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Getting to grips with qualitative research: lessons from the first year of my PhD

In this week’s blog, Oonagh Coleman writes about what she has learned in her first year as a PhD student at King’s College London. Oonagh was awarded a Mental Health Research UK PhD Scholarship in 2021 and the title of her project is: Understanding risk and building mental health resilience after childhood psychological trauma: focus on the subjective experience.

Psychological research makes use of two distinct approaches: quantitative and qualitative methods. Quantitative methods use numerical or measurable data and statistical tests to draw inferences about a particular phenomenon, whilst qualitative methods focus on language and how individuals subjectively experience and understand the world in ways that can be described and observed but not measured numerically. For my PhD I am using both quantitative and qualitative methods to explore how differences in the way in which individuals subjectively experience and remember maltreatment in childhood might lead to better or worse mental health outcomes in adulthood.

During the first year of my PhD, I have been focusing primarily on the qualitative side of this research project, using thematic framework analysis of extensive interview notes to explore the way in which individuals make sense and meaning out of their childhood experiences. I had very little prior experience of what this research method entailed and was initially pretty puzzled about how exactly such a general and slightly abstract process could actually produce meaningful results. The stages of a thematic analysis are very broad, and it is not a linear process but instead one in which you should expect to go back and forth across the steps a number of times in order to refine your ideas. The process of getting to grips with this research method has been an interesting and sometimes difficult task, but I have found it incredibly rewarding and enriching in terms of different ways to think about conducting psychological research, and I am writing this blog as a complete convert. Here are some of the key lessons I have learnt along the way…

1. Be patient – it takes time

If you go into it expecting instant results or to be able to quickly come up with themes that can be applied to the whole dataset, you will be disappointed. It is quite literally impossible to rush the process of doing a thematic analysis, which can sometimes feel at odds with the pressure to produce results and demonstrate progress in your PhD. Firstly, you have to read and familiarise yourself with the data – in my case hundreds of pages of interview notes – literally as many times as you can tolerate. Then, the process of coding the data and sorting it into themes is a very time-consuming task that requires a constant revision and could go on infinitely. I have lost count of the number of times I have been certain that I have come up with the perfect thematic framework, only to view it with fresh eyes the next day and realize that it doesn’t quite work for any number of reasons. It is important to embrace the time it takes to do this type of analysis properly, and accept that sometimes at the end of a day you might have very little to show for the work you have done, but all the hours spent familiarizing yourself with the data and working out exactly how it all might fit together into an interesting and meaningful thematic framework will make your interpretation far richer.

2. Use memos

This was recommended to me halfway through the process and was lifechanging. Keeping track of thoughts and ideas that pop into your head as you’re reading, and noting any ideas that keep appearing or possible points of comparison (however basic they might be), will make the actual data analysis and interpretation a far easier and less overwhelming process. I initially did not do this in a very thorough or systematic way and was struggling to keep track of the broader picture of how my data might fit together, however my supervisor recommended for me to use the memo function in NVivo (a qualitative data analysis software package which I thoroughly recommend using) which easily links memos to specific subjects or themes. The memos do not have to be fully formed thoughts or even sound good – no-one else is going to read them but you, so just getting into the habit of jotting down any random ideas or words that pop into your head as you go will make a huge difference.

3. Discuss your ideas

Talk to people!! Talk to your supervisors, collaborators, other PhD students or researchers in your department, and even your friends and family, to test ideas and force yourself to put into words the abstract thoughts buzzing around in your head. This one took me a while to figure out, because having the confidence to discuss a half-formed idea with your supervisor or another researcher can seem intimidating. However, the benefit of such discussions can be enormous, and will make your analysis far richer and more interesting.

4. Trust yourself

A crucial thing I have learned is the importance of trusting your own ideas and having confidence in your judgements and intuitive responses to the data. Firstly, you will know the data better than anyone else so you know how it fits together as a whole in a way that others will not if they only look at the set of quotes that make the final selection for write-up. Secondly, part of the process of conducting qualitative research is to acknowledge and interrogate your own positionality in relation to the data as a researcher, rather than trying to pretend that is not the lens through which you are understanding it. Therefore, paying attention to your reactions and thoughts about the data, and trusting that they are worth interrogating, is important.

5. Enjoy it

And finally… enjoy the process! Sometimes the lack of a strict rulebook or set of steps can be intimidating, but my final and perhaps most important piece of advice would be to take advantage of the freedom that this kind of research allows for, think outside the box, and have fun with it.

Oonagh Coleman

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