Qualitative Analysis: Low‑Emission Food Systems

Evidano is an AI-powered qualitative data analysis platform that unifies policy PDFs, transcripts, FGDs, and survey data to accelerate reproducible qualitative workflows.

This post shows how a PLOS Soc Trang case study (published 1 July 2026, data May–Oct 2024) used mixed methods and how teams can reproduce and scale that workflow in seven steps.

• The PLOS case study used 24 semi-structured key informant interviews, six FGDs, and a 24-household survey and found funding instability, limited training access (83% report men as primary training recipients in parts of An Thanh 3), and gendered social norms.
• Small-N qualitative datasets (for example, n≈24 interviews) can reveal operational barriers and local enablers that quantitative indicators miss.
• Reproducible workflows should prioritize cross-segment frequency and co-occurrence analyses to surface differences by gender and location (An Thanh 3 vs Long Duc).

This section summarizes the quick numerical findings and implications from the PLOS Soc Trang case study in one table.

This section describes what the PLOS case study did and why the findings matter. The authors used a mixed-methods case study approach focused on two communes in Soc Trang. Data sources included national and provincial policy documents, 24 semi-structured key informant interviews (12 men, 12 women), six FGDs (men, women, youth), and a household survey of 24 randomly selected households. Interview and FGD transcripts were coded in NVivo and analyzed using inductive and deductive thematic techniques with triangulation between qualitative and survey data.

• Why this matters: the paper shows that gender is often written into national policy but not resourced or monitored at the local level.
• Operational takeaway: small qualitative datasets (n≈24 interviews) can reveal barriers (access, funding, norms) and local enablers (cooperatives, Women’s Union programs) that quantitative indicators miss.

This checklist gives seven concrete steps (inputs to outputs) to reproduce the PLOS Soc Trang qualitative analysis.

• 1) Gather documents: policy PDFs, interview audio and transcripts, FGDs, and a survey spreadsheet on a May–Oct 2024 style timeline.
• 2) Ingest into Evidano and run auto-transcription with a custom dictionary for local terms and acronyms to reduce manual corrections.
• 3) Auto-suggest themes and import or create a codebook; apply hierarchical codes across all sources.
• 4) Run cross-segment frequency and co-occurrence analyses to compare women, men, youth and An Thanh 3 vs Long Duc.
• 5) Validate with quick AI-assisted queries such as "show me quotes where women describe barriers to funding" and human-review representative quotes.
• 6) Produce visuals: word clouds, co-occurrence networks, hierarchical code trees for stakeholder reports.
• 7) Export a concise decision memo for provincial teams that links quotes to recommended actions on funding models and training access changes.

This section summarizes the post and directs readers to the study and trial options. If you are translating policy documents and a modest qualitative corpus like the PLOS Soc Trang study into policy-ready recommendations, focus on cross-segment comparisons, durable funding signals, and actionable training access metrics. Evidano speeds that work by unifying documents, automating transcription with local dictionaries, producing reproducible thematic and cross-segment analyses, and exporting stakeholder-ready visuals.

• Read the full study: PLOS Sustainability Transformation.
• Ready to turn transcripts and policy docs into rapid, defensible insights? Start a trial or request a demo: Try Evidano for free.