Understanding Conceptual Relationships in Qualitative Data
Is counting words enough to understand what your data really says?
Word frequency analysis has long been a go-to method in early qualitative research. It offers speed, objectivity, and a sense of structure. But when it comes to interpreting complex human meaning, frequency alone can be misleading. The real insight lies not in what was said the most but in which ideas were, how they are related and what they represent.
What is conceptual analysis in qualitative research?
Conceptual analysis focuses on the meanings behind words, rather than the words themselves. Instead of isolating single terms and counting their appearances, this approach examines the concepts those words convey by grouping related terms together and analysing how these concepts interact within a dataset.
For example, in a dataset about workplace wellbeing, the words “overwhelmed,” “burnout,” and “too much” may all refer to the same underlying concept of stress. Conceptual analysis allows us to code and connect these fragments of meaning, offering a much more nuanced understanding than any single term could provide. You could then look to see where stress appears with other themes within the dataset, to gain insight into the causes and consequences of stress that are being reported.
How do you code qualitative data by meaning?
Conceptual coding involves identifying and grouping words or phrases that represent the same idea, even if they differ semantically. This may include synonyms, metaphors, colloquialisms, or even context-dependent expressions.
This process builds what researchers often refer to as a semantic or conceptual thesaurus - a structured mapping of how language is used to represent ideas in a specific dataset (Saldaña, 2016). These thesauri are not static; they evolve through iterative analysis and grow richer as new data is added and refined.
Conceptual codes are powerful because they:
Capture implicit meaning and latent themes
Integrate linguistic variability across participant groups
Allow researchers to detect meaning that transcends vocabulary
This can also be particularly useful in multilingual or multicultural research, where literal translation may obscure shared meanings.
Why are conceptual relationships important?
Human communication is rarely linear. We speak in associations, metaphors, and webs of meaning. Conceptual relationships capture these connections, showing how ideas relate rather than just what was said.
For instance:
In narratives about “success,” you may find connections to “risk,” “sacrifice,” and “family”. Even if those words never occur in the same sentence.
In healthcare interviews, a theme like “trust” might link to “information,” “transparency,” and “support” without appearing in a single paragraph.
These relationships are rarely possible to detect using frequency analysis. Conceptual mapping, on the other hand, reveals them.
What does the literature say about conceptual analysis?
Qualitative scholars have long recognised the limitations of frequency-based coding. Miles, Huberman, and Saldaña (2014) note that conceptual coding allows for abstraction …an essential step in developing theory from qualitative data. Braun and Clarke (2006) similarly argue that thematic analysis must go beyond surface content to explore the semantic and latent meanings in texts.
Furthermore, Bazeley (2013) warns against “code-counting” without considering the richness of contextual relationships: “Quantifying qualitative data can blind us to the meaning that makes it worth studying.”
Conceptual coding addresses this by focusing on patterns of meaning, enabling researchers to:
Develop deeper explanatory models
Ground findings in both context and connection
Improve analytic reproducibility and rigour
How do conceptual maps emerge from coded data?
Once words are grouped by their conceptual meaning, they can be plotted into a conceptual map. This is a visual model that shows the proximity and strength of relationships between ideas.
In this map:
Concepts act as nodes
Thematic groupings emerge as clusters
Stronger or more frequent co-occurrences form denser links
These maps are especially useful in large datasets, where manual interpretation becomes impractical. They allow researchers to see the structure of meaning as it appears in natural discourse.
This method supports grounded theory development, content analysis, and inductive approaches, all while reducing researcher bias and increasing clarity.
Why is this approach essential for academic research?
If the goal of qualitative research is to understand human experience, conceptual analysis offers an indispensable lens. It allows scholars to:
Extract meaning across diverse linguistic expressions
Recognise abstract or emergent themes
Avoid over-reliance on preconceptions or dominant narratives
In research involving policy, education, healthcare, or social justice (where meaning is often implicit or contested), conceptual relationships illuminate what participants may be unwilling or unable to state directly.
As thematic analyst Virginia Braun notes, “Themes are not merely descriptions of patterns in the data, but constructs that capture something important about the data in relation to the research question” (Braun & Clarke, 2006).
So should you still count words?
Word frequency can be an interesting first step, but it should never be the last. Frequency tells you what’s common; conceptual analysis tells you what’s meaningful.
So the next time you're faced with a wall of transcripts or survey responses, ask:
What ideas are being expressed?
How do these concepts relate?
Am I analysing words - or meaning?
When you move beyond word counts, your data starts to speak in dimensions that matter.
References:
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
Bazeley, P. (2013). Qualitative Data Analysis: Practical Strategies. SAGE Publications.
Miles, M. B., Huberman, A. M., & Saldaña, J. (2014). Qualitative Data Analysis: A Methods Sourcebook (3rd ed.). SAGE Publications.
Saldaña, J. (2016). The Coding Manual for Qualitative Researchers (3rd ed.). SAGE Publications.
