SKILL.md
---
name: e2
description: |
Agent E2 - Qualitative Coding Specialist - Systematic coding and theme development.
Covers codebook development, coding strategies, saturation assessment, and CAQDAS guidance.
version: "12.0.1"
---
## ⛔ Prerequisites (v8.2 — MCP Enforcement)
`diverga_check_prerequisites("e2")` → must return `approved: true`
If not approved → AskUserQuestion for each missing checkpoint (see `.claude/references/checkpoint-templates.md`)
### Checkpoints During Execution
- 🟠 CP_CODING_APPROACH → `diverga_mark_checkpoint("CP_CODING_APPROACH", decision, rationale)`
- 🟠 CP_THEME_VALIDATION → `diverga_mark_checkpoint("CP_THEME_VALIDATION", decision, rationale)`
### Fallback (MCP unavailable)
Read `.research/decision-log.yaml` directly to verify prerequisites. Conversation history is last resort.
---
# E2: Qualitative Coding Specialist
## Role
Expert in systematic qualitative data coding, codebook development, theme identification, and saturation assessment. Guides researchers through rigorous coding processes for thematic analysis, grounded theory, and content analysis.
## Core Capabilities
### 1. Codebook Development Approaches
#### Deductive (A Priori) Coding
**When to Use:**
- Literature-driven research
- Theory-testing studies
- Structured content analysis
- Pre-defined frameworks (e.g., SDT, TPB)
**Process:**
```yaml
deductive_coding:
step_1_literature_review:
action: "Extract key constructs from theoretical framework"
output: "Initial code list with definitions"
step_2_operationalization:
action: "Define codes with inclusion/exclusion criteria"
output: "Structured codebook"
step_3_pilot_coding:
action: "Test codebook on 10-20% of data"
output: "Refined codebook"
step_4_reliability_check:
action: "Calculate inter-rater reliability (Kappa)"
output: "Reliability metrics, codebook adjustments"
```
**Example Deductive Codebook (Self-Determination Theory):**
```yaml
code: "autonomy_support"
definition: "Teacher actions that support student self-direction and choice"
when_to_use:
- "Teacher offers choices"
- "Teacher solicits student input"
- "Teacher acknowledges feelings"
when_not_to_use:
- "Teacher gives commands without rationale"
- "Generic praise without choice element"
example_quotes:
- "The teacher said 'you can choose to work alone or in pairs'"
- "She asked us what topics we wanted to explore"
related_codes: ["autonomy_thwarting", "intrinsic_motivation"]
parent_theme: "motivational_climate"
```
#### Inductive (Emergent) Coding
**When to Use:**
- Exploratory research
- Phenomenological studies
- Grounded theory
- Under-researched phenomena
**Process:**
```yaml
inductive_coding:
phase_1_open_coding:
approach: "Line-by-line, no preconceptions"
output: "100-200 initial codes"
phase_2_axial_coding:
approach: "Group codes by similarity, identify patterns"
output: "30-50 focused codes"
phase_3_selective_coding:
approach: "Identify core categories and relationships"
output: "8-15 themes with subthemes"
```
**Example Inductive Code Evolution:**
```yaml
evolution:
open_codes:
- "student_mentions_chatbot_patience"
- "student_appreciates_no_judgment"
- "student_feels_safe_making_errors"
focused_code: "psychological_safety"
theme: "non-judgmental_learning_environment"
definition: "Learners perceive AI chatbot as safe space for practice without fear of negative evaluation"
```
#### Hybrid (Deductive + Inductive)
**Best Practice for Social Science:**
```yaml
hybrid_approach:
step_1: "Start with literature-derived codes (deductive)"
step_2: "Remain open to emergent codes (inductive)"
step_3: "Track code sources (deductive vs. emergent)"
step_4: "Report both a priori and emergent themes"
example:
deductive_codes: ["engagement", "motivation", "self-efficacy"]
emergent_codes: ["technical_frustration", "privacy_concern", "gamification_appeal"]
```
### 2. Coding Strategies by Methodology
#### Thematic Analysis (Braun & Clarke, 2006)
**Six-Phase Process:**
```yaml
phase_1_familiarization:
activities:
- "Read and re-read entire dataset"
- "Note initial ideas and patterns"
- "Highlight interesting passages"
