qualitative-research

name	qualitative-research
description	Use when conducting customer discovery interviews, user research, surveys, focus groups, or observational research requiring rigorous analysis - provides systematic 6-phase framework with mandatory bias prevention (reflexivity, intercoder reliability, disconfirming evidence search) and reproducible methodology; peer to hypothesis-testing for qualitative vs quantitative validation

Qualitative Research

Overview

Systematic framework for conducting and analyzing qualitative research (interviews, surveys, focus groups, observations) with rigorous bias prevention and reproducible methodology.

Core principle: Rigor through mandatory checkpoints. Prevent confirmation bias by enforcing disconfirming evidence search, intercoder reliability, and reflexivity documentation.

Peer to hypothesis-testing: hypothesis-testing validates quantitative hypotheses with data analysis. qualitative-research validates qualitative hypotheses with systematic interview/survey analysis.

When to Use

Use this skill when:

• Conducting customer discovery interviews to validate demand
• Running user research to understand pain points
• Analyzing survey responses for themes and patterns
• Conducting focus groups or observational research
• ANY qualitative data collection and analysis requiring rigorous, reproducible methodology

When NOT to use:

• Quantitative data analysis (use hypothesis-testing instead)
• Casual conversations or informal feedback (not systematic research)
• Literature review or secondary research (use internet-researcher agent)

Mandatory Process Structure

YOU MUST use TodoWrite to track progress through all 6 phases.

Create todos at the start:

- Phase 1: Research Design (question, method, instrument, biases) - pending
- Phase 2: Data Collection (execute protocol, track saturation) - pending
- Phase 3: Data Familiarization (immerse without coding) - pending
- Phase 4: Systematic Coding (codebook, reliability check) - pending
- Phase 5: Theme Development (build themes, search disconfirming evidence) - pending
- Phase 6: Synthesis & Reporting (findings, limitations, follow-ups) - pending

Update status as you progress. Mark phases complete ONLY after checkpoint verification.

Flexible Entry: If user has existing data (transcripts, survey responses), can start at Phase 3. Verify raw data exists in raw-data/ directory.

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Phase 1: Research Design

CHECKPOINT: Before proceeding to Phase 2, you MUST have:

• Research question defined (specific, testable)
• Qualitative method selected (interview/survey/focus group/observation)
• Collection instrument created (interview guide, survey questions, protocol)
• Sampling strategy documented (who, how many, recruitment)
• Reflexivity baseline documented (YOUR assumptions and biases written down)
• Saved to 01-research-design.md

Instructions

1. Select method and load appropriate template:

   • Interview → Use templates/interviews/phase-1-interview-guide.md
   • Survey → Use templates/surveys/phase-1-survey-design.md
   • Focus Group → Use templates/focus-groups/phase-1-facilitator-guide.md
   • Observation → Use templates/observations/phase-1-observation-protocol.md

2. Document reflexivity baseline (MANDATORY):

This is NON-NEGOTIABLE. Before any data collection, write down:

• What you believe the answer will be
• What assumptions you're making
• What biases you bring (industry experience, expert opinions, prior hypotheses)
• What would surprise you

Why this matters: If you don't document biases BEFORE data collection, you cannot identify confirmation bias AFTER.

3. Create neutral questions (use template guidance):

Templates enforce neutral question design. Common mistakes:

• Leading: "How much would you pay for X?" (assumes they want X)
• Neutral: "How do you currently solve Y problem?" (explores actual behavior)

4. Plan adequate sample size:

• Interviews: Minimum 8-10 for saturation monitoring
• Surveys: Depends on question type and analysis goals
• Focus groups: 3-5 groups minimum
• Observations: Plan for 10-20 observation sessions

5. Save to 01-research-design.md using template

6. STOP and verify checkpoint: Cannot proceed to Phase 2 until reflexivity baseline documented.

Common Rationalization: "I don't have biases to document"

Why this is wrong: Everyone has assumptions. If you can't name them, they're controlling you invisibly.

Do instead: Write one sentence: "I believe [X] because [Y]." That's your bias. Document it.

Common Rationalization: "Expert opinion reduces need for bias documentation"

Why this is wrong: Expert opinion IS a bias that must be documented. Authority backing is a strong prior.

Do instead: "Expert A said B. This is my assumption going in. Must verify with data."

Common Rationalization: "Time pressure means I can't do formal process"

Why this is wrong: Documenting assumptions takes 5 minutes. Presenting biased findings wastes hours.

Do instead: Set timer for 5 minutes. Write down assumptions. Move on.

