qualitative-analysis

Qualitative analysis of open-ended survey responses identifies manifest and latent themes while assigning traceable quote IDs. It outputs a theme catalog and per-unit taggings, ensuring reproducible qualitative coding with auditable verification steps.

Equity-aware qualitative text analysis for policy research stratifies themes by available demographics to reveal representational gaps. It processes interviews and free-form notes, applying equity checks to prevent unsupported demographic inferences during theme extraction.

Interview sentiment analysis can be stratified by available demographic groups to highlight representational gaps. This group stratification reveals equity disparities in public perception studies, supported by auditable quotes linked to specific theme IDs.

YAML frontmatter configuration is required to establish repeatable parameters for qualitative coding on interview transcripts. This human-in-the-loop setup drives the extraction process and generates a governance-ready human-validation checklist for review.

Theme extraction distinguishes manifest versus latent themes when coding free-form notes by associating exact quotes with themes using IDs. This ensures traceable, auditable steps that output a theme catalog and per-unit taggings for human validation.

Automated theme extraction for public perception studies requires a human-in-the-loop workflow with explicit checkpoints to validate results. It outputs a governance-ready human-validation checklist because automated stratification cannot independently make demographic inferences.