Qualitative researchers face a growing risk that complex evidence will be reduced to text when AI tools are applied. The primary keyword for this post is qualitative data types. According to LSE Impact on 10 August 2026, qualitative data can include not only transcripts but also "silences and absences, hesitations, spatial arrangements, informal conversations, [and] fieldnotes". This post explains what counts as qualitative data, why the distinction matters for analysis with AI, and practical steps research teams can take to preserve contextual signals during automated processing.
Key Takeaways
According to LSE Impact on 10 August 2026, qualitative data includes transcripts plus contextual materials such as silences, fieldnotes, and spatial arrangements; those materials become evidence when they are systematically recorded and compared.
- 1. LSE Impact published the essay on 10 August 2026 and named pauses, hesitations, and spatial arrangements as forms of qualitative evidence.
- 2. The LSE author, Sezai Doruk Soyata, warns on 10 August 2026 that "a transcript can tell us what was said, but it rarely captures the full situation in which something was said."
- 3. Researchers should keep clear audit trails and explain how fieldnotes and absences were turned into findings, as advised by LSE Impact on 10 August 2026.
What Happened: how the LSE essay reframes qualitative data types
The LSE Impact essay on 10 August 2026 reframes qualitative data to include non-textual signals as analyzable evidence.
According to the LSE Impact piece, an interview is not only a container of answers but also a social encounter that produces hesitations, laughter, topic shifts and silences that can be analytically meaningful when recorded consistently.
According to the LSE Impact essay, fieldnotes convert observation into evidence by documenting who was present, how a space felt, and what changed after conversations, and the author uses ethnographic examples from Turkey to show how décor and menu design conveyed religious atmospheres without explicit statements.
"An interview is not simply a container for answers, " wrote Sezai Doruk Soyata in LSE Impact on 10 August 2026, and Soyata adds that researchers must ask why and when nonverbal moments occur and whether they repeat across cases.
Implications for qualitative researchers
Researchers should treat qualitative data types as broader than text and plan workflows that capture context before automated processing.
According to LSE Impact on 10 August 2026, failing to record hesitations or spatial cues can strip analysis of evidence that supports claims, so teams must create protocols to capture fieldnotes, audio/video metadata, and researcher reflections.
According to the LSE Impact essay, transparency matters: researchers should explain how absences or silences were judged analytically significant, and where full sharing is impossible they should document what cannot be shared and why.
A practical step is to build an audit trail: record when fieldnotes were written, link them to timestamps in recordings, and flag analytic memos that explain interpretation choices.
How Evidano Helps
Overview
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Evidano supports transcription with custom dictionary and PII redaction, which helps teams capture what was said while protecting participants, and Evidano supports integration of fieldnotes and timestamps so nonverbal moments are linked to transcript segments.
Evidano’s transcription features are described at Evidano Speech-to-Text and its thematic analysis and visualizations are explained at Evidano Features.
Problem: Non-textual context is lost in automated pipelines
Solution: Evidano links fieldnotes, audio/video metadata, and timestamps to transcripts so hesitations, silences and spatial descriptions remain searchable alongside quotes.
Problem: Analysts need transparent audit trails
Solution: Evidano records provenance for codes and memos, providing exportable audit trails that show how contextual observations moved into evidence.
Problem: Teams need fast synthesis without losing nuance
Solution: Evidano uses thematic and cross-segment analyses plus AI chat over your documents to produce evidence summaries while preserving links to the original fieldnotes and recordings.
FAQ: qualitative data types
What counts as data in qualitative research?
Answer: Qualitative data includes transcripts, documents, fieldnotes, pauses, absences, spatial arrangements and informal conversations when those materials are systematically recorded and interpreted.
According to LSE Impact on 10 August 2026, a silence or an absence becomes data only when the researcher documents and compares it across cases and explains its analytic relevance.
Can AI tools reliably identify silences and hesitations?
Answer: AI tools can detect audio pauses and hesitation markers but cannot by themselves determine analytic meaning.
According to LSE Impact on 10 August 2026, AI is useful for transcription and coding but judgment about contextual significance still depends on methodological transparency and researcher knowledge.
How should I document fieldnotes so they remain evidence?
Answer: Write timestamps, link notes to recordings, keep dated memos and record why each note matters to your claims.
According to the ethnographic literature cited by LSE Impact, disciplined note-taking that records who was present, the setting, and subsequent reflections turns impressions into traceable evidence.
How can teams preserve sensitive qualitative material while sharing audit trails?
Answer: Share redacted excerpts, anonymised metadata, and detailed methodological memos that document decisions while withholding identifying details when required.
According to LSE Impact on 10 August 2026, transparency does not always mean full public sharing; it can mean clear documentation of what cannot be shared and why.
Conclusion & Next Steps
The LSE Impact essay on 10 August 2026 reminds researchers that qualitative data types go beyond transcripts to include silences, spatial arrangements and fieldnotes, and teams must document how these materials become evidence.
Practically, research teams should build protocols to timestamp and link fieldnotes to recordings, keep exportable audit trails, and combine human judgement with AI tools for transcription and synthesis.
If you want to pilot a workflow that preserves contextual signals during automated analysis, try integrating transcription, timestamping and thematic synthesis with an AI platform designed for qualitative research like Evidano.
Start a trial today: Try Evidano for free.
Topics
- qualitative data types
- what counts as data in qualitative research
- AI-enabled qualitative research
- qualitative data analysis
Keep reading
- Commentary on NewsAI-enabled Qualitative Research: Cultural IdentityReproducible AI-enabled qualitative workflow for cultural curricula: extract themes from a 47-teacher PLOS One study (Jul 21, 2026) and run a two-week pilot.
- Commentary on NewsDevelopmental Assessment Ethiopia: AI Qualitative AnalysisRead a practical breakdown of barriers to child developmental assessment in Ethiopia and how AI-enabled qualitative analysis speeds insight and implementation planning.
- Commentary on NewsFixing Gaps: Child Developmental Assessment in EthiopiaActionable guide for researchers and implementers on child developmental assessment in Ethiopia, using AI-enabled qualitative analysis to speed synthesis and implementation.