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Phase 2: Data Collection

CHECKPOINT: Before proceeding to Phase 3, you MUST have:

• Minimum sample collected (Phase 1 plan executed)
• Saturation monitoring documented
• All raw data captured (transcripts, responses, field notes)
• Raw data files in raw-data/ directory
• Reflexive journal maintained during collection
• Saved to 02-data-collection-log.md

Instructions

1. Execute method-specific protocol:

   • Use Phase 2 template for your selected method
   • Maintain consistency (same questions, same facilitator when possible)
   • Document context for each data collection instance

2. Track toward saturation:

Saturation = when new insights stop emerging

After each interview/session/survey batch, ask:

• Did this reveal new themes I hadn't seen?
• Or was this reinforcing existing patterns?

Document in collection log. Plan to continue until 2-3 consecutive instances add nothing new.

3. Maintain reflexive journal (MANDATORY):

After each data collection instance, write:

• What surprised you
• What confirmed your assumptions
• What contradicted your expectations
• How your thinking is evolving

Why this matters: Reflexivity tracks how your interpretation changes. Prevents retroactively fitting data to initial beliefs.

4. Create raw data files:

File structure:

raw-data/
├── transcript-001.md
├── transcript-002.md
├── ...

OR for surveys:

raw-data/
├── survey-responses-batch-1.md
├── survey-responses-batch-2.md

One file per interview/session. Numbered sequentially.

5. Save collection log to 02-data-collection-log.md

6. STOP and verify checkpoint: Cannot proceed to Phase 3 until minimum sample collected and raw data captured.

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Phase 3: Data Familiarization

CHECKPOINT: Before proceeding to Phase 4, you MUST have:

• All raw data read/reviewed multiple times
• Initial observations documented (NOT codes, just observations)
• Surprising findings noted (contradictions to assumptions)
• Reflexivity updated (how understanding evolved)
• Saved to 03-familiarization-notes.md

Instructions

1. Read ALL data without coding:

This is critical: Do NOT start coding yet. Just read and observe.

Why: Premature coding locks you into first impressions. Familiarization lets patterns emerge naturally.

2. For large datasets (10+ interviews), use analyze-transcript agent:

Invoke: analyze-transcript agent
Input: transcript-001.md through transcript-010.md
Output: Summary, key quotes, initial observations per transcript

Agent prevents context pollution. Returns structured observations for your review.

3. Document observations in 03-familiarization-notes.md:

Format:

• Initial patterns noticed (not themes yet - just "I see X coming up")
• Surprising findings ("I expected A but saw B")
• Questions emerging ("Why did 3 people mention Y?")
• Reflexive notes ("This contradicts my assumption that...")

4. STOP and verify checkpoint: Cannot proceed to Phase 4 until all data reviewed and surprises documented.

Common Rationalization: "I can code while familiarizing to save time"

Why this is wrong: Coding while familiarizing locks you into first impressions. Patterns shift after full dataset review.

Do instead: Finish familiarization completely. Then start fresh with coding.

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Phase 4: Systematic Coding

CHECKPOINT: Before proceeding to Phase 5, you MUST have:

• Codebook complete (definitions, inclusion/exclusion criteria, examples)
• Entire dataset coded systematically
• Intercoder reliability check completed (10-20% sample)
• Agreement percentage documented
• Audit trail of all coding decisions
• Saved to 04-coding-analysis.md

Instructions

1. Develop initial codebook using agent:

Invoke: generate-initial-codes agent
Input: 2-3 transcripts or data segments
Output: Suggested codes with definitions and examples

Review agent suggestions. Refine codes. Create codebook.

2. Codebook structure (MANDATORY):

For each code:

• Name: Short label
• Definition: What this code means
• Inclusion criteria: When to apply this code
• Exclusion criteria: When NOT to apply
• Examples: 2-3 data extracts demonstrating code

3. Code all data systematically:

Work through raw data files sequentially. Apply codes from codebook. Document any new codes discovered (add to codebook with rationale).

4. Intercoder reliability check (MANDATORY - NON-NEGOTIABLE):

Invoke: intercoder-reliability-check agent
Input: Codebook + 2 transcripts (10-20% of dataset)
Output: Independent coding + agreement analysis

This step is REQUIRED. Cannot skip. Cannot defer. Cannot substitute with user review.

Why: Even clear codebooks have subjective judgment. Second coder catches systematic bias in code application.

5. Document in 04-coding-analysis.md:

Sections:

• Section 1: Codebook (all codes with definitions and examples)
• Section 2: Coding Process (how you applied codes, any refinements)
• Section 3: Intercoder Reliability (agent results, agreement %, disagreement resolution)
• Section 4: Audit Trail (all coding decisions documented)

6. STOP and verify checkpoint: Cannot proceed to Phase 5 without intercoder reliability check COMPLETED and documented.

Common Rationalization: "Coding was straightforward, low risk of errors"

Why this is wrong: "Straightforward" is subjective. Even clear codes have interpretation variance.

