What counts as data in qualitative research?

In research articles and reports qualitative research is often reduced to text, which is in turn analysed by human and increasingly machine readers. However, original qualitative analyses depend on a far wider range of observations and impressions. Sezai Doruk Soyata considers how qualitative research becomes data and whether researchers can do more to represent these influences in their work. 

This article is shared from the LSE Impact blog the aarticle gives the views and opinions of the authors and does not reflect the views and opinions of the Impact of Social Science blog (the blog), nor of the London School of Economics and Political Science or Dementia Researcher. Shared under the Creative Commons Attribution 3.0 Unported (CC BY 3.0) the original publication can be found at https://blogs.lse.ac.uk/impactofsocialsciences/2026/08/10/what-counts-as-data-in-qualitative-research/

When people hear the word “data”, they often think of numbers, survey results, interview transcripts or documents that can be stored, searched and quoted. These are all important forms of evidence. But in qualitative research, data can also include silences and absences, hesitations, spatial arrangements, informal conversations, fieldnotes and sensitive or difficult topics that participants may approach indirectly.

This does not mean that anything can count as data. A silence during an interview is not automatically meaningful, and an absence in a field site is not automatically a finding. But when such details are systematically recorded, compared and interpreted, they can help us understand social life in ways that a transcript alone may not capture.

This broader understanding of data matters because qualitative research is often asked to justify what makes its evidence rigorous. Its value does not lie only in collecting what people say, but in interpreting how speech, silence and setting come together. Yet, as artificial intelligence tools come to shape how researchers collect, process and analyse textual material, this point becomes harder to ignore.

Interviews are more than answers

Interviews are often treated as a central source of qualitative data because they generate recordings, transcripts and quotations. But an interview is not simply a container for answers. It is also a social encounter.

Participants may pause, hesitate to answer, answer indirectly, laugh, lower their voice, change the subject or speak in unusually general terms. These moments are not distractions from the “real” data. They can be part of the data, especially when they appear repeatedly across interviews or connect to wider patterns in the field.

A participant’s reluctance to speak about a topic, for example, may point to embarrassment, fear, moral discomfort or the limits of what can be said in that relationship. The point is not to turn such moments into dramatic interpretations, but to ask careful questions: Why and when did they occur? Were they repeated? How did they relate to what was said around them?

This is where fieldnotes become important. Many of these details are easy to lose when qualitative data is treated as transcripts alone.

Fieldnotes turn context into evidence

Fieldnotes are sometimes misunderstood as private impressions or background material. In practice, they are an important tool through which qualitative researchers transform experience into evidence. As classic work on writing ethnographic fieldnotes shows, note-taking is not a mechanical act, but a craft through which researchers record what happened, who was present, how people interacted, what the space looked like and what they noticed after leaving the scene.

This matters because the most useful notes are not always the most obvious ones. They may describe a joke made after the recorder is turned off, the atmosphere of a room, a shift in tone, or the difference between what people say in an interview and what appears ordinary in practice. Such notes do not simply mirror reality; they reflect what the researcher noticed and recorded. But when they are detailed, consistent and revisited during analysis, they allow researchers to compare situations and trace patterns over time.

This became clear in my own ethnographic research in Turkey, where I studied how devout upper-middle-class Muslims live and express their religiosity, including how they navigate leisure spaces such as restaurants and cafés. Interviews were important, but the spaces themselves also mattered. In some venues, prayer rooms existed but were hidden in peripheral corridors. In others, alcohol was absent, but the menu offered colourful mocktails that looked much like alcoholic cocktails. Décor, music, menu design, what was absent, and the visibility or invisibility of religious markers all helped communicate what kind of piety, class position and lifestyle the venue made possible.

These details showed how religion could be made present without always being made explicit. Religious atmosphere was not produced only through direct labels, statements or visible symbols; it also emerged through repeated patterns across different sites. When compared across venues, interviews and fieldnotes, such details became more than impressions; they became part of the evidence.

Why this matters in the age of AI

As AI tools become more common in research, qualitative data is increasingly discussed in terms of transcription, coding, summaries and large collections of text. These uses can make parts of qualitative research faster and more manageable. But they also raise a central question: what is lost when qualitative data is treated mainly as text?

A transcript can tell us what was said, but it rarely captures the full situation in which something was said. The meaning of an interview answer may depend on details that are not easily captured as text, but become clearer through observation, fieldnotes and comparison across cases.

This does not make AI irrelevant to qualitative research. AI tools are most useful when they are situated within a broader understanding of how qualitative evidence is produced. They may help with transcription, organisation and coding, but they cannot, on their own, reliably determine how contextual details become analytically meaningful. That judgement still depends on methodological transparency and the researcher’s knowledge of the field.

From observations to evidence

Qualitative data can be a transcript, a quotation or a document. But it can also be a pause, an absence, a spatial arrangement, an informal conversation or a fieldnote written after an ordinary encounter.

The challenge is not simply to notice these materials, but to make their movement into evidence more visible. Researchers can do this by keeping clearer audit trails, recording how fieldnotes were produced and used, explaining why certain absences or silences became analytically significant, and showing how contextual observations were compared with interviews, documents or other forms of material. In some cases, open research practices and carefully anonymised data sharing may also help. But in sensitive qualitative research, transparency does not always mean making everything public. It can also mean being clearer about what cannot be shared, why not, and how claims were developed despite those limits.

This is the central point in asking what counts as data in qualitative research. The answer is not that everything does, nor that only transcripts, quotations and documents do. Rather, qualitative research broadens what can count as data, while also requiring researchers to show more clearly how such material becomes evidence.

About the author

Dr Sezai Doruk Soyata is a sociologist of religion and a lecturer in the Department of Sociology at Koç University. His research focuses on secularisation, class, religious change, qualitative methods and contemporary Turkey. His work examines how religion is lived, negotiated and transformed in everyday social life, with particular attention to ethnographic methods and everyday religious practice.