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Thematic analysis part 3: six phases of reflexive thematic analysis

Posted on 31st March 2020 by

Tutorials and Fundamentals

In the last of a series of three blogs about Thematic analysis (TA), Dolly Sud describes the six phases of TA and provides details of further reading you can do on the topic. 

The first blog provided an introduction to TA and discussion of what a theme is,  and the second blog provided a description of the three schools of TA and some study design recommendations.

Six phases of Reflexive TA

The approach to TA that Braun & Clarke have developed [2] involves a six-phase process for doing analysis. These phases should be considered to be undertaken sequentially with each phase building on the one before. However, analysis should be considered recursive – in other words moving back and forth between each phase. These phases should not be considered as rules but tools that guide analysis that help thorough in-depth engagement with, and analysis of, data. A recent publication on this [2]  is a must read for those of you wishing to engage in this process.

  • Familiarisation phase

This involves reading and re-reading the data to become immersed and intimately familiar with its content. Braun & Clarke suggest doing this phase with a glass of wine!

  • Coding phase

This involves generating pithy labels (codes!) that identify important features of the data that might be relevant to answering the research question. The entire dataset should be coded then all the codes and all relevant data extracts collated together for later stages of analysis. In contrast, this phase requires a good cup of strong coffee!

  • Generating initial themes (see important note below)

This phase involves examining the codes and collated data to identify significant broader patterns of meaning (potential themes). Then data is collated relevant to each candidate theme. The researcher can then work with the data and review the viability of each candidate theme.

  • Reviewing themes

Candidate themes are checked against the dataset to determine that they tell a convincing story of the data, and one that answers the research question. Themes are typically refined, which sometimes involves them being split, combined, or discarded. In our TA approach, themes are defined as pattern of shared meaning underpinned by a central concept or idea

  • Defining and naming themes

This phase involves developing a detailed analysis of each theme, working out the scope and focus of each theme, determining the ‘story’ of each. Informative names for each theme are also decided.

  • Writing up

This final phase involves weaving together the analytic narrative and data extracts, and contextualising the analysis in relation to existing literature.

Take home message: This blog would not be complete without mentioning the following issue. Themes do not passively emerge from either data or coding; they are not ‘in’ the data waiting to be identified and retrieved by the researcher (like diamonds scattered in the sand, waiting to plucked-up by a lucky passer-by [9,10]). Themes are creative and interpretive stories about the data, produced at the intersection of the researcher’s theoretical assumptions, their analytic resources and skill, and the data themselves [2]. The researcher is active in the process of generating themes [4].










TA provides an accessible method for less experienced qualitative researchers. However, it is important to use the method with a degree of ‘theoretical knowingness’ [2] – an understanding of the philosophical basis of enquiry. This means, for instance, understanding the assumptions underpinning coding reliability or consensus coding practices.

Where can I find out more information?

Braun & Clarke have written extensively on the topic of TA. Their website [4] provides a very comprehensive and detailed overview of TA. It is an excellent resource and I would highly recommend (even instruct!) anyone interested in TA use this website to its full extent. Please see the full reference list at the end of this blog.

This series of blogs provides a snapshot of TA. Information that can be found on the website [4]  includes:

  • What is thematic analysis?
  • What is reflexive thematic analysis?
  • Different orientations in thematic analysis
  • Phases in doing reflexive thematic analysis
  • Answers to frequently asked questions
  • Resources for thematic analysis
  • Evaluating and reviewing (reflexive) thematic analysis research | a checklist for editors and reviewers

References (pdf)


Dolly Sud

Dolly is currently working towards a PhD with Aston University. She gained a BSc (Hons) in Pharmacy from De Montfort University in 1996. After completing a pre-registration post with Alder Hey Children's Hospital, Liverpool she registered as a Pharmacist in 1997 and worked extensively in both the NHS secondary care setting and primary care for various NHS trusts and GP practices. Dolly has gained relevant clinical qualifications including a Diploma in Pharmacy Practice (Derby University) and Diploma in Psychiatric Pharmacy (Aston University). Significant professional experience has been gained within the NHS hospital sector holding a variety of appointments including a year on secondment as Leicestershire Interface Pharmacist and leading on a five year national NHS England project improving quality of physical health care for those with Severe Mental illness. Currently, Dolly holds a post as a Senior Clinical Pharmacist for Leicestershire Partnership NHS Trust (which she has done for nearly 12 years) where, as well as clinical duties she is the Principal Investigator for several research studies. View more posts from Dolly

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No Comments on Thematic analysis part 3: six phases of reflexive thematic analysis

  • Dolly Sud

    Good question!

    This depends on your research question.

    I think you have several options
    Here are some suggestions

    (i) undertake TA on each separate type of data set and then compare and contrast to each other
    (ii) undertake TA on all of the data sets together as a whole
    (iiii) undertake TA on each separate type of data set and then together as a whole and compare and contrast.

    Comparing and contrasting will allow for triangulation

    I hope that helps


    10th May 2021 at 9:09 am
    Reply to Dolly
  • Kaie

    If I am collecting data from 4 different sources, should the data from each source br coded separately or should it be coded as one. So is the data set the data gathered from all the sources: interviews, focus group, questionnaire, etc or is the data set all the data collected from all the sources?

    4th May 2021 at 11:22 am
    Reply to Kaie
    • Emma Carter

      Hi Kaie. To ensure you see a notification, I wanted to let you know that Dolly has answered your question in a comment below.

      10th May 2021 at 9:54 am
      Reply to Emma

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