This post explains how to analyze multi‑stakeholder qualitative datasets so school teams and researchers can turn transcripts into policy- and program-ready recommendations. The June 29, 2026 PLOS ONE study (n = 18) on school sports in Sakarya, Türkiye used three focus groups (students, teachers, parents) to surface convergent and divergent perspectives on motivation, time management, and school commitment, and it provides a compact example to follow. Read the original study: PLOS ONE. Visit the product site for more information: Evidano.
Key Takeaways
Evidano is an AI-powered qualitative data analysis platform that helps you run reproducible analyses of school sports transcripts and turn findings into decision-ready outputs. Use multi‑stakeholder focus groups to reveal role-based divergences, then align schedules and supports to realize academic and psychosocial benefits. A two-week pilot workflow can move audio to stakeholder-ready briefs with representative quotes and visuals.
- Taş et al. (Published June 29, 2026) used 3 focus groups (n = 18) and found perceived gains in motivation, time management, and school belonging, with parents noting fatigue and academic balance concerns.
- Design analyses to compare segments (students vs teachers vs parents) so you avoid misleading generalizations and highlight operational levers like scheduling and coach-teacher coordination.
- A two-week pilot (upload, auto‑transcribe, code, compare, visualize, brief) produces decision-ready outputs for school leaders and policymakers.
Fast take & source
Fast take: Taş et al. (Published June 29, 2026) found school sports are perceived to boost motivation, time management, and school belonging, while parents raised concerns about student fatigue and balancing academics.
Read the original study: PLOS ONE.
- Why it matters: multi‑stakeholder qualitative data reveal convergent benefits and role‑based divergences that schools must coordinate to realize academic gains.
- Payoff for you: reproducible themes, segment comparisons (student vs parent vs teacher), and visualizations that support policy or schedule changes.
Study snapshot (quick facts)
| Metric | Value | Source / Note |
|---|---|---|
| Publication date | June 29, 2026 | PLOS ONE |
| Sample | n = 18 (3 focus groups of 6) | 6 students (grades 10–12), 6 parents, 6 PE teachers |
| Location / Context | Public Anatolian High School, Sakarya, Türkiye | Fall semester 2024–2025 academic year |
| Data collection dates | Students: Nov 26, 2024; Teachers: Dec 4, 2024; Parents: Dec 7, 2024 | Audio recorded, Turkish transcripts |
| Method | Focus groups, descriptive qualitative and inductive content analysis | Manual coding, themes validated by second researcher |
| Key themes | Motivation, time management, school belonging, psychosocial gains, perceived academic risk, adult support | Mixed stakeholder perspectives |
What the study did (plain English)
The study ran separate 45 to 60 minute focus groups for students, teachers, and parents to capture how school sports are experienced and interpreted.
The transcripts were analyzed with inductive content analysis to surface recurring patterns and differences across stakeholder groups.
- Data: verbatim Turkish audio transcriptions, translated for reporting.
- Analysis orientation: descriptive, then inductive coding, then categories, then higher‑order themes.
- Quality checks: peer debriefing, independent coding by a second researcher, audit trail.
Key findings you can reuse
The paper’s practical takeaways are immediately actionable for program managers and researchers who run school sport interventions.
- Perceived benefits: increased academic motivation, discipline, time planning, self confidence, and school belonging.
- Stakeholder divergence: students and teachers emphasize motivation and organization, parents focus more on fatigue and academic priority.
- Conditionality: benefits appear when sports are embedded in supportive teacher and parental practices, and when institutional coordination exists.
- Operational implication: scheduling, flexible academic arrangements during competition periods, and monitoring systems reduce risk.
So what for researchers, policy teams, and school leaders
For qualitative researchers
Multi‑informant designs reveal role‑based interpretive frames and should include segment comparisons to avoid misleading generalizations.
Record exact dates, sampling criteria, and saturation logic in your audit trail to support transferability.
For school program managers
Stakeholder quotes combined with theme frequencies help make the case for schedule changes and targeted supports such as study sessions around tournaments.
Track which sports or branches produce the largest time‑use tradeoffs and pilot staggered practice schedules.
For policymakers
Combine perceived outcomes with objective indicators in mixed‑method follow‑ups, such as attendance and grades, to build robust evidence for resource allocation.
Prioritize coordination policies that specify coach‑teacher communication protocols during exam seasons.
Do more, faster with Evidano
Ingest & clean
Import audio, transcripts, and survey sheets directly into Evidano for cleaning and PII redaction.
Use custom dictionaries for Turkish sports terms and automatic PII redaction to support ethics compliance.
Transcription & translation
Auto‑transcribe interviews with reviewer workflows and translate excerpts while preserving participant terminology using custom glossaries.
Add a human review step for final reporting to preserve cultural nuance.
Thematic + frequency analysis
Run AI‑assisted inductive coding to surface themes such as motivation or fatigue, then refine with a human‑validated codebook.
Get theme frequencies and representative quotes per stakeholder segment.
Cross‑segment comparisons
Automatically compare theme prevalence across students, teachers, and parents, and flag divergences such as parents reporting fatigue more than students.
Use those comparisons to prioritize operational responses.
Visualizations & reporting
Export word clouds, co‑occurrence networks, hierarchical code to subcode maps, and stakeholder‑specific quote tables for slides or policy briefs.
Use representative quotes and visuals to align teachers, coaches, and parents in stakeholder meetings.
Security & reproducibility
Data are encrypted at rest and in transit, and Evidano models are proprietary with user data not used to train third‑party models.
Export coded datasets and audit trails for reproducibility and ethics documentation.
Two-week pilot workflow: from audio to decision
Follow this minimal two‑week plan to replicate a study like the PLOS ONE example and produce decision‑ready outputs.
- Day 1–2: Upload audio and consent forms to Evidano, set custom dictionary for sports terms and proper names.
- Day 3–5: Auto‑transcribe, validate transcripts, and run initial topic extraction.
- Day 6–8: Generate inductive codes with AI suggestions, import or edit codebook, and apply across segments.
- Day 9–11: Run cross‑segment frequency and co‑occurrence analyses, pull representative quotes per theme and stakeholder.
- Day 12–13: Create visual reports such as code hierarchy and network, and a one‑page executive brief.
- Day 14: Stakeholder review meeting, using clickable quotes and visuals exported from Evidano to align teachers, coaches, and parents.
FAQ: qualitative analysis of school sports
How do I compare perceived outcomes (qual) with grades (quant)?
Export coded themes and match participant IDs to academic records when consent is provided.
Use Evidano’s cross‑dataset merges to run simple pivot comparisons or export for statistical analysis in your preferred tool.
Can Evidano handle non‑English transcripts and preserve nuance?
Yes, upload original language audio or transcripts and add custom dictionaries for local terms, then use human review for final reporting to preserve cultural nuance.
Maintain original transcripts alongside translations to support auditability in publications.
What about ethics and sensitive data?
Use built‑in PII redaction during transcription, store encrypted exports, and document consent in your project metadata.
Evidano does not train external large language models on your data and provides exportable audit trails for ethical review.
Wrapping up: next steps
Evidano compresses weeks of manual work into reproducible pipelines that produce themes, segment comparisons, and publishable visuals.
- Run a two‑week pilot with your school or research corpus and produce a stakeholder brief before the next term.
- See how quickly you can move from audio to policy by starting a pilot: Try Evidano for free.