How Many Themes is Too Many? Let’s Talk Thematic Saturation
One of the biggest challenges in thematic analysis is knowing when to stop coding. How many themes are enough? How do you avoid drowning in too much detail or, conversely, over-simplifying your findings?
Manual thematic analysis requires an iterative, time-consuming process of identifying, refining, and justifying themes. This means researchers often struggle with thematic saturation—the point where additional coding no longer adds meaningful insights. Too many themes? The analysis becomes fragmented. Too few? Critical nuances get lost.
But what if you never had to second-guess your themes again?
What is Thematic Saturation?
Thematic saturation refers to the point where no new meaningful patterns are emerging from your data. It means that further coding and analysis are unlikely to add new insights—your themes are well-developed, clearly defined, and fully capture the essence of your dataset.
However, many researchers either:
Stop too early, missing important nuances and alternative interpretations.
Keep going indefinitely, producing an overwhelming number of themes that dilute the main findings.
So, how do you know when you’ve reached true thematic saturation rather than just running out of patience?
How Many Themes Should You Have?
There’s no magic number of themes that guarantees a rigorous analysis. The "right" number depends on factors like:
The depth and complexity of your dataset – A small study may have 3-5 rich themes, while a larger study may need more to capture key variations.
Your research question – A broad, exploratory study may require more themes, while a focused study may require fewer but more detailed ones.
The richness of the themes themselves – Strong themes explain patterns rather than simply describing topics. If you find yourself with too many thin, fragmented themes, you may need to consolidate them.
The Struggles of Manual Coding in Thematic Analysis
Braun and Clarke’s thematic analysis framework relies on careful, manual coding. This means:
Reading and re-reading transcripts or texts multiple times.
Assigning codes to segments of text, trying to balance rigour with flexibility.
Grouping codes into themes - a process that is inherently subjective.
Continuously refining, merging, or splitting themes, often second-guessing choices.
Manual coding requires a deep immersion in the data, but it is also inherently subjective. The process begins with multiple readings of the text, followed by coding decisions that rely on a researcher’s judgment. These codes are then grouped into themes, refined, merged, or split, depending on how well they seem to represent the patterns in the data.
At its best, this process is rigorous and reflective. At its worst, it is slow, inconsistent, and very vulnerable to bias. Two researchers analysing the same dataset may come up with vastly different thematic structures, simply because thematic analysis is shaped by individual perspectives. Some themes may never emerge because they don’t fit the researcher’s expectations, while others may be overemphasised because they appear interesting at first glance.
Traditional thematic analysis requires constant questioning. Have enough themes been identified to capture the depth of the data? Have too many themes been created, leading to unnecessary fragmentation? Are themes distinct and meaningful, or are they overlapping in ways that weaken the analysis? The search for thematic saturation is often an exhausting and uncertain process.
Now, imagine removing all of this uncertainty while achieving high-quality, unbiased thematic insights.
Leximancer’s Automated Coding and Hierarchical Theme Discovery
Leximancer takes a different approach. Instead of relying on manual coding, it automatically detects themes and concepts based on how words appear in relation to each other across the dataset. This removes subjectivity and speeds up the analysis while still ensuring that themes emerge directly from the data rather than being imposed by the researcher.
Thematic analysis in Leximancer is powered by hierarchical clustering, a technique that organises themes into structured layers. Instead of producing a rigid, flat set of themes, Leximancer recognises that themes exist at different levels of abstraction, much like the way human cognition organises meaning. Themes are not standalone entities but part of a broader network of ideas, and Leximancer visually represents these relationships in a way that manual coding cannot achieve.
How the Themes Slider Eliminates the Struggle for Thematic Saturation
Leximancer’s Theme Slider allows researchers to adjust the level at which they view the theme hierarchy. At a high level, broad themes become visible, capturing the major patterns in the data. At a more detailed level, smaller sub-themes emerge, allowing researchers to explore nuances within the dataset.
This ability to shift perspectives on the data fundamentally changes how thematic analysis is conducted. Rather than agonising over whether themes have been refined enough or whether additional coding is needed, researchers can instantly explore different levels of granularity with a simple adjustment. There is no need to collapse themes prematurely or force artificial distinctions between them. Thematic saturation is no longer a fixed endpoint but a flexible process that adapts to the needs of the research.
Why Leximancer is a Game-Changer for Thematic Analysis
Leximancer removes the uncertainty that comes with traditional thematic analysis. Instead of spending weeks refining codes and second-guessing thematic structures, researchers can move directly to interpretation, knowing that themes have been identified in a way that is both rigorous and data-driven. Automated coding reduces bias, ensures consistency, and makes the entire process significantly faster without sacrificing depth.
The ability to dynamically adjust the view of themes means that researchers never have to worry about over-coding or under-coding again. The software reveals insights that might have been overlooked in a manual process while allowing researchers to maintain control over the analysis. By eliminating the tedious aspects of thematic discovery, Leximancer makes it easier to focus on what truly matters - the meaning behind the data.
Instead of struggling with thematic saturation, researchers using Leximancer gain the ability to explore their data from multiple perspectives, ensuring they capture the most meaningful patterns without unnecessary complexity. The next time you find yourself wondering whether you have identified the right number of themes, consider whether manual coding is holding you back.