tools: ["Annotation software", "Memo writing"]
output: "Annotated transcripts, research journal notes"
time_estimate: "20-30% of total coding time"
phase_2_initial_coding:
activities:
- "Systematic line-by-line coding"
- "Create code labels"
- "Organize data extracts by code"
tools: ["CAQDAS", "Excel", "Index cards"]
output: "Initial codebook (50-150 codes typical)"
quality_check:
- "Every data item coded"
- "Data extracts retain context"
- "Codes are specific enough to be meaningful"
phase_3_theme_searching:
activities:
- "Collate codes into potential themes"
- "Mind mapping of relationships"
- "Create theme tables"
techniques:
- "Post-it note sorting"
- "Mind maps"
- "Thematic tables"
output: "Candidate themes (8-15 typical)"
phase_4_theme_review:
level_1_review:
action: "Check themes against coded data extracts"
criteria: "Internal homogeneity (coherence within theme)"
level_2_review:
action: "Check themes against entire dataset"
criteria: "External heterogeneity (distinction between themes)"
output: "Refined themes, thematic map"
phase_5_theme_defining:
activities:
- "Name each theme"
- "Write theme descriptions (2-3 paragraphs)"
- "Identify essence of each theme"
- "Define subthemes if needed"
output:
- "Final theme definitions"
- "Thematic structure"
quality_criteria:
- "Theme names are concise and informative"
- "Definitions capture unique contribution"
- "No significant overlap between themes"
phase_6_report_writing:
activities:
- "Select vivid quotations"
- "Write analytic narrative"
- "Link themes to research question"
- "Situate findings in literature"
output: "Findings section with theme-based structure"
```
**Thematic Analysis Quality Checklist:**
```yaml
quality_criteria:
data_engagement:
- "[ ] Transcripts read multiple times"
- "[ ] Coding checked against transcripts"
- "[ ] Themes grounded in data extracts"
coding_rigor:
- "[ ] Each data item coded"
- "[ ] Coding systematic and thorough"
- "[ ] Similar codes collated"
theme_coherence:
- "[ ] Themes internally consistent"
- "[ ] Themes distinct from each other"
- "[ ] Thematic structure logical"
transparency:
- "[ ] Coding process described"
- "[ ] Code-to-theme process explained"
- "[ ] Sufficient quotations provided"
```
#### Grounded Theory (Charmaz, 2006)
```yaml
grounded_theory_coding:
phase_1_initial_coding:
approach: "Open coding - line-by-line analysis"
coding_style:
- "Use gerunds (verbs ending in -ing)"
- "Stay close to data"
- "Avoid premature interpretation"
example:
data: "I felt nervous talking to real people, but the chatbot didn't judge me"
codes:
- "feeling_nervous_with_humans"
- "perceiving_chatbot_as_non_judgmental"
- "comparing_human_vs_AI_interaction"
phase_2_focused_coding:
approach: "Select most frequent/significant codes"
activities:
- "Synthesize initial codes"
- "Test codes against data"
- "Develop categories"
example:
initial_codes: ["feeling_anxious", "fearing_judgment", "avoiding_speaking"]
focused_code: "social_anxiety_in_language_learning"
phase_3_axial_coding:
approach: "Identify relationships between categories"
framework:
conditions: "When/why category occurs"
actions_interactions: "How people respond"
consequences: "What happens as result"
example:
category: "chatbot_psychological_safety"
conditions: "High speaking anxiety + fear of peer judgment"
actions: "Increased practice with AI, risk-taking in language use"
consequences: "Gradual confidence building"
phase_4_theoretical_coding:
approach: "Integrate categories into theory"
output: "Core category + theoretical model"
example:
core_category: "scaffolded_confidence_development"
theoretical_model: "AI → Safe practice → Risk-taking → Competence → Human interaction"
```
**Grounded Theory Memos:**
```yaml
memo_types:
code_memo:
purpose: "Define and elaborate codes"
example: |
Memo: "Perceiving chatbot as non-judgmental"
Date: 2024-10-15
This code captures participants' descriptions of chatbots as lacking
evaluative judgment. Unlike human interlocutors, AI doesn't show
disappointment, frustration, or impatience. This perception creates
psychological safety.
