Template Overview

Designing intuitive content architecture requires understanding how users categorize concepts in their own minds. The Card Sort Affinity Matrix Record documents participant groupings, category label variations, similarity coefficients, and final taxonomy recommendations in plain Markdown.

Markdown Template Structure

Copy the block below or click Download .md File to add this template directly to your personal vault:

---
title: "Card Sort Matrix: [Information Domain]"
date: [YYYY-MM-DD]
researcher: "[[Researcher Name]]"
study_type: "Open Card Sort / Closed Card Sort"
total_cards: 30
total_participants: 18
tags: [ux-research, card-sorting, affinity-matrix, information-architecture]
---

# Card Sort Affinity Matrix — [Information Domain]

## 1. Study Setup & Card Inventory
- **Objective:** Establish intuitive category boundaries for documentation and template libraries.
- **Card Sample:** 30 cards representing note types, configuration settings, and data workflows.

## 2. Category Clustering & Naming Variations
| Participant Proposed Category | Included Cards | Frequency Count | Recommended Standard Label |
|---|---|---|---|
| **Audio Processing** | Local Whisper, Microphone Input, Sample Rate | 16 / 18 (89%) | **Audio & Transcription** |
| **Meeting Templates** | Board Sync, 1-on-1, Standup, Post-Mortem | 18 / 18 (100%) | **Meeting Templates** |
| **Privacy & Security** | Encryption Key, Local Storage, No-Cloud Policy | 15 / 18 (83%) | **Security & Privacy** |
| **Vault Organization** | MOC, PARA, Zettelkasten, Tags | 14 / 18 (78%) | **Vault Structure** |

## 3. Co-Occurrence Affinity Matrix (Card Proximity)
| Card Pair | Joint Appearance | Proximity Score | Architectural Implication |
|---|---|---|---|
| **Whisper Models + Audio Input** | 17 / 18 | 94% | Keep in adjacent settings tabs |
| **Markdown Export + Git Integration** | 15 / 18 | 83% | Position export settings near version control |
| **Meeting Notes + Action Items** | 18 / 18 | 100% | Embed action item syntax directly in meeting scaffolds |

## 4. Ambiguous & Outlier Cards
- **Card 'Custom Vocabulary':** 8 participants placed under `Audio & Transcription`, while 7 placed under `Vault Structure`.
- **Resolution:** Place primarily in `Audio Settings` with contextual link from `Vault Settings`.

## 5. Final Recommended Taxonomy
> [!NOTE] Taxonomy Architecture
> The study strongly validates a four-pillar navigation structure: **Vault**, **Audio & Models**, **Templates**, and **Data & Export**.

## 6. Action Items
- [ ] Update documentation sidebar navigation to match four-pillar structure.
- [ ] Validate new taxonomy via closed tree testing study.

Filled Real-World Example

Here is a realistic example demonstrating how this template captures actual deliberations:

---
title: "Card Sort: Documentation Restructure"
date: 2026-10-06
researcher: "[[Marcus Vance]]"
study_type: "Open Card Sort"
total_cards: 24
total_participants: 16
tags: [ux-research, card-sort, documentation]
---

# Card Sort Affinity Matrix — Documentation Restructure

## 1. Study Overview
Marcus Vance conducted an open card sort with 16 software developers to redesign documentation navigation.

## 2. Strongest Clusters
100% of participants clustered local model configurations with audio hardware settings. 88% grouped CLI tool references with developer automation scripts.

## 3. Recommendations
The documentation hierarchy will merge 'Models' and 'Hardware' into a single 'Audio Engine' section, reducing sidebar depth by one tier.

When to Use and When to Avoid

When to Use

  • Use when reorganizing complex documentation hubs, settings screens, or template galleries.
  • Use to discover natural language terminology and category naming conventions from real users.
  • Use to build consensus across cross-functional product and design teams.

When to Avoid

  • Avoid using when navigation hierarchies are legally mandated or predetermined by API standards.
  • Avoid using to test visual typography or color palette choices.

On-Device AI Integration

Summarize your recording in Oyma with this local instruction:

Analyze the card sort co-occurrence data:
1. Identify cards with high clustering agreement (>80%) and recommend canonical category names.
2. Highlight split or contested cards and evaluate candidate parent categories.
3. Generate a finalized Markdown taxonomy table with clear category definitions.

Your data remains securely on your Mac with zero cloud exposure.

Frequently Asked Questions

What is the difference between open and closed card sorting?

Open card sorting lets participants create and name their own categories, while closed card sorting gives participants fixed predefined categories.

How does an affinity matrix help interpret card sort data?

An affinity matrix displays the co-occurrence frequency of cards, revealing natural conceptual groupings and outlier topics.

Can card sort matrices be formatted as Markdown tables?

Yes. This template uses clean Markdown grid tables that render clearly across all Markdown previewers and Git diffs.