How to get started with your analysis

Qual Academy partner Elly Phillips

October 28, 2026

You've finally gathered your data and are ready to start getting your teeth into the analysis. It's exciting, but can be intimidating when you're faced with pages of data. You might be wondering where to start, or be worried about doing something 'wrong'. 

Note: I'm thinking primarily about IPA and reflexive TA while writing this.

With IPA, we work through each case in turn and in detail. Smith et al. (2022) mention that this is often the first interview, but it doesn't have to be. It can be one that is engaging, interesting and not too long (Smith et al., 2022, p. 78). It's worth skimming through each to make that choice, or thinking back to your interviews, because you're also going to be practising your skills, so you want to give yourself the most material to work with. I might look for an interview where the participant has quite a bit to say (so you have longer stretches of their words to work with), perhaps that they describe things in a lot of detail, or use interesting imagery/phrasing, or have strong opinions. Those are all things that can give you more 'meat' to grapple with. 

With reflexive TA, you should start by familiarising yourself with the whole dataset, which means reading through and potentially listening to recordings again (Braun & Clarke, 2022). If you've done your own transcription, you might be quite familiar with the data and have the sound of the interviews 'in your ear' once you've finished that stage, which does make a strong case for doing the transcription! But it's still worth returning to the first item you transcribed and reading through without the stops and starts and long-drawn-out timeframe that is unavoidable with the process of transcribing. You might use similar criteria for selecting a first case: the participant has a lot to say, that seems relevant to your research question, and I'd perhaps pick something where I feel interested in what they have to say (it's well described, or contradicts my own views of the topic, or makes me think or respond to it in some way). That'll help keep your engagement up and give you plenty to work with.

And if you get stuck, it's fine to ask for help (see the last point) or switch. You want to make initial progress to help you get started! 

Remind yourself that qual analysis is a process, not a line

You won't 'see' everything the first time through, and your ideas about the data will continue to develop, not only through each step of your analysis, but as you move through cases AND as you reach later steps and are inspired to circle back to the beginning. Try to frame this as a positive - you don't have to get everything right the first time, and there will be more that develops as you progress. I've had plenty of data items where my first pass on noting or coding was pretty sparse, but as I kept working with the data, it developed more. This can be a really hard thing for a researcher on their first big project, so if you need to refer back to this post as reassurance, please do so!

I always recommend allocating multiple dedicated time slots spaced apart to work with a data item. Being able to step away and let ideas percolate can be really productive and removes the pressure of 'needing' to write something down in that moment. If you have a suitable device, you might use voice memos to capture inspiration when you're away from your desk (my dog-walking time tends to be quite productive).

Work from the middle or end of the data regularly. The opening of an interview or focus group can be more about icebreaking or providing general background information, and you might find more of interest as the participant (and researcher) get 'warmed up' and dig more into the topic. It's also easy to cycle through the same opening text again and again, and then it gets really hard to see anything new.

You also don't have to completely finish coding or noting to move onto the next stage if you're really stuck. While insufficient noting or coding is a problem, writing something about your ideas can be a good way to prompt new lines of thinking that you can take back to your data. I'm a huge fan of writing as a tool for analysis. Doing something different can be a good way to get out of a rut, and if you're not feeling like you're making progress, it can be most helpful to do something different or new.

If you are seeking input from a supervisor and have limited time available, good points to seek detailed feedback are:

• When you've noted or coded your first interview (questions to ask include: is this in enough detail? Am I making appropriate comments considering my approach and theoretical stance? Are the codes/notes clearly justified from the data?)

• When you've generated some initial ideas for themes/personal experiential statements/personal experiential themes (again to check that your ideas fit the analytic approach you're using, if there's 'enough' - or too much, if you're missing anything, or your supervisor has any additional insights based on their expertise that give you new avenues to explore)

• Some initial writing, e.g., a theme with quotes, analytic comments and your narrative

My experience as a research supervisor is that often it doesn't take much time to review a few pages of coded transcript or basic analytic writing, but that small section can identify useful pointers and actions for a novice researcher. I'm also a huge fan of collaboration in research, and, while a research degree doesn't enable extensive joint work, there are still opportunities to find inspiration and motivation in interactions with others.

Your next steps

1. Pick the data item you're most excited about working on and begin there

2. Set a small, manageable task (e.g., read through and note down anything that really stands out)

3. Schedule a few timeslots to sit with the same piece of data

4. Find your research support - set a meeting with your supervisor or plan a working lunch with other students to review your initial ideas.

5. Bookmark this post and re-read it if you feel stuck in the future or need reassurance.

References

Braun, V., & Clarke, V. (2022). Thematic Analysis: A practical guide. SAGE.

Smith, J. A., Flowers, P., & Larkin, M. (2022). Interpretative phenomenological analysis: Theory, method and research (2nd ed.). SAGE.