You’re Doing Thematic Analysis Wrong! 5 Common Mistakes That Are Ruining Your Research
Thematic analysis is one of the most widely used qualitative research methods. It provides a structured yet flexible way to identify patterns of meaning in data, making it particularly appealing for researchers across disciplines. However, many researchers misapply Braun and Clarke’s method’s of thematic analysis, often reducing it to a mere exercise in categorisation.
When applied correctly, thematic analysis is a powerful tool that can uncover deep insights about human experiences, social structures, and cultural narratives. When applied incorrectly, it results in shallow findings that lack analytical depth. If your thematic analysis feels like a sorting exercise rather than a meaningful exploration of data, you might be making one of these common mistakes.
Mistake 1: Treating Thematic Analysis Like a Simple Word Search
One of the most frequent misconceptions about thematic analysis is that a theme is simply something that appears frequently in the data. This leads researchers to count how often specific words or phrases occur, assuming that those with the highest frequency must represent key themes.
This approach fundamentally misunderstands the nature of thematic analysis. The presence of a word does not necessarily indicate its significance within the data. A theme is not just a collection of similar words—it is an overarching pattern of meaning that tells us something important about the topic being studied.
For example, if multiple interview participants mention the word "stress," that does not automatically mean "stress" is a theme. Instead, the researcher must examine how participants talk about stress, what contexts it appears in, and what underlying experiences or social factors are shaping these discussions.
To conduct robust thematic analysis, researchers should move beyond surface-level word recognition and consider the deeper, implicit meanings behind the data. Thematic analysis allows for both semantic (explicit) and latent (underlying) themes. A semantic approach stays close to what the participants say, while a latent approach seeks to interpret the broader implications of their words. The most insightful research often involves a combination of both.
Mistake 2: Skipping Reflexivity and Assuming Objectivity
Another critical mistake in thematic analysis is failing to acknowledge the researcher’s own influence on the process. Thematic analysis is not a neutral, objective exercise—it is shaped by the researcher’s background, assumptions, and interpretative lens.
Every researcher brings their own perspectives and biases to the coding and theme development process. If these biases are not actively considered, they can lead to selective interpretation or confirmation bias, where themes reflect the researcher’s expectations rather than the actual data.
To counteract this, Braun and Clarke stress the importance of reflexivity—the practice of critically reflecting on one’s role in the research process. This involves asking questions such as:
How do my own beliefs and experiences shape the way I interpret this data?
Am I privileging certain voices or perspectives over others?
Have I considered alternative interpretations of the data before settling on a theme?
Keeping a reflexivity journal throughout the research process can help researchers remain aware of their assumptions. Additionally, discussing coding decisions with colleagues or using inter-rater reliability techniques can provide fresh perspectives and reduce individual biases.
Mistake 3: Confusing Themes with Categories
Many researchers make the mistake of presenting categories instead of true themes. Categories simply group together similar responses, whereas themes offer deeper insights into what those responses mean.
For example, imagine a study exploring students' experiences of online learning. If a researcher codes responses into categories such as "technical difficulties," "lack of motivation," and "flexibility," they have only created broad groupings of data. These are descriptive labels rather than analytical insights.
A theme, on the other hand, tells a story about the data. Instead of "lack of motivation," a theme might be framed as "Struggling to stay engaged: The emotional toll of online learning." This captures not just what is happening, but why it matters.
To avoid this mistake, researchers should ask:
Does my theme provide an interpretation, or is it simply a label?
Does it help answer my research question, or is it just a summary of responses?
Can I explain why this theme is meaningful beyond just its frequency?
True thematic analysis goes beyond classification to theorising meaning, ensuring that findings contribute to a deeper understanding of the subject matter.
Mistake 4: Stopping Too Soon or Overloading with Too Many Themes
Determining when to stop coding and finalise themes is another common challenge. Some researchers stop too early, settling on initial themes without thoroughly reviewing them. Others take the opposite approach and generate an excessive number of themes, leading to a fragmented and unfocused analysis.
A strong thematic analysis balances depth and clarity. If too few themes are identified, the analysis risks being too general and missing important nuances. If too many themes are created, the research becomes cluttered, making it difficult to draw meaningful conclusions.
A good rule of thumb is to ensure that each theme is:
Distinct (not overlapping too much with other themes)
Coherent (all data within the theme logically fits together)
Rich in meaning (not just descriptive, but offering insight)
Braun and Clarke’s six-phase approach to thematic analysis helps researchers avoid stopping too soon or over-coding. Their iterative process—moving back and forth between the data, refining themes, and checking them against the research question—ensures that themes are well-developed and supported by evidence.
Mistake 5: Failing to Justify Theme Selection
Once themes have been identified, they need to be justified. A common mistake is presenting themes without explaining why they were chosen and how they were developed. Without justification, the analysis appears arbitrary and unconvincing.
To strengthen the credibility of thematic analysis, researchers should:
Clearly define each theme and explain how it was derived from the data.
Provide direct examples from the dataset to illustrate the theme.
Discuss how themes relate to existing research and theoretical frameworks.
Address alternative interpretations and explain why certain themes were prioritised over others.
By making the process of theme development transparent, researchers ensure that their findings are rigorous, credible, and meaningful.
Thematic analysis is a powerful and flexible method for qualitative research, but it is not a simple process of categorisation. Researchers must move beyond surface-level coding, engage in reflexivity, distinguish between categories and themes, ensure a balanced number of themes, and provide clear justifications for their findings.
By avoiding these five common mistakes, researchers can conduct thematic analysis that is insightful, methodologically sound, and impactful. Braun and Clarke’s framework provides a structured approach, but it requires thoughtful application to truly uncover meaningful patterns in qualitative data.
For researchers looking to strengthen their thematic analysis, the key takeaway is this: themes should tell a story, not just organise data.
