Evidano is an AI-powered qualitative data analysis platform that automates transcription, translation, AI-assisted coding, and cross-segment analysis for secure, auditable research workflows. Researchers and school teams often have rich focus-group transcripts but no fast, reproducible way to turn them into actionable policy and program changes. This post shows a compact, reproducible workflow for qualitative analysis of school sports using the new PLOS One study (n = 18; Sakarya, Türkiye) as an example, and explains how to map each step into Evidano so teams can go from transcripts to themes, cross-segment comparisons, and visual deliverables in hours instead of weeks. Key evidence used in this post: the PLOS One paper published 29 June 2026 and focus groups run Nov–Dec 2024. See how Evidano can automate transcription, thematic coding, and secure, segment-level reporting without training your data on third-party models.
Key Takeaways
This post shows a reproducible workflow to turn school-sports focus-group audio into policy-ready findings using automated transcription, translation, AI-assisted coding, and cross-segment analysis. The example uses a PLOS One qualitative study (published 29 June 2026) with n = 18 participants in Sakarya, Türkiye, whose themes included motivation, time management, belonging, psychosocial gains, and parental concerns about fatigue and scheduling.
- The PLOS One study (29 June 2026) ran three focus groups in Nov–Dec 2024 with n = 18 (6 students, 6 parents, 6 PE teachers).
- Core positive themes were academic motivation, time management, school belonging, and psychosocial development while parents primarily raised fatigue and scheduling conflicts.
- Follow the post’s seven-step checklist to reproduce a comparable multi-informant qualitative analysis in about a week using automated transcription, translation, AI-assisted coding, and cross-segment analysis.
- Evidano preserves verbatim traces, provides cross-segment frequency and co-occurrence analyses, and secures data without using it to train third-party models.
Fast take + source
This section summarizes the study, its design, and the central findings. TL; DR: A qualitative PLOS One study (published 29 June 2026) found school sports were perceived to increase academic motivation, time management, and school belonging, but parents flagged fatigue and scheduling risk. Read the full paper: PLOS One.
- Study design: descriptive qualitative focus groups with 18 participants (6 students grades 10–12, 6 parents, 6 PE teachers) in Sakarya, Türkiye.
- Data collection: three focus groups in Turkish (student 26 Nov 2024; teachers 4 Dec 2024; parents 7 Dec 2024); ethics approval 15 Nov 2024 (Decision No: 50/22).
- Primary themes: academic motivation/discipline, time management, school belonging, psychosocial development, and adult support versus perceived academic risk.
Findings snapshot
| Item | Value | Source / Note |
|---|---|---|
| Publication date | 29 June 2026 | PLOS One (DOI e0351877) |
| Sample | n = 18 (6 students, 6 parents, 6 PE teachers) | Grades 10–12; one public Anatolian high school, Sakarya |
| Methods | 3 focus groups; descriptive qualitative + inductive content analysis | Transcribed verbatim in Turkish; translated for reporting |
| Core positive themes | Motivation, time management, belonging, psychosocial gains | Reported across students & teachers |
| Primary concern | Fatigue / scheduling conflicts | Raised mainly by parents |
What the study actually did (brief)
This section explains the study design and analytic approach. The authors ran separate focus groups for students, parents, and PE teachers and analyzed transcripts using descriptive qualitative procedures with inductive content analysis. Transcripts were transcribed verbatim in Turkish and translated for reporting; coding and theme development were manual and validated through peer discussion. The emphasis was on perceived academic achievement (motivation, concentration, engagement) and school commitment framed via emotional, behavioral, and cognitive engagement.
- Context matters: findings are context-bound to a single school and the Turkish exam culture.
- Perceptions are not objective grades: the study focuses on meaning-making, not test scores.
- Useful reproducibility details reported: interview dates (Nov–Dec 2024), ethics clearance (15 Nov 2024), and the coding-tree approach summarized in the paper.
So what for researchers, policy teams, and schools?
For qualitative researchers
The study offers a compact template for multi-informant focus groups and inductive coding. Use the paper to align interview guides with school engagement theory and to document coding trails to support transferability. If you plan a similar study, predefine criteria for saturation (the authors used repetition across 6-person groups) and preserve verbatim quotes for credibility.