Do instead: If coding is straightforward, intercoder reliability will be high and quick. Do the check.

Common Rationalization: "Time constraints justify skipping verification"

Why this is wrong: Presenting flawed findings takes more time to fix than 1-hour verification.

Do instead: Verification takes 1 hour. Fixing flawed findings after presentation takes days. Do the math.

Common Rationalization: "User reviewed coding, that's enough validation"

Why this is wrong: User can't catch their own interpretation bias. Second coder does.

Do instead: User review is pre-flight check. Intercoder reliability is the actual test. Both required.

Common Rationalization: "Can do reliability check later if needed"

Why this is wrong: After themes developed, reliability check invalidates hours of work if problems found.

Do instead: Reliability MUST be verified in Phase 4, not Phase 6. Do it now.

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Phase 5: Theme Development & Refinement

CHECKPOINT: Before proceeding to Phase 6, you MUST have:

• Themes defined with supporting codes
• Disconfirming evidence search completed (MANDATORY for ALL themes)
• Negative cases explained (data that doesn't fit themes)
• Themes refined based on full dataset review
• Verbatim data extracts supporting each theme
• Saved to 05-theme-development.md

Instructions

1. Group codes into potential themes using agent:

Invoke: identify-themes agent
Input: Codebook + all coded segments
Output: Potential themes with supporting codes and data extracts

Review agent suggestions. Refine theme definitions.

2. Disconfirming evidence search (MANDATORY - NON-NEGOTIABLE):

For EACH theme, you MUST run:

Invoke: search-disconfirming-evidence agent
Input: Theme definition + full dataset
Output: Contradictory evidence, edge cases, exceptions to pattern

This is REQUIRED. No exceptions. No shortcuts. No "pattern is obvious so no need."

Why: Clear patterns are MOST vulnerable to confirmation bias. Obvious themes need MOST rigorous verification.

3. Document negative cases:

For each theme, explain:

• How many participants DON'T fit this theme?
• What did those participants say instead?
• Why doesn't the theme apply to them?
• Is there a boundary condition (theme applies only in specific contexts)?

Example:

Theme 1: "Cost concerns are primary barrier" - 8 of 10 participants

NEGATIVE CASES:
- Participant 3: Didn't mention cost. Focused entirely on integration complexity.
- Participant 7: Said price was "not a concern if it solves the problem"

EXPLANATION: Theme applies to majority but not universal. Subset willing to pay premium for right solution.

4. Refine themes based on disconfirming evidence:

After seeing contradictions, revise theme definitions for accuracy. "8 of 10" is more honest than "all participants."

5. Extract supporting quotes using agent:

Invoke: extract-supporting-quotes agent
Input: Theme definition + coded dataset
Output: Best representative verbatim quotes for each theme

6. Document in 05-theme-development.md:

Format:

• Theme name and definition
• Supporting codes
• Prevalence (X of Y participants)
• Verbatim quotes (use extract-supporting-quotes agent output)
• Disconfirming evidence (from search-disconfirming-evidence agent)
• Negative case explanation

7. STOP and verify checkpoint: Cannot proceed to Phase 6 without disconfirming evidence search for ALL themes.

Common Rationalization: "Themes are clearly supported by majority of participants"

Why this is wrong: Majority agreement doesn't eliminate contradictory evidence. Must explain ALL data.

Do instead: "8 of 10 mentioned cost. What about the 2 who didn't? Must explain."

Common Rationalization: "Expert prediction validates findings"

Why this is wrong: Expert prediction + matching findings = confirmation bias red flag, not validation.

Do instead: When predictions match findings perfectly, search HARDEST for contradictions.

Common Rationalization: "High consistency (8/10, 9/10) indicates robust themes"

Why this is wrong: High unanimity can indicate leading questions or selective interpretation.

Do instead: Real customer sentiment is messy. 9/10 agreement deserves scrutiny, not celebration.

Common Rationalization: "Disconfirming evidence search unnecessary when pattern is obvious"

Why this is wrong: Obvious patterns are MOST vulnerable to confirmation bias.

Do instead: Obvious patterns require MOST rigorous disconfirmation. Search is mandatory.

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Phase 6: Synthesis & Reporting

CHECKPOINT: Before marking complete, you MUST have:

• Findings documented with verbatim quotes for each theme
• Limitations explicitly stated (sample, method, researcher bias, context)
• Confidence assessment (credibility, dependability, confirmability, transferability)
• 2-3 follow-up research questions identified
• Overview updated with final summary
• Saved to 06-findings-report.md and 00-overview.md updated

Instructions

1. Write findings report:

Structure:

• Main Findings: Each theme with supporting quotes
• Prevalence: Honest reporting (X of Y, not "all" or "most")
• Negative Cases: Exceptions explained
• Context: When/where does this apply?

