A worked example of contextualising and using reflexive thematic analysis in nursing research
Intended for healthcare professionals
Evidence and practice    

A worked example of contextualising and using reflexive thematic analysis in nursing research

Emma Rowland Lecturer in emotional geographies of health, Florence Nightingale Faculty of Nursing, Midwifery & Palliative Care, King’s College London, London, England
Anna Conolly Lecturer, health sciences, University of Surrey, Guildford, England

Why you should read this article:
  • To support the use of reflexive thematic analysis (RTA) in analysing qualitative systematic reviews and empirical data.

  • To review your understanding of RTA to analyse nursing research within the context of wider methodological and methods considerations.

  • To explore practical examples of RTA in nursing research.

Background A researcher must consider their research question within their world view before selecting a technique appropriate for analysing their data. This will affect their choices of methodology and methods for collecting and analysing data. Reflexive thematic analysis (RTA) has become a go-to technique for qualitative nurse researchers. However, the justifications for using it and its application in the context of a wider approach are under-discussed.

Aim To rationalise the use of RTA within a wider philosophical-methodological-methods-analysis approach and provide nurse researchers with practical guidance about how to apply it to qualitative data.

Discussion This article conceptually grounds the seminal work of Braun and Clarke (2006) and provides a process for rigorously and systematically analysing qualitative data. Researchers undertaking qualitative research must use a rigorous philosophical-methodological-method-analysis approach. Before selecting a technique appropriate for analysing their data, they must consider their research question within their own world view. This has implications for their choice of methodology and consequently the data collection methods and analysis techniques they use. Researchers should be mindful of RTA’s conceptual roots when applying it.

Conclusion Transparent and rigorous data analysis leads to credible findings, supports evidence-based practice and contributes to the growing body of nursing research. Within the context of the wider philosophical-methodological-methods-analysis approach, RTA produces high-quality, credible findings when applied well.

Implications for practice This article can guide nursing students and novice researchers in choosing and applying RTA to their research.

Nurse Researcher. doi: 10.7748/nr.2024.e1924

Peer review

This article has been subject to external double-blind peer review and checked for plagiarism using automated software

Correspondence

emma.rowland@kcl.ac.uk

Conflict of interest

None declared

Rowland E, Conolly A (2024) A worked example of contextualising and using reflexive thematic analysis in nursing research. Nurse Researcher. doi: 10.7748/nr.2024.e1924

Published online: 29 August 2024

Introduction

Qualitative research has made a valuable contribution to evidence-based practice. It has helped researchers to understand the experiences, needs, views and opinions of patients, their family members and healthcare professionals with respect to various topics, including the impact that ill-health has on patients’ lives as well as the lives of their family members and/or caregivers. This has enhanced the delivery of care and the provision of services.

Unfortunately, despite the prevalence and value of qualitative studies, normative assumptions persist that quantitative research is superior (Shaw et al 2022). Positivist epistemologists continue to argue that qualitative research lacks rigour, transparency and credibility, and that understanding people’s experiences is inferior to quantitative research (Shaw et al 2022).

We acknowledge that qualitative researchers need to demonstrate rigour and credibility by presenting a transparent, fully considered philosophical-methodological-methods-analysis approach (Howell 2013). The literature rarely discusses in an accessible way the development of a meticulous research approach, including its decision-making processes. We will therefore articulate in this article the wider philosophical-methodological-methods-analysis context of qualitative research. We will demonstrate how this shapes decisions concerning the choice of data analysis technique – specifically, reflexive thematic analysis (RTA).

We will also provide conceptual and practical guidance as well as worked examples showing how to apply RTA in nursing and health research. The data used in this article come from an ethnographic study of healthcare professionals working in hospitals (Rowland 2021a) and ambulance crews (Rowland 2021b) in the UK. The aim of the study was to understand how healthcare professionals use emotional labour and emotion work to manage their emotions and care for patients (Rowland 2015). The research was underpinned by a socially and culturally constructed philosophy. Data were collected through ethnographic observation and semi-structured interviews with a wide range of healthcare professionals, ambulance crews and middle managers.

Key points

  • An appropriate qualitative data analysis technique is chosen in the wider context of the researcher’s epistemological and ontological world view and the methodology and methods adopted to answer a research question

  • It is important to take a reflexive approach to think about how preconceived ideas and assumptions can affect data collection, interpretation and analysis

  • Engaging with Braun and Clarke (2006) will bring rigour and innovation to qualitative research

The philosophical-methodological-methods-analysis approach

Philosophy: ontology and epistemology

It is impossible to consider the development of rigorous qualitative research without being cognisant of philosophical approaches. Epistemological and ontological positions include conceptions of subjects and subjectivities, and understandings of how knowledge and reality are constructed and produced (Mauthner and Doucet 2003).