For school leaders & policy teams
This study indicates sports can boost motivation and belonging when adult guidance and schedule coordination are present. Policy levers include synchronized calendars, flexible academic arrangements during competitions, and monitoring systems to flag students showing fatigue or absenteeism.
For program evaluators
The study recommends combining perceived outcomes with academic indicators in mixed-methods follow-ups. Use cross-segment analysis to check for parent/teacher/student divergence before scaling programs.
Do more, faster with Evidano
Problem: Manual transcription & scattered files
Evidano automates transcription, translation, and secure document management to remove manual file wrangling. Spend hours cleaning audio, translating, and assembling transcripts no more: Evidano offers automated transcription with a custom dictionary and PII redaction while preserving source-language traces for auditability.
Problem: Slow, inconsistent coding
Evidano accelerates and standardizes coding with AI-assisted suggestions and codebook import, while preserving an audit trail. Evidano ingests transcripts and applies AI-assisted coding using your codebook or suggests inductive codes, produces hierarchical themes and subcodes, and lets you audit every coded quote.
Problem: Comparing stakeholder views
Evidano provides cross-segment analytics to reveal which themes are dominant for students, parents, or teachers. Run cross-segment frequency and co-occurrence analyses to show which themes are dominant for students versus parents versus teachers, which mirrors the comparative lens used in the PLOS study.
Problem: Sharing findings beyond methods teams
Evidano exports visualizations and clickable quotes to make findings accessible to stakeholders. Export word clouds, co-occurrence networks, hierarchical code maps, and stakeholder memos; all data is encrypted and never used to train third-party models.
Problem: Need for follow-up data
Evidano supports standardized follow-ups to check on emerging issues like fatigue during exam windows. Evidano supports AI avatar interviews to collect standardized follow-ups while keeping transcripts in your secure workspace.
Checklist: reproduce the paper’s analysis in a week
This checklist lists the practical steps to replicate a comparable multi-informant qualitative analysis in about a week. Follow these practical steps to replicate a comparable multi-informant qualitative analysis:
- 1) Gather assets: audio files, consent forms, interview guide; note interview dates and ethics clearance.
- 2) Upload audio and supporting documents to Evidano and run automated transcription with a custom dictionary for sport terms and local names.
- 3) Translate where needed, keeping the original text and translation side-by-side for auditability.
- 4) Create or import a starter codebook (motivation, time management, belonging, risk, adult support) and run AI-assisted coding.
- 5) Review and refine codes, then lock final code definitions for reproducible outputs.
- 6) Run cross-segment frequency and co-occurrence analyses (students v parents v teachers) and export visualizations.
- 7) Produce a one-page stakeholder brief with the top three leverage recommendations and clickable example quotes.
FAQ: qualitative analysis of school sports
Q: How do I compare segments reliably?
Answer: Use consistent code definitions, automated counts, and visualization to compare segments reliably. Use consistent code definitions, run automated frequency counts, and visualize co-occurrence by segment; Evidano includes cross-segment tables and flags to help triage differences.
Q: What about sensitive data?
Answer: Keep qualitative data consented, redacted, and encrypted to meet ethical standards. The post notes the study is research-focused and non-diagnostic; redact personally identifying information, keep files in encrypted Evidano workspaces, and remember data is not used to train third-party models.
Q: Can I reproduce the study’s manual coding approach?
Answer: Yes, reproduce manual coding by combining AI-assisted code suggestions with human review and an audit trail. Upload transcripts, run AI-assisted coding to generate initial codes, then conduct manual review; Evidano preserves an audit trail of coding decisions for transparency.
Wrapping up & next steps
This section summarizes the fastest path from raw audio to policy-ready recommendations and gives next steps. If your team runs focus groups like the PLOS study, the fastest path from raw audio to policy recommendations is an automated, auditable pipeline: transcription, translation, AI-assisted coding, cross-segment analysis, and visual exports. Try a pilot: upload one focus-group transcript and run the seven-step checklist above, or Try Evidano for free. Explore a demo and secure workspace at Evidano to see how the tools map to the PLOS study’s needs.