2. Document limitations (MANDATORY - be HONEST):

You MUST address:

• Sample limitations (size, homogeneity, recruitment source)
• Method constraints (interviews vs. observations, question design)
• Researcher bias (documented in Phase 1, how it may have influenced)
• Context limitations (geography, time period, industry)

Why: Acknowledging limitations STRENGTHENS credibility. False certainty undermines trust.

3. Assess confidence (trustworthiness criteria):

• Credibility: Do findings accurately represent participant experiences?
• Dependability: Would another researcher reach similar conclusions?
• Confirmability: Are findings based on data, not researcher bias?
• Transferability: Do findings apply beyond this specific sample?

Rate each: High / Medium / Low. Provide justification.

4. Identify 2-3 follow-up questions:

Every analysis should raise new questions:

• What would you investigate next?
• What surprised you that needs deeper exploration?
• What would strengthen confidence in findings?

5. Update 00-overview.md with summary:

Add final summary section with:

• Main findings (3-5 bullet points)
• Signal classification (if invoked by marketing-experimentation): Positive/Negative/Null/Mixed
• Confidence level
• Follow-up recommendations

6. Save to 06-findings-report.md

7. Mark Phase 6 complete: All checkpoints verified.

Common Rationalization: "Limitations will undermine findings, downplay them"

Why this is wrong: Stating limitations INCREASES credibility. Readers trust honest uncertainty.

Do instead: State limitations clearly. Be honest about what you don't know.

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Common Rationalizations - STOP

These are violations of skill requirements:

Excuse	Reality
"I don't have biases to document"	Everyone has assumptions. If you can't name them, they're controlling you invisibly.
"Expert opinion reduces need for bias documentation"	Expert opinion IS a bias. Authority backing is a strong prior that MUST be documented.
"Time pressure justifies skipping formal process"	Documenting assumptions takes 5 minutes. Presenting biased findings wastes hours.
"Coding was straightforward, low risk"	"Straightforward" is subjective. Even clear codes have interpretation variance.
"Time constraints justify skipping verification"	Verification takes 1 hour. Fixing flawed findings after presentation takes days.
"Informal spot-check is sufficient"	Spot-checks catch obvious errors. Intercoder reliability catches systematic bias. Both required.
"User reviewed coding, enough validation"	User can't catch their own interpretation bias. Second coder does. Non-negotiable.
"Can do reliability check later if needed"	After themes developed, reliability check invalidates hours of work. Do it in Phase 4.
"Themes clearly supported by majority"	Majority agreement doesn't eliminate contradictory evidence. Must explain ALL data.
"Expert prediction validates findings"	When predictions match findings perfectly, that's when to search hardest for contradictions.
"High consistency (8/10, 9/10) indicates robustness"	Real customer sentiment is messy. 9/10 agreement deserves scrutiny.
"Disconfirming evidence search unnecessary for obvious patterns"	Obvious patterns MOST vulnerable to confirmation bias. Search is mandatory.
"Limitations undermine findings"	Stating limitations INCREASES credibility. False certainty undermines trust.
"This is just initial/exploratory research"	Exploratory means open-ended questions. Doesn't mean skip rigor. Follow the phases.
"I'm following the spirit of the rules"	Violating checkpoints violates both letter AND spirit. No shortcuts.

All of these mean: Checkpoint violated. Cannot proceed.

Red Flags - STOP

If you catch yourself thinking ANY of these, you are rationalizing. STOP and follow the checkpoint:

• "I recommend..." (should be "You MUST...")
• "Would you like to..." (should be "Cannot proceed without...")
• "This is optional" (critical steps are MANDATORY)
• "Spot-check" instead of "intercoder reliability check"
• "I'll look for contradictions" instead of "Invoking search-disconfirming-evidence agent"
• "This is just initial validation" (rigor required at all stages)
• "Expert backing reduces need for X" (authority is bias, must be documented)
• "Pattern is obvious" (obvious patterns need MOST rigorous verification)
• "Can skip X and do it later" (checkpoints are mandatory NOW, not later)

All of these mean: Violated skill requirements. Go back and complete checkpoint.

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Summary

This skill ensures rigorous, reproducible qualitative research by:

1. Preventing confirmation bias: Reflexivity baseline, neutral questions, disconfirming evidence search
2. Ensuring systematic analysis: Codebook rigor, intercoder reliability, audit trails
3. Enforcing checkpoints: Cannot skip critical steps (reflexivity, reliability, disconfirmation)
4. Using agent-based methods: Sub-agents handle data-intensive operations, prevent context pollution
5. Demanding intellectual honesty: Explicit limitations, confidence assessment, honest prevalence reporting

Follow this process and you'll produce defensible, credible qualitative research that stands up to scrutiny.