Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, fieldnotes, and documents. The primary question for qualitative researchers and research managers is how to define what counts as data so that analysis is rigorous and reproducible. The primary keyword for this post is qualitative data definition. This post explains the argument made by LSE Impact on 10 August 2026, shows concrete steps researchers can take to preserve context beyond transcripts, and maps those needs to AI-enabled workflows and tools.
Key Takeaways
According to LSE Impact (10 August 2026), qualitative data includes more than transcripts and quotations: it can include silences, pauses, fieldnotes, spatial arrangements, and informal conversations, and researchers must document how those materials became evidence. Read the original piece at LSE Impact.
- LSE Impact published the argument on 10 August 2026 that qualitative data should include non-textual observations when systematically recorded and compared.
- According to LSE Impact (10 August 2026), researchers should keep clearer audit trails and explain why silences or absences became analytically significant.
- According to LSE Impact (10 August 2026), AI tools help with transcription, coding, and organization, but they cannot on their own determine the analytic meaning of contextual observations.
What happened and how qualitative data is being redefined
LSE Impact argued on 10 August 2026 that qualitative data should be understood broadly, not limited to transcripts and quotations.
According to LSE Impact (10 August 2026), classic qualitative materials include interview recordings and transcripts, but the author highlights that researcher fieldnotes, silences, hesitations, spatial arrangements, and informal conversations can also be evidence when they are systematically recorded and compared.
According to the SAGE Handbook of Qualitative Research, methodological transparency is central to judging qualitative rigor; LSE Impact (10 August 2026) extends that point to say transparency must cover how non-textual observations were produced and used.
The LSE Impact article states that “An interview is not simply a container for answers, ” and that researchers should ask why pauses, silences, or absences occurred and whether those moments repeat across cases.
The LSE Impact article also states, “Rather, qualitative research broadens what can count as data, ” and it recommends clearer audit trails and careful anonymisation decisions when sharing sensitive materials.
Implications for researchers and research teams
Researchers should treat the qualitative data definition as plural: transcripts are necessary but not always sufficient for claims.
According to LSE Impact (10 August 2026), research teams should adopt three practices: systematic fieldnote protocols, explicit coding rules for non-verbal signals, and documented decisions about what cannot be shared for ethical reasons.
User research teams and ethnographers should plan data collection instruments to capture context: audio/video when ethically possible, structured fieldnote templates, and situational metadata such as room layout or who was present.
Policy analysts and evaluators should require transparent audit trails when commissioning qualitative work, as recommended by LSE Impact (10 August 2026), so readers can see how silences, absences or spatial cues informed conclusions.
How Evidano Helps
Problem: Context lost when analysis focuses on transcripts
Answer: Analysts often lose context when they convert field encounters to text-only transcripts, and LSE Impact (10 August 2026) warns this narrows what counts as data.
Solution: Evidano supports ingestion of transcripts plus structured fieldnotes and metadata so teams can code pauses, silences, and spatial tags alongside verbatim text. See Evidano features for thematic coding and cross-segment analysis.
Problem: Slow, manual synthesis of fieldnotes and transcripts
Answer: Manual synthesis is time-consuming and makes it hard to compare subtle patterns across cases.
Solution: Evidano provides automated thematic extraction and cross-case frequency analysis that preserves researcher annotations and fieldnote provenance, accelerating synthesis without dropping context. For audio-first projects, Evidano integrates with our speech-to-text pipeline that supports custom dictionaries and PII redaction.
Problem: AI tools misinterpret non-textual signals
Answer: Off-the-shelf AI can mislabel hesitations and silences if those signals are not annotated and audited.
Solution: Evidano lets teams attach structured annotations to timeline segments, code non-verbal cues, and keep an audit trail showing how AI-assisted codes were generated and human-reviewed. Evidano’s workflow explicitly separates machine pre-processing from final human-led interpretation.
FAQ: qualitative data definition
What exactly counts as data in qualitative research?
Answer: Data in qualitative research can include transcripts, fieldnotes, recorded hesitations or silences, spatial arrangements, informal conversations, and other contextual observations when they are systematically recorded and used in analysis.
Supporting detail: LSE Impact (10 August 2026) makes this point and recommends researchers keep clear audit trails showing how these materials were turned into evidence.
Can AI replace the researcher’s judgement about context?
Answer: No, AI can assist with transcription, organization, and initial coding but cannot on its own determine how contextual details become analytically meaningful.
Supporting detail: LSE Impact (10 August 2026) explicitly warns that AI tools are most useful when situated within researcher-led methodological transparency and knowledge of the field.
How should I document silences, hesitations, or absences so they can count as data?
Answer: Document silences, hesitations, and absences with structured fieldnote templates, time-stamped annotations, and comparative notes explaining why those moments seemed significant.
Supporting detail: LSE Impact (10 August 2026) advises comparing such moments across interviews or sites and keeping an audit trail describing when and why the researcher treated those moments as evidence.
How do I balance transparency and participant confidentiality when sharing fieldnotes?
Answer: Balance requires selective anonymisation, clear documentation of what cannot be shared, and explaining how claims were developed despite limits on sharing.
Supporting detail: LSE Impact (10 August 2026) notes that transparency does not always mean public sharing; researchers should be explicit about what is withheld for ethical reasons and how they validated findings.
Conclusion & Next Steps
LSE Impact (10 August 2026) reframes the qualitative data definition to include non-textual observations when those observations are systematically recorded and compared.
Researchers should adopt structured fieldnote protocols, time-stamped annotations, and clear audit trails so silences, spatial cues, and informal talk can be valid evidence.
Teams that want to combine AI-assisted transcription and coding with transparent provenance should consider tools that preserve annotations and human review workflows.
To try these approaches, explore how Evidano supports transcripts, fieldnotes, and audit trails and Try Evidano for free.
Topics
- qualitative data definition
- what counts as data in qualitative research
- qualitative evidence
- AI for qualitative research
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