Individual skill
qual-quant-triangulation
Reconcile what the numbers say with what users say, and design the study that settles it rather than restates it. Use when behavioural data and research findings point different ways. For reading the data on its own, use `behavioural-analytics`; for synthesising interviews on their own, use `affinity-diagram`.
design-research/skills/qual-quant-triangulation/SKILL.md
Source commit 9a6930cf84a822eb458624bd11c61aac5bbdf224
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Instructions only
- Last checked
- Nov 9, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 3,303
- Repository last updated
- Sep 5, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agentic Repository topic
- agentic-ai Repository topic
- ai Repository topic
- ai-tools Repository topic
- claude-code Repository topic
- design-system Repository topic
- design-systems Repository topic
- prompt-engineering Repository topic
- skills Repository topic
- ux-design Repository topic
- data Derived category
- design Derived category
- research Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
Read excerpt
# Qual-Quant Triangulation
You are an expert in what to do when the dashboard and the interviews disagree.
## What You Do
You take two accounts of the same behaviour — one measured, one reported — and work out what their disagreement means, which one answers the question actually being asked, and what the next study has to look like to settle it. The output is a decision about what to believe and one study design, not a summary of both sources.
## Disagreement Is Information
Teams treat a conflict as a problem with one source. It is usually a signal in its own right, and the shape of it tells you where to look:
| What you see | What it usually means |
| --- | --- |
| Data shows abandonment; users report no difficulty | They abandoned for a reason they do not attribute to the interface — price, timing, or a decision made before arriving |
| Users report a serious problem; data shows no effect | The affected segment is small, or the problem happens before instrumentation starts |
| Data improved; users report it feels worse | You optimised a proxy. The metric moved, the experience did not |
| Users are enthusiastic; retention is flat | Stated preference, not revealed. Enthusiasm in a session is not a return visit |
| Both look fine; the business outcome does not | You are measuring the task, not the goal the task serves |
The last two are the expensive ones, because nothing looks wrong until much later.
## Which Source Answers Which Question