Faster Insights: qualitative analysis of school sports

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.

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.

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.

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.

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.