Reading Time: 6 minutes


Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3, 77–101.
  • Braun and Clarke consider Thematic Analysis as a foundational method of qualitative analysis
  • Thematic analysis is flexible and accessible
    • Independent of theory
    • Compatible with both essentialist and constructionist paradigms
      • “Reports experiences, meaning and the reality of participants” as well as “examines the ways in which events, realities, meanings, experiences, and so on are the effects of a range of discourses operating within society” or somewhere in between (p. 81)
      • Focus on the implications of the context
  • Thematic analysis seeks to “provide a rich and detailed, yet complex, account of the data” (p.78)
  • Researchers must make their assumptions, epistemological/theoretical stance explicit when engaging and reporting thematic analysis
  • Key Terms
    • Data Corpus: all the data within a project
    • Data Set: data being used for particular analyses
      • May consist of any or all of the data
      • May be based on a particular interest, therefore including all data where that interest is present
    • Data Item: individual piece of data (e.g., individual interview)
    • Data extract: coded chunk of data (p. 79)
  • Goal is to identify, analyze and report patterns within the data
  • Organizes that data in detail, allowing the researcher to go further into analyzing and interpreting the data in relation to the researching topic.
  • Braun and Clarke (along with Taylor & Ussher, 2001) believe that the terminology of themes emerging or being discovered is too passive, which ignores the researcher as an active and influential part of the research.
  • There must be congruency between the theoretical framework and the methods that the researcher chooses to use in relation to what they want to know. These decisions must be acknowledged.
  • Areas to reflect on before and throughout the research
    • “What counts as a theme?” (p. 82)
      • A pattern or meaning that is important to the research question
      • Does not need to occur often within the data to be important
      • Rigid rules will limit the research
    • “A rich depiction of the data set, or a detailed account of one particular aspect” (p. 83)
      • Perhaps the aim is to give a broad overview of the themes and patterns within the whole data set. However some complexity and richness will be lost.
      • Perhaps you will focus on certain themes within the research, using a more semantic approach.
    • Inductive vs. deductive (i.e., theoretical thematic analysis)
      • Inductive (bottom-up)
        • Themes are based in the data themselves
        • Does not attempt to fit data to pre-existing themes or framework
      • Deductive (top-down)
        • Driven by preselected theoretical framework
        • Tends to provide less detail of the overall data but focuses on themes or aspects of the data
    • “Semantic or latent themes” (p. 84)
      • Semantic
        • Explicit level, nothing beyond what the participant has said
        • Progress from description to interpretation of what has been found in the data means or its implications
      • Latent
        • Interpretive
        • Explores underlying meaning, ideas, assumptions that shape the semantic content
        • Theme development involves interpretive work as theory is being built along with the themes
        • Based in constructionism (e.g., Burr, 1995)
    • Epistemological stance
      • Constructionist framework does not focus on the individual but rather the sociocultural context and structural conditions
    • Questions of Qualitative research
      • Research questions of the project
        • Broad (exploratory) vs. Narrow (may inform broad overarching question)
      • Questions used within the data collection process
      • “Questions that guide the coding and analysis of the data” (p. 85)
      • Search across the data set for patterns and themes
  • Doing thematic analysis
    •  Notes:
      • Writing occurs throughout as comparisons are made as you move between data collection, coding, and theorizing.
        • It is not a linear process
      • Prior reading of the literature depends on your approach, as a more theoretical (top-down) approach requires engagement with the literature first.
    • Familiarize yourself with the data
      • Transcribe, read (actively), reread, initial notes
      • Read whole data set prior to beginning coding
      • Transcribing is an interpretive act, key to analysis where ideas begin to form (Lapadat & Lindsay, 1999)
      • The level of detail in transcripts depends on the level of analysis that will be conducted. However, should always be verbatim, retaining the original meaning
    • Generate initial codes.
      • Looking for interesting patterns, features, ideas and coding systematically
      • Collate data within codes
      • Usually quite specific (themes are broader)
      • Theory or data driven?
      • Code for as many patterns/themes as possible
      • Code inclusively, to ensure the context of each extract is not lost
      • One extract can have multiple codes/themes
      • Acknowledge and note contradictory extracts
    • Searching for themes
      • Explore codes for themes and collate all data within a theme
      • How do they relate to one another?
      • It’s okay to have overlapped, or miscellaneous codes
        • Do not delete/remove codes at this stage
    • Reviewing the themes
      • Do the extracts work within the themes (Level 1)
      • Do the themes work within the entire data set (Level 2)
        • Reread entire data set to determine fit, but also code for extracts that may relate to themes that were found
      • Internal thematic homogeneity and external thematic heterogeneity (Patton, 1990)
      • Create a thematic map
    • Defining and naming themes
      • Ongoing refinement of themes, what are the specifics of each? What is the overall story?
      • Define and name the themes
        • Don’t just paraphrase extracts
        • What is interesting about them and why
        • What are they, and what are they not
    • Producing a report
      • Finding exemplars and contradictory extracts for each theme
      • Linking themes back to research questions and the extant literature
  • There is a 15-point checklist for a good thematic analysis on p. 96. This is certainly something I will be using to reflect on my own process
  • Advantages of Thematic analysis (p. 97).
    • Flexibility
    • Easy to learn and do
    • Accessible for new researchers
    • Findings tend to be accessible to a broad audience
    • Useful within participatory approaches
    • Provides a summary of the data and thick description
    • Highlights similarities and differences within the data set
    • Opportunity for unanticipated insights
    • Provides an opportunity for the social and psychological exploration of the data
    • Can inform policy
Clarke, V., & Braun, V. (2013). Teaching thematic Analysis: Overcoming challenges and developing strategies for effective learning. The Psychologist, 26(2), 120–123.
  • Thematic analysis is only recently recognized as a methodology on par with grounded theory or discourse analysis. However, Clarke & Braun suggest it is more of an “analytic method” than a methodology, as it is theoretically flexible.
  • Useful for analyzing data in relation to various types of research questions related to the phenomenon within particular contexts
  • Useful for primary and secondary sources
  • Can be used with large or small data sets
  • Can be used inductively or deductively
  • Researchers should conduct a reflexive exercise to explore
    • Their assumptions about the topic
    • Their values and life experiences
    • How might these influence how they collect, read, analyze and interpret the data
Vaismoradi, M., Turunen, H., & Bondas, T. (2013). Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nursing and Health Sciences, 15(3), 398–405.
  • Qualitative research tends to be person-centred and open-ended (Holloway & Todres, 2003)
  • Thematic analysis emphasizes the importance of context, whereas content analysis does not
  • Thematic analysis explores manifest and latent content together; Content analysis explores them separately.
  • A thematic map is drawn within thematic analysis
  • Peer checking is not necessarily required for thematic analysis
    • Due to the thinking that both may have been coached to find similar findings
    • A research diary is recommended to improve rigour in both approaches
  • Non-linear analysis process for both content and thematic analysis
  • Thematic analysis explores common themes/patterns throughout an interview and throughout a data set.
  • Both approaches can utilize an inductive or deductive approach. However, when using deductive approaches to confirm theory, there is flexibility in allowing for both a deductive and inductive approach (Sandelowski, 2010)
  • Thematic analysis systematically explores text, symbols, messages, information, media, and interactions within the context in which they occur (Loffe & Yardley, 2004).
  • Data collection and analysis often occur concurrently
  • Analytic terms (p. 402)
    Thematic Analysis Content Analysis
    Data Corpus Unit of Analysis
    Data Item Meaning Unit
    Data Extract Condensed Meaning Unit
    Code Code
    Theme Category/Theme
  • Unlike content analysis, the importance of themes within thematic analysis is not dependent on the number of times they occur within a data set, as this can be heavily reliant on the willingness of participants to talk about certain factors/experiences.
Aronson, J. (1995). A pragmatic view of thematic analysis. The Qualitative Report, 2(1), 1–3.
  • Steps to thematic analysis
    • Collect the data
    • Identify patterns of experiences
    • Identify all data that relate to the patterns
    • Combine and catalogue related patterns into sub-themes
    • Piece together themes to form a comprehensive story
    • Create a valid argument by exploring themes in relation to the extent literature
  • Member check with informants to verify patterns/themes


