How to Present Thematic Analysis Results

When presenting thematic analysis (TA) results, the goal is to tell a compelling story about the data that convinces the reader of the analysis’ merit and validity.

1. Report an appropriate number of themes:

The number of themes will depend on the study’s scope and the data set’s size.

Each theme should tell a story and not be merely a collection of observations about a topic.

Quality over quantity is paramount in reflexive TA. For a typical 8,000-word report, 2 to 6 themes, including subthemes, is a reasonable guideline, though the report’s length and available word count will still shape the final number.

Aim for a manageable set of well-developed, distinct themes, each built around its own central organizing concept. Fewer, fully developed themes beat many superficial ones: cramming in too many within a limited word count produces a thin, fragmented analysis.

2. Organize And structure the themes:

The primary goal is to present findings that are informative, persuasive, and insightful.

Thematic analysis findings are typically structured in hierarchical levels: overarching themes, themes, and subthemes.

This hierarchical structure helps organize the analysis into a coherent narrative that reveals the relationships between different aspects of the data.

To achieve this, researchers must carefully consider how to structure their themes to tell a compelling story about their data.

The themes should work together to tell a story that answers the research question.

When developing this structure, researchers should:

1. Establish clear relationships between different thematic levels.
2. Ensure each level contributes to the overall narrative.
3. Create logical connections between related themes.
4. Maintain consistent organization throughout the presentation.

By prioritizing clarity, coherence, researchers can help their audience grasp both the individual themes and their interconnections, leading to a deeper understanding of the analysis as a whole.

Provide clear definitions and names for themes:

The theme names and definitions should work together, guiding the reader through a clear, compelling narrative of the findings.

Theme names should be concise yet informative, capturing the theme’s core concept.

They should also be evocative.

Avoid one-word names, and avoid names that merely state a topic, such as “Benefits of” or “Barriers to.”

Braun and Clarke call this a domain-summary theme: a topic label rather than a shared-meaning theme that names a genuine interpretation.

The distinction matters.

The theme definition should articulate its central organizing concept, the unifying idea connecting its different facets.

This should be a pattern of shared meaning, not simply a shared topic.

A good test: can the definition stand alone as a meaningful statement that captures the theme’s complexity?

Determine a logical order:

Organize themes into a clear hierarchy that reflects the relationships between them. Aim for a maximum of three theme levels to avoid fragmentation.

• Overarching Themes: Broad, overarching concepts that provide an umbrella for several related themes. Overarching themes are not typically reported in detail.
• Themes: The core concepts identified in the data, capturing essential ideas and patterns of shared meaning.
• Subthemes: Used judiciously to highlight important facets or nuances within a larger theme, adding depth and complexity to the analysis. Using subthemes should add interpretatively to the story or hold together the other themes discussed.
  • Too many subthemes can lead to a fragmented, thin analysis.

Decide on a sequence that presents the themes in a meaningful and engaging way.

This might involve starting with the most prominent or foundational theme, or building a narrative that gradually reveals the data’s complexity.

Themes are not simply “found” in that order, though.

As Braun and Clarke (2019) explain, researchers actively generate themes; order them by when you constructed each one, not when it “emerged.”

The order should be transparent to the reader, and it may be helpful to explicitly state the rationale behind the chosen sequence.

Connect and integrate:

Highlight the connections and relationships between themes, creating a sense of coherence and flow in the presentation.

Explicitly discuss how themes relate to each other, whether they build upon, contrast with, or intersect.

A smooth transition between themes helps the reader follow the analytic narrative.

The presentation of themes should not be a mere list of isolated concepts.

Instead, it should be a cohesive and compelling narrative that draws the reader in, offering insights into the data’s meaning and answering the research question.

Use visual aids:

Visual aids, such as thematic maps or tables, can enhance clarity and understanding.

A thematic map visually depicts the relationships between themes and subthemes.

Tables can present an overview of the themes, their definitions, and illustrative quotes.

3. Balance analytic narrative with data extracts:

Striking a balance between analytic narrative and data extracts is crucial when presenting thematic analysis results.

Think of it as showing versus telling.