A ‘positivist’ (quantitative) ontological perspective understands reality as being external to the social world, objective and measurable, with the researcher neutral (Dingwall and Staniland 2020). In contrast, ‘critical’, ‘constructionist’ or ‘interpretivist’ qualitative ontological perspectives understand reality as being constructed through the interaction of social actors, with the world subjective and relational and the researcher present within it (Dingwall and Staniland 2020).

Epistemology reflects a researcher’s understanding of what constitutes knowledge and how they gain it – through either experience (a posteriori knowledge) or investigation (a priori knowledge) (Howell 2013). If researchers are aware of their epistemological perspectives, they can consider how to conduct their research to derive knowledge. Researchers who believe reality is constructed through the interaction of social actors will acquire knowledge using interviews and other qualitative methods that enable them to understand people’s experiences.

Ontology and epistemology therefore cannot be separated and must be scrutinised to align optimally with the appropriate methodological approach (St Pierre 2021).

Methodology

It is important to be mindful of the distinction between a methodology and a method: the former is a set of diverse principles, procedures and frameworks while the latter is a tool for collecting data (Silverman 2021). Nurses and other allied health professionals use phenomenology, ethnography, grounded theory, case study and other methodologies as underlying epistemological philosophies for their research (Silverman 2021).

Phenomenology

Phenomenological nursing research seeks to understand health, illness and the delivery of care by understanding people’s experiences of them. There are two approaches (Dibley et al 2020):

  • Descriptive phenomenology, which seeks to elucidate phenomena or experiences through description.

  • Hermeneutic phenomenology, which seeks to reveal deeper meanings in and understanding of people’s experiences through reflective thinking that draws on the researcher’s prior knowledge and experience.

Ethnography

Ethnography is both a methodology and a method of collecting data. It focuses on creating shared understandings of a group of participants’ cultural or organisational experiences, norms and values. These understandings, gathered from participants’ own lives and experiences over a prolonged period of time, enable deeper and more meaningful collective understandings of health, illness and delivery of care (Smith et al 2023).

Grounded theory

Grounded theory (GT) is both a methodology and a technique for analysing data. It seeks to develop inductive theoretical explanations for phenomena by gathering insights from people who have experienced them (Turner and Astin 2021).

GT methodology operates across a continuum from ‘classic Glaserian’ through ‘Straussian’ to ‘constructionist’ GT:

Case study

Case study methodology seeks to understand the complexity of a ‘case’ (patient/illness) through real-life multiple perspectives (Simons 2014).

Method

Methodological choice has implications for the choice of method. Qualitative methods include interviews, focus groups and (ethnographic) observations. Researchers must choose methods for their research that align with their methodologies’ philosophies. For example, phenomenology seeks people’s experiences, so one-to-one interviews are the most appropriate method to use; a focus group would be inappropriate as it elicits the experiences and views of the whole group.

Analysis

Researchers must choose a technique to analyse their data that aligns with their study’s methodology and method. For example, if they are conducting a study that has a hermeneutic phenomenological methodology, they would analyse their data using hermeneutic phenomenological analysis (Dibley et al 2020). Likewise, a study using GT methodology, might use grounded theory analysis (GTA). Other data analysis techniques might be less prescriptive in terms of their allegiance to a specific methodology such as thematic analysis (TA) (Braun and Clarke 2021a).

Data saturation

Data saturation is frequently hailed as the gold standard for determining sample size or when to cease collecting or analysing data. This is because it assumed that no new information or ideas are being elicited when saturation is achieved. However, ‘data saturation’ as a term is problematic (Braun and Clarke 2021b) – the interpretation of data could be unending, as other researchers could have different experiences and knowledge so might interpret the same data in different ways.

Qualitative researchers are therefore turning instead to ‘information power’ to determine when to stop analysing data in their projects (Malterud et al 2016). This takes into consideration a project’s aim, sample size and theoretical underpinning; the quality of the data; and whether the technique used to analyse data is appropriate for their approach’s philosophy, methodology and method.

Thematic analysis

Thematic analysis is a systematic and rigorous technique for analysing data that uses a process of coding and theme development to interpret data. It is compatible with a range of methodologies, as it is flexible and not tied to a particular philosophy, methodology or method (Braun and Clarke 2021a).