I believe that coming from an ecological or complexity epistemological stance (more to come on this), there may be an acknowledgment of both the individual factors and the sociocultural and structural conditions that play a role within a phenomenon. I think it will be an interesting experience in itself to approach thematic analysis from both a realist and constructionist viewpoint.

While the convention is to either choose a top-down or bottom-up, semantic or latent approach, I believe there is flexibility to explore both. Additionally, thematic analyses can be applied to all qualitative data I plan on collecting (policy documents, focus groups, interviews, website engagement).


According to Braun and Clarke (2006), thematic analysis fits well with my participatory approach, and potentially my ecological lens in which I will be analyzing the data. Additionally, I can have the analyses be driven by systems theory, but allow for some flexibility for unanticipated findings.

Things to explore further

  • Taylor & Ussher, 2001
  • Burr, 1995
  • Lapadat & Lindsay, 1999
  • Patton, 1990
  • Sandelowski, 2010
  • Loffe & Yardley, 2004



Karen · December 13, 2017 at 9:22 pm

We’re using theoretical thematic analysis for our circling project right now. So thanks for this!

You should also check out:

Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet trustworthiness criteria. International Journal of Qualitative Methods, 16, 1-13.

    Heather W. · December 13, 2017 at 9:24 pm

    Thank you for the article! Trustworthiness will certainly be covered in my Strengths and Limitations section, so this helps!

Leave a Reply

Avatar placeholder

Your email address will not be published. Required fields are marked *