mentioned together frequently?
  • Are there any other patterns, themes, or trends? Are there any deviations from these patterns?
  • Are outcomes different for different groups of people?
  • Why were some outcomes achieved, and others not achieved? How does this link to the outputs?
  • How do people understand their journey or story? What do they think has caused or affected the outcomes they have experienced?
  • What has surprised you about the data? What has challenged your assumptions?
  • Are there any gaps? What do you need to find out more about?

Make sure your analysis can be verified and you can justify the claims that you make.

  • Keep a paper trail including copies of your notes and your coded data.
  • Check your analysis with others. It can be helpful to have two people code some of the data to check whether the coding matches. You may also wish to check your analysis with your evaluation respondents to confirm you are representing them accurately.
  • Wherever possible, check data from different sources to see if the results are the same or different.
  • Check your own biases. Write down your initial views on the data and deliberately look for evidence to dis-confirm your views.
  • Coding your data can result in looking at statements out of context. Check back against the rest of the data provided by a respondent to make sure you haven’t misinterpreted them.

Adapted from content from Inspiring Impact partner NCVO

Not sure where to start?

The Data Diagnostic asks 10 multiple choice questions about what your programme or service is, how it works, and who it targets. It then provides a tailored report recommending what kind of data you should consider collecting and how.

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