You’ve gathered your raw qualitative data, and you may have jotted down some early ideas about the themes coming through. You’re ready to get into data analysis. Here’s how:   

Coding and thematic analysis

Braun and Clarke (2006) developed a useful framework for conducting qualitative analysis, which is summarised here:

Get to know your data: Start by reading through your full data set, ideally a few times. Jot down your early thoughts and impressions.

Generate early codes: A code is like a basic descriptive ‘tag’ that you add to qualitative comments. Coding helps you to boil down your data into little nuggets of meaning.

For instance, if someone said, “I love team sport because I’m competitive and I like being part of a group,” you may add the tags:

• team sport
• pro-competition
• pro-groups

You don’t need to code every single piece of text – just focus on the quotes or content that feels relevant to your research question. 

In terms of practicalities, you can code:

• On a Word document or print-out. Put your quotes on the left side, line by line, and your codes on the right.
• On an Excel sheet, again putting one quote per line on the left, and then adding codes to the right.
• Using tools like Qualtrics (for text) or Transana (for images and audio data). However, these are advanced tools, and you can get robust results without them.

You can also use colours to help you code. For instance, if you are looking at the barriers and drivers to participation in sport, barriers could be highlighted in pink and drivers in green. 

Develop and refine themes 

Qualitative data contains themes, and it’s your job to uncover them using thematic analysis. It can be useful to look at the data from both the bottom up and the top down.

Each theme is “a pattern that captures something significant or interesting about the data and/or research question” (Maguire and Delahunt, 2017, p. 3356).

How to approach theme development:

Start with a bottom-up view:

Look over your summary codes and ask what patterns you can see. Consider the following:

• How do the codes relate to each other?
• Can you group things into categories?
• Are there causes and effects?
• Are there overarching codes and sub-codes?

Then try a top-down approach:

Step back, look over the data as a whole, and see what you notice. How would you summarise this data for someone else?

Build a skeleton structure

At the end of this step, your data will be organised in a way that simplifies what you’ve heard but still captures the essence of what was shared. You’ll end up with a ‘skeleton structure’. There is no right or wrong way to develop this. Just keep playing with it until you have a version that fits.

Flesh out the skeleton structure

It can be very helpful to flesh out your skeleton structure by copying and pasting key quotes or notes under each of your themes. For example, under “Early personal experiences – positive – scored goals” you may put this quote:

“I remember scoring a goal at netball, in my second-ever game. Everyone cheered and I won player of the day. It was a great feeling.” (15yo netball player)

This process will give you confidence that your themes are a good fit for the data. And if they’re not, keep adjusting them until they are suitable.

Stress-test your themes

Look over your thematic analysis and ask yourself some reflective questions (Maguire and Delahunt, 2017):

• Do the themes make sense? 
• Does the data support the themes? 
• Am I trying to fit too much into a theme? 
• If themes overlap, are they really separate themes? 
• Are there themes within themes (sub-themes)? 
• Are there other themes within the data? 

Convert your themes into one-liners

The final step in thematic analysis is to clearly identify the essence of each theme. This is what you’re going to report in your final write-up or presentation. For instance:

Keep in mind: Subjectivity

Qualitative analysis is subjective. This means it is affected by you, the researcher, and the ‘lens’ through which you view the world. Your lens is shaped by your history, your demographics, your personality, your hopes and fears and more. You can’t remove your lens, but you should think about how your perspective affects your findings. Ask yourself: 

• What assumptions did I have before I started this study? 
• How might my identity influence what people are choosing to share? 
• How has my background affected how I am analysing this data? What am I paying most attention to, and why might that be? 
• What conclusions do I want to make, and how might that affect my interpretation?