- name
- document-analysis
- description
- Use when analyzing documents, artifacts, policies, or media as qualitative data sources.
# Document and Artifact Analysis
Documents include policies, emails, meeting minutes, curricula, clinical records (where permitted), social media threads, photographs, videos, and organizational charts. In qualitative research, documents are not “background”; they are potential **primary data** when your questions concern texts, practices encoded in artifacts, or institutional meaning-making.
## Types of documents
- **Public records:** laws, reports, websites—often accessible but require contextual interpretation.
- **Personal documents:** diaries, letters, portfolios—high intimacy; consent and harm considerations intensify.
- **Organizational documents:** SOPs, training decks, KPI dashboards—reveal formal and informal priorities when read critically.
- **Media and digital traces:** news, forums, apps—raise provenance, bot, and archival volatility issues.
Classify each document by **authorship, audience, purpose, and genre** before heavy interpretation.
## Authentication and contextualization
Ask: Who produced this, when, under what constraints, and for whom? Triangulate with interviews or observations when documents conflict with practice (“official story” vs “work-as-done”). Note redactions, version control gaps, and translation effects if documents cross languages.
## Content analysis basics (qualitative orientation)
Qualitative content analysis codes meaning units into categories while preserving context. Steps typically include: define corpus boundaries; sample or include all relevant texts; define recording units (paragraph, post, case note segment); develop codes inductively, deductively, or hybrid; maintain a codebook with examples; iterate definitions as patterns clarify.
## Discourse analysis basics
Discourse-oriented reading examines how language constructs subjects, objects, and power relations. Useful prompts: What is presupposed? Who is included/excluded? What metaphors recur? What silences matter? Align discourse analysis commitments (e.g., critical, Foucauldian, discursive psychology) with your paradigm statement.
## Coding documents in a grounded theory framework
Treat a document segment as an **incident**: compare segment to segment and segment to emerging concepts. Avoid letting document headings dictate your categories if the data pushes elsewhere. Keep a memo trail for interpretive leaps (e.g., why a policy clause signals “risk shifting” as a category).
## “All is data” and critical reading
GT’s “all is data” does not mean naive acceptance. Documents can misrepresent, lag reality, or perform legitimacy. Critical reading asks what the text *does* socially while still mining it for **participant/informant meanings** embedded in wording and format.
## Practical workflow
1. Inventory documents with metadata (source, date, access path).
2. Select chunks relevant to the evolving theory; avoid infinite corpus sprawl.
3. Apply consistent naming for files and excerpts used in publications.
4. Link excerpts to audit trail entries (why this passage matters analytically).
## Checklist
- [ ] Corpus boundaries and inclusion criteria documented.
- [ ] Provenance, authorship, and audience analyzed.
- [ ] Coding approach matches methodology (GT openness vs structured content analysis).
- [ ] Potential bias/misrepresentation flagged and triangulated where possible.
- [ ] Confidentiality and copyright constraints respected.
## References (starting points)
- Bowen, G. A. (2009). Document analysis as a qualitative research method. *Qualitative Research Journal*.
- Prior, L. *Using documents in social research*.
- Glaser, B. G. *Doing Grounded Theory* — documents as comparable incidents.
## Quick reference
**Before coding a page:** note genre, intended audience, and production constraints (who could edit this, and why it exists).
**While coding:** treat each excerpt as an incident; compare to interviews/observations when interpretations hinge on “official vs practiced” gaps.
**In write-ups:** distinguish textual claims from your interpretation; flag uncertain authorship or missing pages rather than smoothing gaps silently.
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