The extracts show the reader what is in the data; the narrative tells them what it means.

Aim for a 50/50 split between analytic commentary and data extracts.

Go beyond paraphrasing: the commentary should offer original insight into what the data means for your research question.

Focus on interpretation:

The analytic narrative should go beyond merely describing or paraphrasing the data extracts.

It should offer original interpretations and insights into the data’s meaning.

This interpretative approach moves beyond the surface level of the data and offers a deeper understanding of the phenomenon.

Data as evidence:

Data extracts should be carefully chosen to illustrate and support the analytic claims.

They provide evidence for the interpretations offered in the narrative.

The data extract then anchors the interpretation in the participants’ own words, adding credibility and depth to the analysis.

Ratio as a guideline:

While there’s no strict rule, a 50/50 split between analytic narrative and data extracts is a useful starting point. This ensures sufficient space for both interpretation and evidence.

Presentation of themes:

The balance also applies to the presentation of individual themes. Rather than listing several short extracts with minimal commentary, aim for a more engaging approach.

1. Begin each theme by introducing its core concept.
2. Present relevant data extracts clearly.
3. Follow with insightful commentary explaining their significance.
4. Connect the interpretation back to your research question.

State your approach and report contradictory data:

State clearly which version of TA you used, reflexive, codebook or coding-reliability, since a reader needs to know which standard to judge your write-up against.

Reflexive TA in particular expects transparency about your own analytic decisions, not just your findings.

Resist smoothing over cases that do not fit.

If most participants in a home-working study resent losing the office/home boundary, still report the one who says they prefer it.

A contradictory voice strengthens the account.

A 2026 study of how Autistic adults define quality of life shows this in practice, citing LaPoint et al. (2026).

The research team documented which members were Autistic or not, an explicit statement of positionality that let readers judge the interpretation for themselves.

A worked example:

Picture a study of remote working.

The theme is not labelled “working from home”: that name would just describe the topic, not the meaning.

Instead it is named “My kitchen table is now my office”: the collapse of the work-home boundary, a phrase that carries the theme’s actual meaning.

The name itself makes a claim.

The extract “I answer emails at 10 p.m. because the laptop is right there” shows the pattern in the participant’s own words.

The commentary then explains why it matters: the laptop’s physical presence keeps work psychologically “on,” eroding the boundary a commute used to provide.

4. Choose vivid And compelling data extracts:

Choosing vivid and compelling data extracts is crucial for effectively presenting the results of thematic analysis.

When choosing data extracts, always keep the reader in mind.

The goal is to select extracts that will resonate with the reader and help them understand the significance of the themes being presented.

A well-chosen extract can bring the analysis to life, making it more engaging, persuasive, and impactful.

Illustrative and evocative:

The selected extracts should be rich and evocative, offering clear and compelling examples of the analytic points being made.

When choosing between several potential extracts, prioritize those that are particularly vivid and engaging, capturing the essence of the theme in a way that resonates with the reader.

Diversity and breadth:

Draw on extracts from across the data set to showcase the theme’s pervasiveness and demonstrate that it is not limited to just one or two participants.

Avoid relying too heavily on one articulate participant, even if their contributions are particularly insightful.

Instead, aim for a balance that reflects the diversity of perspectives within the data set.

Concise and focused:

Choose extracts that are concise and to the point, effectively illustrating the analytic claim without unnecessary complexity.

Edit out extraneous material that doesn’t directly contribute to the point being made.

Ensure that the extract is easily identifiable as an example of the theme and that its relevance is clear to the reader.

Contextualization and clarity:

If needed, provide contextual information for the extract to ensure clarity and understanding.

This might involve briefly explaining the participant’s background or the situation in which the data was collected.

However, avoid excessive contextualization that detracts from the extract’s impact.

References

Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597.

LaPoint, S. C., Kiernan, B., Heinly, J., Hector, B. L., Benevides, T. W., & Maddox, B. (2026). Quality of life defined by Autistic people: A thematic analysis. Autism in Adulthood, 8(4), 664–676. https://doi.org/10.1089/aut.2024.0180