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.

Practical considerations, such as the length of the report and the available word count, influence the number of themes that can be effectively presented.

Aim for a manageable number of themes that are well-developed and distinct, each with a central organizing concept.

Consequently, fewer, well-developed themes are preferable to numerous, superficial ones.

Avoid presenting a fragmented analysis with too many underdeveloped themes or subthemes.

Trying to report too many themes within a limited word count can result in a fragmented and underdeveloped analysis, lacking depth and nuance.

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 to guide the reader’s understanding of the analysis, creating a clear and compelling narrative of the findings.

Theme names should be concise yet informative, capturing the theme’s core concept and giving the reader a clear sense of what the theme is about.

They should be evocative, intriguing the reader and encouraging them to delve deeper into the theme’s analysis.

Avoid using one-word theme names as they might not be descriptive enough. Avoid theme names that merely state a topic or domain, such as “Benefits of” or “Barriers to.”

These names suggest that the theme simply summarizes what participants said about a topic, rather than offering an interpretive analysis of shared meaning.

The theme definition should clearly articulate the theme’s central organizing concept, highlighting the unifying idea that connects the various facets of the theme.

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

A good test for a theme definition is whether it can stand alone as a meaningful statement, summarizing the theme’s key ideas and conveying its 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, progressing chronologically according to how the themes emerged in the data, or building a narrative that gradually reveals the data’s complexity.

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 (TA) results.

The aim is to weave together data and interpretation to create a convincing and insightful account of the data’s meaning.

The balance between data extracts and analytic narrative can be likened to a dance between “showing” and “telling.”

The extracts show the reader what is in the data, while the analytic narrative tells them what sense to make of it.

Achieving this balance ensures that the presentation of TA results is both convincing and insightful.

Aim for a balance between providing interpretations and offering evidence from the data. A useful starting point is a 50/50 split between analytic commentary and data extracts.

The analytic commentary should go beyond simply paraphrasing the data and instead offer original insights into the data’s meaning in relation to the 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.

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.