Joffe (2011), Byrne (2022) and many other papers have contributed to our knowledge, understanding and application of thematic analysis. However, Braun and Clarke have been the most prolific writers in this field. Their seminal paper (Braun and Clarke 2006) introduced their version of thematic analysis. They then refined their thinking, attending to the misunderstandings of their technique and elucidating it. They also clarified how it differs from other forms of thematic analysis, including its greater focus on ‘reflexivity’ – the critical attention researchers give to their thinking about how their knowledge and understandings have been derived and how a priori and a posteriori learning have shaped them (Whitaker and Atkinson 2019). This led to their conceptualisation of RTA (Braun and Clarke 2019, 2021a).

RTA

RTA asks researchers to actively think about their subjectivity and positionality and to ask themselves questions about how they derived their analysis and interpretation. It encourages them to enquire whether they have done enough to analyse the data fully, or whether they have taken their analysis and interpretation too far away from the raw data. This enables them to think more introspectively about how their knowledge and experiences may be influencing data collection and analysis, as well as vice versa – how the process of collecting and analysing data might be influencing their views.

RTA is iterative, rather than linear. It requires researchers to move continuously backwards and forwards through their data and Braun and Clarke’s (2006) original six-step process: familiarisation, coding, theme development, reviewing themes, confirming themes and dissemination (see Figure 1).

Figure 1.

The six steps of RTA

nr.2024.e1924_0001.jpg

There is considerable criticism of RTA, with some authors arguing there is little clear, accessible guidance about how to undertake RTA as well as a dearth of worked examples (Nowell et al 2017, Xu and Zammit 2020). However, clear guidance (Braun and Clarke 2021a) and worked examples (Braun and Clarke 2012, Byrne 2022) are available to support novice researchers.

For example, Braun and Clarke (2022) suggested researchers record reflections in ‘reflexive diaries’. This is what the researcher (ER) did in the ethnographic study described in this article, which includes some extracts from her diary.

The following extract shows how ER’s reflexive engagement with Freud’s psychoanalytical theories led her to realise an obstetrician’s unconscious feelings were present in the data. This enabled her to unlock latent meaning, giving her interpretation greater depth:

‘Is there a double meaning here? Who is “terribly anxious and upset”? I am supposed to understand that it is just the parents of this still-born child that are feeling this way?… I am coming to realise that the doctor is unconsciously disclosing his own emotions…’

Being in the field for 18 months permitted ER to develop new experiential understandings of ambulance crews’ use of language, which enabled profound insights to become visible. ER wrote this first extract from her reflexive diary during the first few days of data collection:

‘I am simultaneously horrified and confused by the way the crews described their “job”… “She kept coming back – she just wouldn’t die!” I am stunned, shocked, offended.’

This second extract comes from 16 months into data collection:

‘I find myself unfazed by the language and dark humour. I have, over time, learned that it is a way of coping… It is not meant to be offensive… I now understand: “She just wouldn’t die.” It was a plea for the patient to die peacefully. It was kindness.’

This article will now provide further guidance on the practical application of RTA.

The process of RTA

1. Familiarisation

Familiarisation begins with data collection and continues through the development of data transcripts. Researchers then immerse themselves in their data by reading and re-reading the whole data corpus. To prevent selection bias, no undue attention is paid to one data point more than the others.

Familiarisation is not a passive endeavour. Instead, researchers actively and critically engage with the data by circling, underlining and highlighting – as demonstrated by this extract from the ethnographic study’s reflexive diary.

‘Ethnographic field notes were translated into transcripts and semi-structured interviews transcripts returning from the transcriber were read and re-read. Although a temptation existed to focus on certain events that had become “sticky in my mind” or interview narratives that struck an emotional chord, I methodically read through the data highlighting, circling and underlining text and jotting notes in the margins. On occasions, I struggle to remain on-task, [which prevents] me from giving equal attention to all the data…’

2. Coding

The circling, underlining and highlighting of familiarisation give way to more formal ‘coding’ approaches in the second step. Coding involves applying to a section of data a word or a label that encapsulates and summarises the data’s content. The resulting ‘codes’ help the researcher to look at the granular level of the data to reveal patterns and differences that the researcher can then explore.

Inductive and deductive codes

Codes can be:

  • Inductive – developed organically from the data.

  • Deductive – derived from prior knowledge through personal experience, research or theory.

