Code a short transcript
Use provisional labels, keep the supporting passage beside each code, and record why the label matters.
Qualitative methods
Qualitative data analysis tutorials grounded in inspectable examples.
Learn the manual reasoning first, then see where software and bounded AI assistance reduce mechanical work without taking over interpretation.
Step-by-step guides
Start with the task in front of you.
how to code interview transcripts
How to code interview transcripts
Move from raw interview text to open codes, codebook rules, memos, and reviewable software assistance.
how to do thematic analysisHow to do thematic analysis
Work through familiarization, coding, theme development, review, naming, and reporting without treating AI as the analyst.
qualitative coding examplesQualitative coding examples
Inspect open, axial, in vivo, and thematic codes against the exact excerpts that support them.
Learning path
Keep evidence visible at every stage.
Turn labels into a codebook
Add definitions, inclusion and exclusion rules, examples, and memo prompts before scaling to more interviews.
Develop and test themes
Review candidate themes against coded excerpts, contradictions, and the research question before writing a claim.
What qualitative data analysis asks you to preserve
Qualitative analysis moves between close reading and higher-level interpretation. A useful workflow preserves both directions: a code can be traced to the passage that supports it, and a passage can be revisited after the codebook or research question changes. The tutorials here keep the excerpt, code, memo, and theme close enough that each analytic step can be inspected.
Software can retrieve coded passages, manage definitions, and reduce repetitive organization. AI can propose labels or groups. Neither decides what the material means for the study. Use the examples to practice the judgment that remains with the researcher, then use the sandbox to inspect how suggested work stays separate from confirmed evidence.
OpenVerbatim entity
What OpenVerbatim is.
OpenVerbatim is an open-source (Apache-2.0) qualitative data analysis platform for coding and analyzing interview transcripts. AI-suggested codes stay marked as suggestions until a human reviewer confirms or rejects them, and every decision is kept in an audit trail. The full feature set is available when self-hosted; there is no paid feature wall.
Try the evidence loop
Review the workflow before you commit your own data.
OpenVerbatim's public sandbox runs in the browser with generated demo material, so researchers can inspect the review loop without creating an account.