# Qual Quant Triangulation

Reconcile what the numbers say with what users say, and design the study that settles...

- Repository: https://github.com/Owl-Listener/designer-skills (3196 stars)
- Category: Design
- License: MIT
- Last updated: 2026-09-05
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Owl-Listener/designer-skills --skill qual-quant-triangulation -g -a claude-code -y`
Codex: `npx skills add Owl-Listener/designer-skills --skill qual-quant-triangulation -g -a codex -y`
Cursor: `npx skills add Owl-Listener/designer-skills --skill qual-quant-triangulation -g -a cursor -y`
Gemini CLI: `npx skills add Owl-Listener/designer-skills --skill qual-quant-triangulation -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Owl-Listener/designer-skills`

## Description

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`.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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
Give each question to the source that can actually answer it, and stop asking the other:
- **What happened, how often, and where**, behavioural data. Interviews are a poor census; people misremember frequency badly.
- **Why, and what they were trying to do**, research. No amount of event data recovers intent.
- **Whether the thing is worth building at all**, neither, on its own. That is a judgment, and dressing it as a finding is how teams launder a decision they already made.
When someone asks a why-question of a dashboard, or

The rest: https://github.com/Owl-Listener/designer-skills/blob/HEAD/design-research/skills/qual-quant-triangulation/SKILL.md