The researcher can simultaneously apply inductive and deductive codes to the same body of text because theory, prior knowledge and experiences inform our engagement with the data from the beginning (Shaw et al 2022).

Line-by-line and ‘lumper’ coding

The researcher can apply codes to texts line by line or to larger chunks (‘lumper’ coding) (Saldaña 2021). Line-by-line coding enables the researcher to explore the data more intensely, giving more latent and nuanced understandings of the data; lumper coding tends to create more semantic and superficial interpretations of the data.

Both techniques are often applied simultaneously. In the ethnographic study, ER used lumper coding to mark out where a story or event started and finished; she used line-by-line coding to focus on the detail.

Managing the data

The researcher can code data by hand or using software such as ATLAS.ti or NVivo. ATLAS.ti was used in the ethnographic study to store, manage and code the data. However, coding initially began by hand, with ER using highlighter pens and Post-it notes to initiate her immersion in the data in an active, tangible way (see Figure 2).

Figure 2.

Example of data coded by hand

nr.2024.e1924_0002.jpg

The researcher’s choice of coding method will depend on the volume of the data they collect, their chosen methodology, the researcher’s technical expertise and whether they are coding independently or as part of a team. However, the use of analysis software has been criticised on two grounds (Mauthner and Doucet 2003):

  • It can create a distance between the researcher and their data.

  • Novice researchers may believe the software will analyse the data for them and add an air of scientific objectivity to a fundamentally subjective, interpretative process.

Code lists

It is useful to compile an alphabetical list of the codes created during the coding process (see Box 1). Doing so can prevent you from developing new codes when suitable codes are already available.

Box 1.

Example code list

  • Affect

  • Bad news

  • Bodily proximity

  • Deathscape

  • Decision making

  • Defence mechanism

  • Detached concern

  • Disconnected care

  • Disruptive emotions

  • Distil nursing

  • DNR

  • Emotional management

  • Emotion work

  • Emotional attachment

  • Emotional contagion

  • Emotional detachment

  • Emotional memories

  • Liminality

  • Organisational rules

  • Physical distance

  • Professional performance

  • Rapport

  • Responsibility

  • Spatiality

  • Task-orientated behaviours

  • Taskscape

  • Temporality

  • Touch

Codebooks

It can be particularly helpful when coding in teams to also create ‘codebooks’ to define the meanings of the codes (see Figure 3). However, Braun and Clarke’s (2021a) arguments against their use included:

Figure 3.

An example codebook

nr.2024.e1924_0003.jpg
  • Codebooks are positivist in their orientation as they rely on predefined, deductive codes.

  • Researchers do not need to agree codes, as this can produce superficial themes and stifle creativity.

However, in our experience, a research team can develop a codebook inductively, growing it organically from the code list; the team also does not need a codebook to reach a consensus. In addition, we have found value in displaying different researchers’ interpretations, as it can create avenues for more nuanced, profound and rich themes to emerge.

3. Theme development

When a researcher is satisfied that they have achieved information power, they can begin the process of theme development. This is a messy, time-consuming challenge, yet ultimately is rewarding.

To start developing themes, the researcher brings together codes with similar shared meanings to create subthemes and then an over-arching theme. The same codes might fit within several themes or subthemes.

However, other codes may not fit neatly into any group. These outliers should not be forced into groups and should be discarded if they cannot contribute to the overall narrative (Braun and Clarke 2012).

There is no definitive number of themes that should be developed. However, dissertations, published papers and reports typically present three to six (Braun and Clarke 2012).

It is important when developing themes and subthemes that researchers build a coherent narrative that answers the research question in a meaningful way. Themes should be distinctive, with each contributing something important to the overall story, enabling researchers to tell a coherent and comprehensive narrative.

The theme’s size or the number of codes it comprises is not an indicator of its importance or value; rather, it is the meaning and the analytical interpretation of the themes that is important. For example, in the ethnographic study, the theme of ‘ghost stories and the haunted mind’ was derived from a small proportion of the interview data. However, it was conceptually important in explaining how ambulance crew members managed their emotions on the road as they moved across the city, ‘haunted’ by memories of traumatic past jobs.

Visual techniques can help to explore different layers of the data. Collaborative software can also assist in organising data by enabling researchers to place virtual Post-it notes in piles or to draw thematic maps (see Figure 4).

Figure 4.

Code mapping

nr.2024.e1924_0004.jpg

4. Reviewing themes

Once provisional themes have been developed, it is vital that researchers confirm the themes represent the data and that the narratives developed are coherent, do not overlap and contribute to the overall story.

