Month: June 2023

This blog is written for BMERG by one of our committee members Dr Grace Pearson. Grace is Bristol Medical School graduate and a current Clinical Research Fellow in Population Health Sciences. Her research interests are in undergraduate medical education, specifically curriculum development and evaluation and geriatrics education.  

Grace shares her experience and tips after attending a workshop hosted by BMERG and the School of Policy Studies on ‘Reflexive Thematic Analysis’ from the expert Qualitative researcher, Professor Virginia Braun from the University of Auckland. 

At medical school, future doctors are taught to detect patterns in history and examination to reach a diagnosis. Moving into medical research, this scientific pattern-recognition continues in quantitative data analysis and interpretation. As a result, approaching mixed methods studies or pure qualitative research can be daunting for those of us in medical and other scientific fields – it certainly was for me.  

There are several core aspects of qualitative data analysis that I’ve never truly got to grips with, despite attending multiple training courses… Therefore, getting the chance to learn directly from a world-leading expert was an opportunity not to be missed.  

I went into this workshop wanting to learn how to analyse or ‘code’ my data and develop my themes. I came away with a much wider appreciation of the importance of exploring context, embracing subjectivity, finding latent meaning, and conceptualising what Prof Braun called ‘galaxy’ themes rather than ‘buckets’. Let me explain a bit more.  

When we first look at qualitative data during analysis, certain things can jump out at us as topics. We may think these may start to look like our themes, but if we are not careful, they can end up looking like our original questions and, because everything we connect to a particular topic ends up together ‘in a bucket’ so to speak, may have lots of conflicting ideas within them.  

Conversely, true themes are more like a galaxy with a clear core, a ‘central organizing concept’ holding together all the ideas which although may be different, just like stars and planets are in a galaxy, they remain inherently linked. 

Here are my 5 key learning points from Professor Braun’s fantastic reflexive thematic analysis (RTA) workshop, which I hope might help others to approach their own qualitative data analysis in a reassuringly robust way:  

• Scientifically Descriptive vs Artfully Interpretive analysis: Descriptive describes and summarises the data in an ‘experiential’ or ‘realist’ manner. Interpretive tells a story, locating the data within a wider context and presents an argument in a ‘critical’ or ‘constructionist’ way. Approaches to thematic analysis (TA), range from ‘scientifically descriptive’ deductive methods such as coding reliability, to ‘artfully interpretive’ inductive methods such as reflexive TA. 

• Small q vs Big Q: Descriptive analysis suits ‘Small q’ research questions that seek to explore or describe peoples’ experiences, understandings, or perceptions – their ‘individual reality’. Interpretive analysis suits ‘Big Q’ research questions that seek to explore the ‘wider context’, for instance influencing factors, representations, and constructions.  

• Context and Subjectivity: Analysis occurs in the intersecting space between the researcher(s), the data, and the research question. Subjectivity is present in all 3, as all are influenced by sociocultural, disciplinary, and scholarly context – as a result, analysis is situated in context, which must be clearly communicated.  

• Coding: codes are ‘units of analytic interest’, the smallest unit of analysis capturing a single analytic idea or facet. These can be semantic (explicit) or latent (implicit) – descriptive analysis generally uses more semantic codes, whilst interpretive analysis uses both. Codes are not ontologically ‘real’, they exist only for the researcher(s) to foster engagement with the data – they need to capture the meaning of the data along with the researchers’ interpretation, orientated towards answering the research question.   

• Themes: a theme is a construction that captures shared or repeated meaning in the data around a ‘central organising concept’. Themes are conceptual, therefore semantic-level data may seem disparate, but it is unified by latent meaning representing diverse manifestations of the core concept (like a galaxy).  Themes sit in the analytic narrative – they must tell a story of how the data is meaningful and answers the research question.   

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Some examples of recommended resources for getting started using reflexive thematic analysis 

• https://www.thematicanalysis.net/  
• Braun, V, & Clarke, V. (2013). Successful qualitative research: A practical guide for beginners. SAGE. 
• Braun, V, & Clarke, V. (2022). Thematic analysis: A practical guide. SAGE. 
• Braun, V, & Clarke, V. (2021). Can I use TA? Should I use TA? Should I not use TA? Comparing reflexive thematic analysis and other pattern-based qualitative analytic approaches. Counselling and Psychotherapy Research, 21(1), 37-47. https://doi.org/https://doi.org/10.1002/capr.12360 
• Braun, V, & Clarke, V. (2021). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative Research in Psychology, 18(3), 328-352. https://doi.org/10.1080/14780887.2020.1769238 

More about Professor Braun https://profiles.auckland.ac.nz/v-braun

Virginia “Ginny” Braun is a New Zealand psychology academic specialising critical psychology of health and gender. She is internationally recognised for expertise in qualitative methodologies, and particularly the now widely utilised method of (reflexive) thematic analysis – developed in collaboration with Victoria Clarke (UWE).