Affinity Diagrams

Jiro Kawakita, Hassōhō (1967). The KJ method (Kawakita's initials, Japanese order). Bottom-up procedure for finding structure in qualitative items without imposing it beforehand.

When to use

• After volume generation (100+ ideas from Crazy 8s or brainwriting need clusters)
• Qualitative research synthesis (interview transcripts, ethnographic notes, observations)
• Requirements gathering (pile of user requests / bug reports / suggestions)
• Sense-making after a workshop (whiteboard full of stickies)
• Bottom-up taxonomy when no good existing one fits
• Diagnosing what's missing — gaps between clusters often reveal what the data set lacks

Don't use when

• Few items (under ~15 — overkill, hold them in mind instead)
• The right structure is already known (use deductive coding)
• Time pressure — done well takes hours
• Solo without enough cognitive distance from items (you'll produce the categories you'd have produced anyway)
• Highly quantitative data (use stats)

Procedure

1. Atomize items. One observation per card. Items must be self-contained, specific, comparable in granularity.
2. Make them physically separable. Sticky notes; index cards; or a shared canvas (Miro/Mural/FigJam). Free movement matters; a list in a doc doesn't work.
3. Spread out. Distribute across a flat surface. No structure yet.
4. Cluster silently. Each participant moves items into proximity with similar ones. Silently — talking shapes group thinking, defeats bottom-up. If two participants disagree on placement, duplicate the item and let it appear in both.
5. Continue until movement slows.
6. Name each cluster. Specific names ("requests for offline functionality"), not generic ("technical issues"). Resist generic names.
7. Look at orphans and gaps.
   • Orphans: items not fitting any cluster — often the most surprising data.
   • Gaps: spaces between clusters — suggest categories the data lacks (questions like "why didn't anyone mention X?").
   • Cluster sizes: very large = items not differentiated enough; very small = specialized concerns worth noting.
8. Look for relationships between clusters. Some depend on others. Some conflict.
9. Narrative test (Kawakita). Write a 1–2 paragraph narrative using the cluster names to tell a coherent story about the domain. If you can't, the clusters are misapprehension.

Worked example

50-person team brainwrites about "what would make the codebase more maintainable" — 108 raw ideas.

After 45 minutes silent clustering:

• Dependency hygiene (~22 items)
• Test coverage and CI speed (~18)
• Documentation drift (~14)
• Onboarding friction (~12)
• Implicit knowledge ("only Sara knows how X works") (~10)
• Tooling fragmentation (~9)
• Technical debt visibility (~8)
• Orphans (~15 — scattered specific concerns)

Gap: noticeably absent — almost no items about production reliability, security review, or cross-team API contracts. The team's perception of "maintainability" is internal-developer-facing; user-facing reliability is not surfaced.

Narrative: "Maintainability concerns cluster around (1) dependencies, (2) tests, (3) docs-code drift, with secondary concerns around onboarding and implicit knowledge. The team experiences maintainability as a developer-experience problem rather than a reliability problem."

The diagram has produced a map of perceived maintainability problems. Decisions about which to address require additional inputs (impact, cost, owner). But the map shows what the team thinks the problem is — and the gap is itself useful.

Anti-slop notes

• Fast affinity grouping that produces familiar categories = deductive coding pretending to be inductive. If the categories are the same as you'd have written before looking at the items, you've performed deductive coding.
• Don't generate fake observations to populate clusters.
• Avoid generic cluster names ("things to improve", "various concerns").
• Don't compress too aggressively. Real data has variable cluster sizes (5–25 typical); uniform sizes suggest forced grouping.
• Affinity diagrams are sense-making, not proof. Clusters represent the researcher's perception of items, not objective truth.
• For LLM-driven affinity grouping: models impose familiar taxonomies. After clustering, ask "what's the most surprising cluster?" If nothing surprising, redo or supplement with human eyes.

Source: Kawakita, Hassōhō (Chuko Shinsho, 1967, in Japanese). Mizuno (ed.), Management for Quality Improvement: The Seven New QC Tools (Productivity Press, 1988).