The researcher can begin this process by writing a summary of the theme and subthemes. If this is challenging, or subthemes are being incorporated into other themes, the themes require revising. This is an iterative process, in which researchers may need to re-examine how they have grouped codes.

Once a concise and bounded summary of the themes and their subthemes has been written, check the sizes of the themes. Interrogate disproportionately smaller themes, as these may be more appropriately represented as subthemes.

Once satisfied that themes and subthemes are internally consistent and appropriately supported by the raw data, researchers can confirm them by defining and naming them. You should also order the themes to create a coherent and logical narrative.

5. Confirming themes

In this penultimate phase, researchers will move beyond describing the data to analysing by bringing into dialogue with theory and the wider literature to think about what it means and why it is important. This is often a challenge but a spreadsheet can provide invaluable assistance in analysing and interpreting the data to produce theoretically informed interpretations.

An excel spreadsheet can be invaluable in assisting the analysis and protestation of the data. In the first column, codes can be placed, in the second column, the title of the subtheme speaking to the main theme is presented.

Example quotations supporting the subtheme should go in the third column, while the fourth column should include theory, policies and wider literature that align with or contradict the data.

The researcher can then sit and ponder the data in the spreadsheet, thinking critically about their meanings and interpretation. Finally, place the resulting analytical claims in the fifth column of the spreadsheet.

Table 1 shows an example spreadsheet from the ethnographic study that explores the theme of ‘emotional detachment’.

Table 1.

Moving from description to analysis

nr.2024.e1924_0001_tb1.jpg

6. Dissemination

The final stage is disseminating your findings as a report, dissertation or publication. A challenge researchers may encounter here is how to choose which data to publish, particularly when the word count available is limited, such as in a peer-reviewed publication.

Selecting data

There are several issues researchers must consider when selecting data to represent themes:

  • Data volume: It is often difficult to choose quotes that best reflect or represent analytic claims, given the large amounts of data normally collected in qualitative research.

  • Representation: Demonstrating the existence of themes across the broad data set requires selections from a demographically broad range of participants and perspectives.

  • Contrary views: Data that do not fit neatly into your themes must be fully explained. Researchers can do this by presenting them as exemplifying unusual or alternative stances that stand them apart from the rest of the data (Conolly et al 2022).

Word count

Researchers may have to reflect on the length of quotes and remove unnecessary details to make them as concise as possible, if there is a limit on how much can be written. However, this may remove context, which can have ethical implications.

For example, Rowland (2021b) demonstrated the performative nature of emotion management by presenting a paramedic’s emotional slippage. The slippage occurred after an intoxicated patient had subjected the paramedic to several hours of physical and verbal abuse. The limited space available to contextualise the events preceding the slippage meant there were concerns about how to present the data without harming the paramedic.

As ER reflected in her diary:

‘I am conflicted… This extract illuminates the theme narrative in the most instructive way… I witnessed the professional performance falter, the mask slip for the briefest of seconds, exposing the…human behind the act. An emotional slippage, followed by a rapid recovery, the… professional performance resumed. BUT what are the implications for my participant?… How will [he] feel on seeing this in print? How can I portray this incident without it being detrimental to his professionalism? What if his line manager sees this? What will others think of him?’

It is therefore imperative to choose extracts carefully. Researchers may also find it beneficial to ‘weave’ participants’ words into the text to ensure that their voices are heard and represented within the analytical dialogue.

Conclusion

Researchers who wish to enhance patient care and services through good, evidence-based practice and to contribute to a growing body of health research must adopt a rigorous philosophical-methodology-methods-analysis approach.

Our aim in this article has been to:

  • Provide a rationale for using RTA.

  • Help novice researchers to understand how to apply it to their qualitative systematic review or empirical study, so they can analyse their data transparently and rigorously.

  • Show how to situate its use within the wider context of their epistemological and ontological standpoints through careful alignment with their chosen methodology and methods.

  • Provide practical guidance in applying RTA to their qualitative data. Pertinent illustrations structured by Braun and Clarke’s six-step RTA process illuminated the practicalities and challenges of using RTA in practice.

We highlighted that being reflective as well as reflexive is necessary throughout the research process to ensure that qualitative data are credible, valid, transparent and rigorous.

We concluded by arguing that it is essential that novice researchers fully engage with Braun and Clarke’s original material to enhance their knowledge and understanding of RTA and bring rigour and innovation to qualitative research.

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