What Counts as Data in Qualitative Research

What counts as data in qualitative research is a practical question for ethnographers, UX researchers, and evaluation teams who rely on interviews, observations and documents. Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. The London School of Economics blog post "What counts as data in qualitative research? " (LSE Impact, published 10 August 2026) argues that qualitative evidence extends beyond transcripts to include pauses, silences, spatial arrangements and fieldnotes. The primary payoff for researchers is learning concrete steps to record, audit and analyse non-textual evidence so those details become defensible claims rather than disappearing in transcription.

Key Takeaways

Qualitative data extends beyond transcripts to include pauses, silences, spatial arrangements and fieldnotes, according to the LSE Impact article (published 10 August 2026), LSE Impact.

LSE Impact published the essay on 10 August 2026 and lists at least six non-textual evidence types to consider, including silence, hesitation and spatial arrangement.
A 2008 study on spatial arrangements is cited in the LSE Impact piece, and the piece also references research published in 2022 about informal conversations, showing the argument is grounded in multi-decade literature.
The article recommends practices such as keeping audit trails, documenting how fieldnotes were produced, and explaining why certain absences became analytically significant.

What happened and why it matters

Answer: The LSE Impact essay (10 August 2026) states that qualitative research is being compressed into text even as evidence materials are far wider, and that narrowing risks losing interpretive context.

According to the LSE Impact article (10 August 2026), researchers often reduce interviews to transcripts and quotations, but that reduction omits interactional details such as pauses, lowered voices and off-record comments.

According to the LSE Impact article (10 August 2026), these interactional details can be systematically recorded and compared so they function as evidence rather than anecdote.

The LSE Impact author, Sezai Doruk Soyata, writes that "A transcript can tell us what was said, but it rarely captures the full situation in which something was said, " which explains why fieldnotes and spatial observation matter.

The LSE Impact piece adds that "A silence during an interview is not automatically meaningful, " and the author recommends that researchers show how and why such silences were interpreted.

Findings Snapshot

Date or Source	Metric	Value	Implication
10 August 2026 (LSE Impact)	Non-textual evidence types listed	6+ (silence, hesitation, spatial arrangement, informal talk, fieldnotes, absences)	Researchers should record these systematically to convert them into evidence
2008 (cited study)	Published research on spatial arrangements	1 influential study cited	Spatial layout can carry social meaning beyond spoken words
2022 (cited research)	Studies on informal conversations	1+ article cited	Informal talk outside recordings can alter interpretation of formal interviews

Implications for qualitative researchers and UX teams

Answer: Researchers must treat fieldnotes, silences and setting as analyzable data, not just background, according to LSE Impact (10 August 2026).

The LSE Impact article (10 August 2026) recommends keeping clearer audit trails that record when and how fieldnotes were produced, which helps defend interpretive moves during review or publication.

UX researchers should note that spatial arrangements and subtle participant behaviours observed in usability sessions can change interpretation of interview answers, a point the LSE Impact piece illustrates with ethnographic examples.

The LSE Impact author emphasizes that transparency does not always require public disclosure of sensitive material, and researchers should explain what cannot be shared and why.

How Evidano helps convert observations into defensible evidence

Problem: Non-textual context is lost during transcription

Solution: Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.

Evidano feature mapping: use automated transcription with a custom dictionary and PII redaction to capture verbatim content while attaching timestamps and speaker markers so pauses and overlaps are preserved (see Evidano speech-to-text).

Problem: Fieldnotes and spatial observations are scattered across documents

Solution: Evidano ingests documents and fieldnote files, then applies thematic extraction and co-occurrence network analysis so repeated spatial cues and tonal markers appear as codable patterns (see Evidano features).

Evidano feature mapping: use the platform’s document import plus AI chat over your corpus to ask targeted queries like "Where did participants hesitate most? " and get extractable excerpts linked back to original notes.

Problem: Audit trails and methodological transparency are time consuming

Solution: Evidano automatically links coded excerpts to source files and timestamps, producing exportable audit trails that match the LSE Impact recommendation to document how fieldnotes became evidence.

Evidano feature mapping: combine thematic and frequency analyses with versioned project histories so reviewers can see when and why interpretive codes were applied.

FAQ: what counts as data in qualitative research

What exactly counts as data beyond interview transcripts?

Answer: Data beyond transcripts includes pauses, silences, hesitations, spatial arrangements, informal conversations and fieldnotes, according to LSE Impact (10 August 2026).

The LSE Impact article (10 August 2026) lists these forms and explains that they become analytic evidence when recorded systematically and compared across cases.

How should researchers record silences and hesitation so they are analyzable?

Answer: Researchers should timestamp, describe and code silences in fieldnotes and link them to transcript timestamps, as recommended by the LSE Impact piece (10 August 2026).

The LSE Impact article (10 August 2026) advises keeping consistent note templates and revisiting notes during analysis to justify interpretive claims.

Can AI tools capture non-textual qualitative data?

Answer: AI tools can assist but cannot replace researcher judgement; the LSE Impact article (10 August 2026) stresses that AI is useful for transcription, organisation and coding but not for deciding analytic meaning without methodological transparency.

The LSE Impact author warns that transcripts alone are insufficient and that AI workflows must be designed to preserve links between text and contextual fieldnotes.

How do I make sensitive qualitative materials transparent without exposing participants?

Answer: Document your audit trail and explain what cannot be shared while anonymising or withholding sensitive items, as recommended by LSE Impact (10 August 2026).

The LSE Impact article (10 August 2026) suggests detailed methodological notes and carefully anonymised data sharing where possible, or clear statements of why material remains private.

Conclusion & Next Steps

Answer: The LSE Impact essay (10 August 2026) reframes qualitative evidence to include pauses, spatial cues and fieldnotes, and urges researchers to make those moves into evidence visible.

Practical next steps are to adopt consistent fieldnote templates, timestamp and link non-textual observations to transcripts, and create transparent audit trails during analysis.

If you want to operationalise those steps in an AI-enabled workflow, try tools that preserve timestamps, ingest fieldnotes and produce exportable audit trails; see Evidano features for relevant capabilities.

To get started with ingesting transcripts, fieldnotes and documents and producing thematic analyses with audit trails, Try Evidano for free.

Topics

what counts as data in qualitative research
qualitative data examples
AI for qualitative research
fieldnotes and qualitative evidence

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What Counts as Data in Qualitative Research

Key Takeaways

What happened and why it matters

Findings Snapshot

Implications for qualitative researchers and UX teams

How Evidano helps convert observations into defensible evidence

Problem: Non-textual context is lost during transcription

Problem: Fieldnotes and spatial observations are scattered across documents

Problem: Audit trails and methodological transparency are time consuming

FAQ: what counts as data in qualitative research

What exactly counts as data beyond interview transcripts?

How should researchers record silences and hesitation so they are analyzable?

Can AI tools capture non-textual qualitative data?

How do I make sensitive qualitative materials transparent without exposing participants?

Conclusion & Next Steps

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