This post explains how researchers should decide "what counts as data in qualitative research" and how AI-enabled qualitative analysis can preserve contextual evidence beyond transcripts. According to the LSE Impact blog post published on 10 August 2026, qualitative evidence includes more than words on a page and requires transparent audit trails to show how observations were turned into claims. According to the LSE Impact blog post (10 August 2026), researchers should record hesitations, silences, spatial arrangements and fieldnotes and compare them systematically with interview transcripts to reach robust interpretations.
Key Takeaways
According to the LSE Impact blog post (10 August 2026), qualitative data includes transcripts and also contextual signals such as silences, pauses, spatial arrangements and fieldnotes (LSE Impact).
- The LSE Impact post was published on 10 August 2026 and lists at least six non-transcript data types researchers should treat as evidence when recorded and compared.
- The LSE Impact post is 7, 062 characters long as archived on 10 August 2026, indicating a detailed methodological argument rather than a short commentary.
- When qualitative evidence from interviews is treated only as text, according to LSE Impact (10 August 2026), key contextual signals such as repeated hesitations or absences can be lost unless fieldnotes and audit trails are preserved.
What happened and how LSE framed the issue
The LSE Impact blog post argued that qualitative research expands what counts as data beyond transcripts, and it published that argument on 10 August 2026.
According to Sezai Doruk Soyata writing for LSE Impact (10 August 2026), an interview should be treated as a social encounter, not just a container of answers: "an interview is not simply a container for answers."
According to LSE Impact (10 August 2026), researchers should record and compare contextual details such as pauses, hesitations, spatial arrangements, informal conversations and fieldnotes so those details can become analytic evidence rather than private impressions.
Findings snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| 10 August 2026 | Publication | LSE Impact | Methodological argument for expanding what counts as data |
| 10 August 2026 | Post length | 7, 062 characters | Detailed examples and citations suitable for methodological guidance |
| 10 August 2026 | Non-transcript data types listed | 6 (silences, hesitations, spatial arrangements, informal conversations, fieldnotes, sensitive topics) | Practical checklist for researchers to record and compare contextual signals |
Implications for qualitative researchers and UX teams
Qualitative researchers should treat observational notes and speech features as evidence and explicitly document how they were collected, according to LSE Impact (10 August 2026).
According to LSE Impact (10 August 2026), UX researchers who rely on interviews should preserve nonverbal cues and the researcher's fieldnotes because these cues can explain recurring patterns that transcripts alone cannot capture.
- Researchers should create audit trails that describe when and how fieldnotes were written, according to LSE Impact (10 August 2026).
- Research teams should decide which contextual signals are recorded, why they are analytic, and how they will be anonymised before analysis, because transparency matters even when raw data cannot be shared, according to LSE Impact (10 August 2026).
- When sensitive topics prevent public data sharing, researchers should document what cannot be shared and why, according to LSE Impact (10 August 2026).
How Evidano helps translate LSE guidance into practice
Problem: Context lost when data is reduced to transcripts
Answer: Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Evidano supports multi-source ingestion so teams can combine transcripts, fieldnotes, and observational annotations into a single project, which implements the LSE Impact recommendation to compare contextual details with transcripts.
Problem: Inconsistent fieldnote capture and audit trails
Answer: Evidano records provenance and version histories so researchers can show when and how fieldnotes were produced, aligning with LSE Impact (10 August 2026) advice to keep clearer audit trails.
Evidano’s transcription and speech-to-text features can capture hesitations and timestamps, and its project logs show how notes were revised during analysis.
Problem: Hard to integrate non-textual signals into coding
Answer: Evidano’s thematic and cross-segment analysis tools allow teams to code pauses, silences and spatial observations as first-class categories, supporting the LSE Impact call to treat these signals as evidence.
Evidano’s visualizations help teams compare where non-transcript codes co-occur with quotations so researchers can trace patterns across cases as the LSE post recommends.
Problem: Privacy and sensitive data sharing concerns
Answer: Evidano provides configurable PII redaction and encrypted storage so teams can document sensitive methods without exposing raw identities, which supports LSE Impact’s point that transparency can be achieved without making everything public.
Evidano’s platform-level controls let researchers note why particular materials cannot be shared and attach explanatory audit notes to analytic claims.
FAQ: what counts as data in qualitative research
What exactly counts as data in qualitative research?
Answer: Qualitative data includes transcripts, documents, fieldnotes, pauses, silences, spatial arrangements and informal conversations when those items are systematically recorded and compared.
According to LSE Impact (10 August 2026), researchers should not assume every silence is meaningful, but they should record and re-examine repeated patterns so that contextual cues can be evaluated as evidence.
How should researchers treat fieldnotes when making claims?
Answer: Researchers should treat fieldnotes as evidence if they are detailed, consistently produced and revisited during analysis.
According to LSE Impact (10 August 2026), fieldnotes become analytic only when researchers describe how they were produced and how they informed comparisons with interviews and documents.
Can AI tools help capture non-textual qualitative signals?
Answer: AI tools can help capture, transcribe and timestamp speech features and help organise contextual notes, but human judgment is still required to turn those signals into analytic evidence.
According to LSE Impact (10 August 2026), AI is useful for transcription and organisation but cannot by itself determine how contextual details become analytically meaningful.
How should sensitive qualitative data be shared or archived?
Answer: Sensitive qualitative data should be documented in audit trails and anonymised where possible, with clear notes about what cannot be shared and why.
According to LSE Impact (10 August 2026), transparency about methods does not always mean public release; it can mean clear documentation of decisions and constraints.
Conclusion & Next Steps
The LSE Impact blog post (10 August 2026) reminds researchers that what counts as data in qualitative research goes beyond transcripts to include silences, spatial arrangements and fieldnotes when those materials are systematically recorded and compared.
Practically, teams should build audit trails, timestamp and code contextual signals, and decide ahead of time how to anonymise sensitive material, consistent with the LSE Impact recommendations.
If you want an AI-enabled workflow that preserves contextual signals while keeping provenance and privacy controls, see Evidano’s features for project-level ingestion, transcription and audit logs, and speech-to-text for capturing hesitations and timestamps.
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Topics
- what counts as data in qualitative research
- qualitative data definition
- AI qualitative analysis
- fieldnotes as data
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