Beyond Frequency Counts: A New Framework for Understanding Qualitative Data

Prevalence, Resonance, and Functional Importance

Imagine two qualitative themes emerging from a research study. The first is mentioned by nearly three-quarters of participants. The second appears in only a quarter of responses. Which one is more important?

Traditional qualitative analysis has a straightforward answer: the first. After all, if more people mention an idea, it must matter more.

But what if the second qualitative theme quietly connects many of the study's other important ideas? What if it helps explain why seemingly separate concerns occur together? And what if participants do not mention it often, yet it plays a critical role in how the broader qualitative system fits together?

These questions point to a simple conclusion: frequency alone is not enough.

We propose a framework built around three complementary dimensions of qualitative importance: Prevalence, Resonance, and Functional Importance. Together, they provide a more complete understanding of what matters in qualitative data.

Prevalence: What Do People Talk About?

Prevalence is the traditional foundation of qualitative analysis. It measures how frequently a qualitative theme appears across responses and how broadly it is distributed across participants.

A highly prevalent qualitative theme tells us that many people are discussing the same issue. This remains essential evidence because it establishes the descriptive foundation of a study and identifies issues that are broadly experienced.

However, Prevalence cannot tell us whether participants consider a qualitative theme especially meaningful or whether it plays an important role within the broader qualitative system.

Resonance: What Do Participants Recognize?

One of the unique capabilities of Remesh is Ask Opinion, which allows participants to react to and endorse the qualitative responses of others during the research process.

Rather than relying solely on what each participant says, researchers can also observe which ideas the broader group recognizes as meaningful, representative, or important. Resonance reflects how strongly a qualitative theme is recognized, affirmed, or identified with by other participants.

Sometimes only a handful of participants articulate an idea, yet many others immediately recognize it as reflecting their own experience. Ask Opinion makes this measurable. Rather than inferring which ideas resonate most strongly, researchers can directly observe collective endorsement as participants respond to one another's qualitative contributions.

Functional Importance: What Role Does the Qualitative Theme Play?

Functional Importance reflects the role a qualitative theme plays within the broader qualitative system. Unlike Prevalence, which describes how widespread a qualitative theme is, or Resonance, which describes how strongly it resonates with participants, Functional Importance describes what the qualitative theme does within the qualitative system.

Some qualitative themes organize many other ideas. Others connect otherwise separate topics. Some reinforce a dominant perspective within a theme community, while others bridge multiple communities of thought. These are system-level functions within the qualitative data, not claims about the organization or social system being studied.

Where NAQD Comes From and How It Works

Network Analysis of Qualitative Data (NAQD) draws on two established traditions: qualitative coding, which identifies recurring concepts in participant language, and network science, which examines patterns of relationships among entities. In a NAQD analysis, qualitative themes become nodes in a network. Relationships are created when qualitative themes appear together within the same response, participant, or other analytically meaningful unit. The result is a qualitative network that represents how coded ideas are related across the dataset.

This changes the unit of interpretation. Conventional thematic analysis can show which qualitative themes are present and how frequently they occur. NAQD also shows which qualitative themes cluster together, which repeatedly travel together, which bridge otherwise separate areas of discussion, and which sit near the center or edge of the qualitative evidence. Measures such as connectivity, bridging, embeddedness, and community role provide complementary evidence about the function a qualitative theme performs. No single network statistic defines Functional Importance; the construct is inferred from the pattern of evidence across several structural indicators and interpreted alongside the original participant language.

At Remesh, we are using NAQD as a computational mixed-methods layer built on top of participant responses and qualitative coding. Ask Opinion shows which responses resonate across the group; NAQD shows how the qualitative themes within those responses relate to one another. We examine theme communities, hubs, bridges, embeddedness, and the stability of those patterns, then interpret them alongside frequency, respondent prevalence, endorsement, subgroup differences, and the original comments. NAQD is not intended to replace qualitative judgment with an algorithm. It provides another evidence base for identifying connections, contradictions, and potentially important qualitative themes that conventional frequency tables may miss. Within this framework, NAQD supplies the structural evidence used to assess Functional Importance.

Looking Beyond the Loudest Voices

The value of the framework became clear when we compared two qualitative studies: one exploring employee worries about the future and another examining public concerns about national confidence.

In the employee study, concerns about company performance, job security, staffing, and workload were highly prevalent, highly resonant, and functionally important. One participant wrote, "The uncertainty within the market. We have a number of contracts up for renewal...and if the outcome isn't good then there may be redundancy." Another described "economic uncertainty in general and how an economic slowdown could affect revenue and employment levels." These comments illustrate how economic conditions, company performance, and job security were linked within the qualitative data. The qualitative network showed that these themes connected many of the other major concerns expressed throughout the study. Multiple forms of evidence pointed toward the same conclusion: they were Core Organizers within the qualitative system.

The national confidence study revealed a different pattern. One participant identified "tax issues and housing issues and crime and theft and corruption of higher leaders," while another described "a downtrending economy and an immigration crisis." These comments demonstrate how several concerns could be bundled within a single response. Across the full qualitative network, themes such as wealth inequality and healthcare played disproportionately important functional roles because they connected discussions of affordability, housing, opportunity, institutional trust, and economic security, even when they were not the most frequent or most highly endorsed themes.

We describe these as Hidden Organizers: qualitative themes with modest Prevalence and Resonance but high Functional Importance. Without examining relationships across the qualitative data, these themes would have been easier to overlook.

A More Complete View of Qualitative Importance

The most important qualitative theme is not always the one mentioned most often. Nor is it necessarily the one participants endorse most strongly. Sometimes it is the qualitative theme quietly helping the broader qualitative system fit together.

By examining Prevalence, Resonance, and Functional Importance together, researchers can move beyond simple frequency counts toward a richer understanding of collective experience. Remesh makes this possible by combining what participants say, how other participants respond through Ask Opinion, and how qualitative themes relate to one another through NAQD.

While the framework is introduced here using Remesh, the underlying theory concerns qualitative evidence itself. Remesh's unique combination of participant-generated responses, Ask Opinion, and Network Analysis of Qualitative Data makes it possible to operationalize all three dimensions of qualitative importance within a single research experience.

Every qualitative theme tells three stories: how widely it is expressed, how deeply it resonates, and what role it plays within the broader qualitative system. Understanding all three provides a more complete picture of qualitative insight than frequency alone ever could.

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