A common issue I see is quotes and interpretations that don\u2019t match up. When you\u2019re immersed in your data, it\u2019s easy to forget what your readers don\u2019t know. This means you might state interpretations as confident findings \u2013 but without the data to back it up, examiners and peer reviewers won\u2019t know what led you to those ideas.<\/p>\n While it\u2019s fine to communicate some contextual information without data \u2013 for example, you might explain that you\u2019d asked a certain question just before this data was given to you, or you might set the scene for what the participant is talking about \u2013 any interpretive findings must be backed up by evidence. Therefore, once you\u2019ve got a draft of your findings section, check all the claims you\u2019re making about the participants\u2019 experiences and ensure that the data that led you there is on the page.<\/p>\n <\/p>\n Another frequent problem in the theses I look at is that the quotes are uniformly too short. Of course, not every quote you include can or should be a full paragraph, but in IPA, if all your quotes are one-liners, this is a red flag to examiners.<\/p>\n <\/p>\n IPA researchers tend to explore complex, emotive topics about which little is currently known. Subjects like these cannot be adequately illustrated through short quotes alone.<\/p>\n <\/p>\n Additionally, IPA analysis is about digging deep into the data \u2013 exploring the messiness, the contradictions, the hidden meanings. Analysis like this is often more fruitful if it\u2019s been applied to longer sections of data.<\/p>\n <\/p>\n Building on the above, another common error is that even if great quotes have been presented, the analysis that accompanies them is too descriptive. IPA is a deeply linguistic form of qualitative analysis, so we should be taking the time to really dig into the language that participants are sharing with us and considering how it adds to our understanding of their lived experiences.<\/p>\n <\/p>\n Let\u2019s take this piece of made-up data, from a made-up study about junior doctors\u2019 mental health, as an example:<\/p>\n <\/p>\n Sarah: I was working 14 hour days, was battling sleep on my drive home every night. I was fighting to do well at work, but when I got home after every shift, I was destroyed. I felt completely laid to waste. Yet I had to plaster a smile on and do it again the next day.<\/em><\/p>\n <\/p>\n A researcher might state that Sarah\u2019s long working hours are harming her mental health. And that\u2019s correct, of course. But for an IPA study, this interpretation doesn\u2019t go far enough.<\/p>\n <\/p>\n Let\u2019s take a closer look at this data, which contains a few striking words and phrases \u2013 note that the participant was \u2018battling\u2019 and \u2018fighting\u2019, and yet despite this, she was \u2018destroyed\u2019 and \u2018laid to waste\u2019. These words conjure up the image of a bloody battle \u2013 a battle which Sarah is losing and which has left her ruined, a shell of her former self.<\/p>\n Her only defence against the war zone of work is to \u2018plaster on a smile\u2019, a phrase which shows that her cheerful work persona feels like a performance. Additionally, a sticking plaster feels completely inadequate for facing the battle of work she has described.<\/p>\n <\/p>\n Ok, so I made this data up and deliberately loaded it with language that could be quickly unpicked in this way, but it\u2019s amazing how often participants pepper their words with vivid imagery that, when analysed, can provide a resonant insight into their life worlds.<\/p>\n <\/p>\n Your data will almost certainly be full of interesting language, so don\u2019t miss the opportunity to analyse it deeply. Insights like this will advance your work greatly.<\/p>\n <\/p>\n Any student of IPA can recite the idiom that we\u2019re looking for divergence as well as convergence. However, it\u2019s rare for me to see divergence mentioned in findings sections. Seeking and discussing convergence is easier and \u2013 perhaps \u2013 feels safer. But divergence is important too. Why? Because human experience is messy and varied, and cannot be reduced to yes or no experiences, much as we might wish it could be!<\/p>\n <\/p>\n Divergence can take several forms. It could be that most participants report a certain emotional reaction to (say) a cancer treatment, and another reports the opposite emotional experience.<\/p>\n <\/p>\n If you were conducting a study about bereavement, all participants may share the experience of anger, but for some, this takes the form of shouting at friends and family, while others take up boxing or direct their anger at themselves.<\/p>\n <\/p>\n Or perhaps you\u2019re doing a study about waiting for a liver transplant. Participants may attach a similar meaning to an experience while expressing it in very different ways. For example, most participants might describe putting their lives on hold because of uncertainty about the future. However, another might respond to the same uncertainty by embracing new experiences and living more intensely in the present. Although their behaviours differ, both accounts may reflect a shared awareness that the future can no longer be assumed.<\/p>\n <\/p>\n All such examples of divergence should be included in your write-up, as they all demonstrate different aspects of the experiential elements you\u2019re presenting through your analysis.<\/p>\n <\/p>\n A final common error I see is a failure to structure subthemes and GETs so that they tell a coherent story. Instead, students sometimes just start with one participant, quoting all their relevant data, along with interpretations, before moving onto the next one.<\/p>\n <\/p>\n No matter how insightful your analysis, this is not a good way to write up IPA. It leads to repetition and means you will miss out on showing how one element of a subtheme leads to another, which builds on the first and paves the way for the next.<\/p>\n <\/p>\n As you create your PET and GET tables, think about how your findings can be structured to tell a story that best reflects the various experiences of the participants and has the potential to offer a resonant example of the meanings behind those experiences.<\/p>\n <\/p>\n You will almost certainly have to keep tinkering with this structure as you write up, but that\u2019s fine as this is part of IPA.<\/p>\n <\/p>\n <\/p>\n If you’re currently analysing IPA data and would value specialist input on theme development, interpretation, or analytic depth, my colleague Rachel Starr and I offer dedicated IPA mentoring via IPA Insights<\/a>. We support researchers at all stages of the IPA process, from early coding through to thesis write-up and viva preparation.<\/p>\n You can contact IPA Insights here<\/a>.<\/p>\n Alternatively, if you’re looking for proofreading or written feedback on your academic work, you can contact PGPR via this form.<\/p>\n![\"\"](\"http:\/\/postgradproofreader.co.uk\/blog\/wp-content\/uploads\/2026\/06\/ian-van-der-linde-npAdk5tjChs-unsplash-1024x683.jpg\")
Photo by Ian van der Linde<\/a> on Unsplash<\/a><\/figcaption><\/figure>\n\n
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Photo by Ravi Sharma<\/a> on Unsplash<\/a><\/figcaption><\/figure>\n\n
![\"\"](\"http:\/\/postgradproofreader.co.uk\/blog\/wp-content\/uploads\/2026\/06\/jaime-spaniol-L0N74GWsq8-unsplash-1024x576.jpg\")
Attending to the imagery that participants use in their language can help deepen analysis
Photo by Jaime Spaniol<\/a> on Unsplash<\/a><\/figcaption><\/figure>\n\n
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Life contains divergence. Seek it out in your analysis!
Photo by Eric Prouzet<\/a> on Unsplash<\/a><\/figcaption><\/figure>\n\n