qual-characterize-patterns git:20261106.bb3d139A

Immutable. This exact content never changes and is served at /api/v1/blob/b6c75971989111e0.

---
name: qual-characterize-patterns
description: Develop dimensional analysis of patterns through systematic characterization
hooks:
  Stop:
    - hooks:
        - type: command
          command: node "${CLAUDE_PLUGIN_ROOT}/hooks/run-hook.js" post-phase-transition.js
          timeout: 10
          once: true
---

# Pattern Characterization Command

This command guides you through dimensional analysis - moving from "what patterns exist" to understanding "how patterns vary" and "under what conditions."

## Overview

Pattern characterization is where your categories become theory. You're not just labeling data; you're understanding the structure, variation, and relationships within your patterns.

## What is Dimensional Analysis?

Dimensional analysis asks:
- **Properties:** What are the defining characteristics of this pattern?
- **Dimensions:** How do these properties vary?
- **Conditions:** Under what circumstances do patterns appear/vary?
- **Consequences:** What follows from different dimensional profiles?
- **Relationships:** How do patterns relate to each other?

## The Characterization Process

### Step 1: Select a Pattern to Characterize

Start with your most robust patterns - those with:
- Strong empirical grounding (many quotes)
- Clear theoretical relevance
- Interesting variation in your data

### Step 2: Identify Properties

For your selected pattern, ask:
- What makes something an instance of this pattern?
- What features are definitional vs. incidental?
- How do informants describe this phenomenon?

Example:
```
Pattern: "Entrepreneurial Uncertainty Navigation"
Properties:
- Information ambiguity
- Decision urgency
- Resource constraints
- Emotional response
```

### Step 3: Map Dimensional Variation

For each property, identify how it varies:

```
Property: Information ambiguity
Dimension: Low ←→ High
Indicators:
- Low: "We had the data to make the decision"
- High: "We were flying blind"
```

### Step 4: Explore Conditions

Identify contextual factors that affect the pattern:

**Causal conditions:** What leads to this pattern?
**Contextual conditions:** What shapes how it manifests?
**Intervening conditions:** What moderates or mediates?

### Step 5: Trace Consequences

What outcomes follow from different dimensional profiles?

```
When [property] is [dimension], then [consequence]
Example: When uncertainty is high AND resources are low,
entrepreneurs resort to intuitive decision-making.
```

### Step 6: Map Pattern Relationships

How does this pattern relate to others in your framework?

- **Hierarchy:** Is this a subcategory of another pattern?
- **Sequence:** Does this pattern lead to or follow from others?
- **Association:** Do patterns tend to co-occur?
- **Contrast:** Are patterns mutually exclusive?

## Using Tools for Characterization

### Sequential Thinking
Use for systematic dimensional analysis:
> "Help me think through the properties and dimensions of [pattern]. What are its defining characteristics and how do they vary?"

### @dialogical-coder
Request dimensional evidence:
> "For pattern [X], show me quotes that represent different dimensional profiles. What variation exists in my data?"

### Lotus Wisdom
When dimensional boundaries are fuzzy:
> "This pattern seems to blur with [other pattern]. Help me think through whether these are dimensions of one pattern or distinct patterns."

## Documentation Template

For each characterized pattern:

```
PATTERN: [Name]
DEFINITION: [1-2 sentences]

PROPERTIES AND DIMENSIONS:
1. [Property 1]
   - Dimension: [low] ←→ [high]
   - Exemplar quotes: [quote at each end]

2. [Property 2]
   ...

CONDITIONS:
- Causal: [what brings this about]
- Contextual: [what shapes its form]
- Intervening: [what moderates effects]

CONSEQUENCES:
- When [dimensional profile], then [outcome]

RELATIONSHIPS:
- Related to [other patterns] via [type of relationship]

THEORETICAL SIGNIFICANCE:
- What does this pattern contribute to understanding?
```

## Quality Indicators

Your characterization is strong when:
- Dimensions are grounded in data (you have quotes for each)
- Variation is meaningful (not arbitrary gradations)
- Conditions are analytically distinct
- Consequences are traceable in your data
- Relationships clarify your emerging theory

## Common Challenges

### Challenge: Too Many Dimensions
**Response:** Which dimensions do theoretical work? Prioritize analytically significant variation.

### Challenge: Dimensions Overlap
**Response:** This may signal that properties aren't distinct. Consider collapsing or reconceptualizing.

### Challenge: Can't Find Variation
**Response:** Pattern may be constant in your sample. Note this as a boundary condition.

### Challenge: Relationships Are Unclear
**Response:** Use visual mapping; sometimes spatial arrangement reveals structure.

## Philosophical Note

Dimensional analysis is where grounded theory becomes dimensional theory. You're moving from description to explanation, from categories to relationships, from data to theory.

This is craft work. Take time with it.

## Next Steps

After pattern characterization:
1. Review your dimensional framework
2. Create visual representations (data structure tables, diagrams)
3. Prepare for Stage 3: Dialogue with Tradition
4. Use @scholarly-companion to examine theoretical contribution