Are You Confusing Common Words with Real Themes? - Why Thematic Analysis Is Not Just Counting
It’s a common assumption in qualitative research: if a word appears often, it must be important.
At first glance, this makes sense. If participants keep using the same words, surely those words represent key themes? But the problem is, word frequency alone doesn’t tell you what’s actually meaningful. Just because a word appears often doesn’t mean it carries real analytical weight, and relying on frequency counts alone can lead to shallow, misleading thematic analysis.
So how do you move beyond word counting to uncover genuine themes in your qualitative data?
Why Word Frequency Is a Misleading Shortcut
Word frequency analysis is tempting because it feels objective. Numbers seem to offer clarity, if a word appears 50 times, it must be more important than one that appears 5 times, right?
Not necessarily.
Consider this: If you analysed interviews about online learning and saw the word “Zoom” appearing frequently, does that mean “Zoom” is a theme? No. It’s just a tool participants mention. The meaning behind why they’re talking about Zoom - frustration with video calls, exhaustion, lack of engagement - is where the real themes lie.
This is the fundamental flaw of word frequency analysis: It focuses on repetition, not meaning.
What Word Frequency Can’t Tell You
Context Matters – The same word can be used in different ways. “Stress” could refer to work pressure, technology issues, or personal struggles. Looking at raw counts alone won’t tell you which interpretation is most relevant.
Meaning Lies in Relationships – Thematic analysis isn’t about isolated words but about how ideas connect. A theme isn’t a single word; it’s a pattern of meaning across a dataset.
Overused Words Can Skew Results – Some words are common in all discussions (e.g., “think,” “feel,” “experience”). Their frequency doesn’t mean they hold thematic importance.
If you rely too much on counting words, you risk mistaking common language for real insight which will weaken your research.
How Thematic Analysis Moves Beyond Word Counting
Thematic analysis is about identifying patterns of meaning, not just the words on a page. This means researchers must:
Go beyond individual words and examine whole phrases – A word on its own tells you nothing, but how it’s used across multiple responses reveals deeper insights.
Look for conceptual relationships – Instead of just seeing how often “stress” appears, ask: What words or phrases does “stress” consistently appear alongside? That’s where you find meaning.
Code based on context, not frequency – A theme should represent an idea or experience, not simply a commonly used word.
This requires a qualitative mindset, one that prioritises interpretation and depth over raw numerical counts. But with large datasets, manually identifying these patterns can be overwhelming.
How Leximancer Approaches Thematic Analysis Differently
Leximancer does more than word frequency, it analyses the relationships between concepts. Instead of assuming that frequently used words hold the most meaning, it automatically builds a thesaurus based on the actual language patterns in your dataset. This means Leximancer doesn’t just show you which words appear together - it reveals which concepts are forming, how they’re connected, and how strongly they relate to one another.
How the Leximancer Thesaurus Works
Leximancer’s Thesaurus Learning System operates iteratively, refining and generalising seed words to form distinct concepts. This means instead of seeing isolated words, you get a ranked list of thesaurus terms that define and describe each concept, along with relevancy weightings that show how strongly each word contributes to that concept.
For example, if participants frequently mention "stress", the software doesn’t just count that word, it looks at related words like "burnout," "workload," "pressure," and "exhaustion", understanding that these belong to the same conceptual cluster rather than treating them as separate items. This process happens automatically, with Leximancer iterating through the text to refine its concept detection without requiring manual input from the researcher.
Why This Matters for Thematic Analysis
Conceptual Meaning Over Raw Counts – Instead of just highlighting that stress appears 100 times, Leximancer shows you how stress is linked to other ideas, like deadlines, performance, and mental health.
No Need for Predefined Categories – The system learns directly from the dataset, meaning themes emerge naturally from the data itself, not from researcher assumptions or predefined frameworks.
Relevancy Weightings Add Depth – Unlike simple word frequency lists, Leximancer assigns relevancy weightings to each word, showing which terms are most strongly defining a concept.
By focusing on concept relationships rather than surface-level word counts, Leximancer helps researchers uncover real thematic patterns, making qualitative analysis more robust, unbiased, and insightful.
Focus on Meaning, Not Just Numbers
Word frequency analysis might seem like a quick way to identify themes, but it misses the bigger picture. Thematic analysis is about understanding ideas, experiences, and perspectives, not just counting words.
If you want to uncover real patterns in your data, you need to move beyond what’s most frequently mentioned and look at how concepts connect and interact.
Because at the end of the day, research isn’t about the words themselves, it’s about the stories they tell.
