Scale Host Insights: Qualitative Analysis of Homestays

This post shows how a 36-month sequential explanatory mixed-methods protocol on homestay hosting can be implemented with an AI-enabled workflow to speed transcription, harmonize coding, and produce cross-segment evidence for policy or UX decisions.

• The protocol is a sequential explanatory mixed-methods design: Phase 1 national survey (target n≥200), Phase 2 qualitative interviews (20–25), Phase 3 group concept mapping/co-design (20–25).
• Timelines in the protocol run from recruitment starting 1 Sep 2026 to study end 1 Sep 2029, with survey results expected March 2027 and REB approval REB# 2026−152.
• Use a 7-step runbook to reproduce the protocol’s analysis flow with AI support, starting with intake and PII redaction and ending with reproducible exports for SmartPLS/SPSS.

This protocol matters because it explains why Canadians host refugees and how hosting can be sustained, and it yields actionable outputs for host support and policy. Al-Hamad et al. outline a 36-month sequential explanatory mixed-methods study (Published: July 13, 2026) to answer why Canadians host refugees and how hosting can be sustained. Read the original protocol: PLOS ONE.

• Design: Phase 1 national survey (target n≥200); Phase 2 qualitative interviews (20–25); Phase 3 group concept mapping/co-design (20–25).
• Timelines: recruitment starts Sep 1, 2026; survey results expected March 2027; study end Sep 1, 2029.
• Why it matters: the protocol links hospitableness, empathy, satisfaction, readiness, and advocacy to usable host support tools and policy recommendations.

The protocol uses a sequential explanatory mixed-methods design to quantify, explain, and translate findings into prioritized actions. The team uses a national survey (structural equation modeling) to quantify relationships, semi-structured interviews to deepen understanding, and group concept mapping co-design to translate findings into prioritized actions.

• Quantitative tools: LimeSurvey for data collection; SPSS and SmartPLS for descriptive stats and SEM.
• Qualitative tools: Zoom-recorded interviews, NVivo 15 for thematic coding, iterative member checking.
• Participatory mapping: Groupwisdom for sorting/rating and cluster mapping; final outputs include a host toolkit and policy recommendations.

This checklist condenses the protocol’s analysis flow into concrete steps you can follow using AI-enabled tooling. Follow this condensed runbook to reproduce the protocol’s analysis flow with AI support:

• 1) Intake: collect survey CSVs, Zoom audio, and field notes; ingest into Evidano and enable PII redaction.
• 2) Transcribe & translate: run automated transcripts with the project dictionary (place names, program names).
• 3) Rapid theme pass: use AI-assisted coding to generate initial code suggestions; review and lock a pilot codebook.
• 4) Cross-segment analysis: tag demographics and run frequency and cross-tab reports to feed SEM variables.
• 5) Deep coding: run hierarchical coding for interviews, extract exemplar quotes for member-checking and co-design prompts.
• 6) Co-design support: import participants’ brainstormed statements and ratings; generate cluster maps and priority matrices for group concept mapping outputs.
• 7) Outputs & handoff: export reproducible datasets (code maps, labeled quotes, visuals) for statistical modeling (SmartPLS/SPSS) and stakeholder briefs.

This protocol is a practical blueprint because Al-Hamad et al.’s 13 July 2026 publication demonstrates a clear path from survey to co-design outputs for homestay hosting research. If your team needs to scale transcription, harmonize coding across analysts, and generate cross-segment evidence for policy or UX decisions, an AI-enabled platform shortens time-to-insight while preserving ethics and auditability.

Start with a 2-week pilot: ingest 10 interviews and 100 survey responses, lock a codebook, and run one co-occurrence/network visualization.

Ready for a secure demo and to map this exact workflow onto your project? Try Evidano for free.