Faster Qualitative Analysis of Research Participation

Evidano is an AI-powered qualitative data analysis platform that accelerates freelist and open-text workflows for segment-level salience and visualization. The PLOS One freelisting study (published 1 July 2026) asked three prompts about “research” to 101 participants across Philadelphia, Atlanta, and Washington, DC (May–Sep 2023) and found shared salient terms like “study, ” “knowledge, ” “search, ” and “scary, ” with “research misconduct” uniquely salient for Black respondents. Teams can reproduce this workflow in days using import, dictionary mapping, salience/frequency analysis, sentiment tagging, cross-segment visualization, and follow-up measurement to test whether messaging reduces negative terms.

What to know in 30 seconds: the PLOS One freelisting study (published 1 July 2026) asked three prompts about “research” to 101 participants across Philadelphia, Atlanta, and Washington, DC (May–Sep 2023). Shared salient terms included “study, ” “knowledge, ” “search, ” and notably “scary”; “research misconduct” was uniquely salient for Black respondents. Full paper: PLOS One.

• Why it matters: negative perceptions are concentrated among people never previously asked to participate, suggesting outreach plus one positive experience changes attitudes.
• Payoff for readers: learn a reproducible, AI-accelerated workflow to turn freelist and open-text inputs into prioritized, segment-level actions.

Freelisting elicits short lists of words or phrases per respondent and the PLOS One study collected answers to three prompts about associations with “research, ” feelings when asked to participate, and thoughts about being a participant. Responses were cleaned into parent terms and analyzed for salience (Smith’s S) using Anthropac, and sentiment was coded by consensus.

• Strengths: quick, captures immediate priorities in participants’ own words, and suitable for comparing groups.
• Limitations noted by authors: small sample (n=101), web-mode priming (example used a ‘scary movie’), and limited geographic spread, deeper interviews still needed for context.
• Key signal: overlap across underrepresented groups suggests shared structural barriers (fear, perceived burden), plus group-specific concerns (for example, “research misconduct” salient for Black participants).

Researchers, UX teams, and trial recruiters should prioritize outreach to people who were never asked, address fear directly, and tailor messaging for historical mistrust. Practical implications from the study for teams designing recruitment and retention strategies include:

• Target people who were never asked: simple outreach converts attitudes, prioritize first-contact pipelines and low-barrier pilots.
• Address fear directly: communication materials should normalize procedures, reduce perceived burden, and include concrete incentives and logistics information.
• Tailor for historical mistrust: for Black communities, add transparency on oversight, data use, and results-sharing, and co-design with community partners.
• Measure impact: use salience and sentiment metrics to track whether messaging reduces negative terms (for example, “scary, ” “lab rats”) over time.

You can reproduce the PLOS One freelisting approach at scale in days by following a seven-step pipeline.

• 1) Import freelist CSVs or open-text survey exports into Evidano and tag respondent demographics (segment flags).
• 2) Apply or build a custom dictionary to collapse synonyms into parent terms (automated suggestions plus manual review).
• 3) Run salience and frequency analysis with order-weighting; produce scree plots and auto-detect the elbow for salient terms.
• 4) Run sentiment tagging and compare sentiment distributions by segment (prior experience versus never asked).
• 5) Visualize top shared versus unique terms with co-occurrence networks and export visuals for community partners.
• 6) Draft targeted outreach messaging addressing top barriers (fear, effort) and test via small pilots.
• 7) Re-run the same pipeline on follow-up freelists to measure whether “scary” and other negative terms decline.

When working with human-subject data, preserve consent documents, IRB approvals, and apply PII redaction before analysis. This post is research-methods focused and non-diagnostic.

Freelisting surfaced actionable signals, shared fears, and a clear opportunity: ask more people from underrepresented groups and make the first contact low-friction. If you run qualitative analysis of research participation at scale, you can turn those signals into measurable recruitment wins.

• Ready to replicate and scale the PLOS One freelisting approach with automated salience, cross-segment comparisons, and shareable visuals? Try Evidano for free.