Properties:
- Non-verbal judgment absent (no eye-rolling, sighs)
- Consistent tone regardless of errors
- No social comparison with peers
Related codes: "social_anxiety", "fear_of_negative_evaluation"
theoretical_memo:
purpose: "Develop conceptual relationships"
example: |
Theoretical Memo: Anxiety-Safety-Practice Loop
Emerging pattern: High speaking anxiety → Preference for AI practice
→ Increased practice volume → Gradual confidence → Willingness to
speak with humans.
This suggests AI serves as transitional object/space for anxious
learners. Not replacement for human interaction but scaffold toward it.
operational_memo:
purpose: "Track methodological decisions"
example: |
Operational Memo: Saturation assessment
After 18 interviews, no new codes emerging for "chatbot affordances"
category. Last 3 interviews yielded only variations on existing codes.
Consider saturation reached for this category.
```
#### Content Analysis (Descriptive + Interpretive)
```yaml
content_analysis_coding:
manifest_content:
definition: "Surface-level, visible content"
approach: "Objective, countable"
examples:
- "Frequency of 'chatbot' mentions"
- "Number of positive vs. negative adjectives"
- "Presence/absence of specific themes"
reliability: "High inter-rater reliability possible (Kappa > 0.80)"
latent_content:
definition: "Underlying meaning, interpretive"
approach: "Subjective, inferential"
examples:
- "Implicit attitudes toward AI"
- "Underlying emotional tone"
- "Power dynamics in human-AI interaction"
reliability: "More challenging (Kappa 0.60-0.80 acceptable)"
coding_units:
word_level: "Individual words (e.g., AI, anxiety, practice)"
phrase_level: "Meaningful phrases (e.g., 'felt less judged')"
sentence_level: "Complete thoughts"
paragraph_level: "Thematic segments"
document_level: "Whole interview"
```
### 3. Code Quality Criteria
**High-Quality Code Entry Template:**
```yaml
code_template:
code_name: "clear_descriptive_name"
definition:
conceptual: "Abstract definition of construct"
operational: "How it manifests in data"
when_to_use:
inclusion_criteria:
- "Criterion 1"
- "Criterion 2"
boundary_conditions: "Where code applies"
when_not_to_use:
exclusion_criteria:
- "What this code is NOT"
- "Common misapplications"
edge_cases: "Ambiguous situations"
example_quotes:
typical_examples:
- "Quote 1 [Participant 3, Line 45]"
- "Quote 2 [Participant 7, Line 112]"
boundary_examples:
- "Borderline case [P5, L89] - coded because..."
counter_examples:
- "Quote that seems similar but isn't [P2, L34] - not coded because..."
related_codes:
parent_code: "Higher-level category"
sibling_codes: ["Related codes at same level"]
child_codes: ["More specific sub-codes"]
code_metadata:
date_created: "2024-10-15"
created_by: "Researcher initials"
source: "deductive/inductive"
frequency: "Number of times applied"
```
**Example High-Quality Codebook Entry:**
```yaml
code_name: "perceived_judgment_anxiety"
definition:
conceptual: "Psychological discomfort arising from anticipation of negative evaluation by others during language production"
operational: "Participant explicitly mentions fear, worry, nervousness, or discomfort about being judged, evaluated, or criticized by others when speaking"
when_to_use:
inclusion_criteria:
- "Explicit mention of judgment, evaluation, or criticism from others"
- "Affective states (fear, anxiety, nervousness) linked to social evaluation"
- "Comparisons between human vs. AI interaction where judgment is factor"
boundary_conditions: "Must be specific to language speaking context, not general social anxiety"
when_not_to_use:
exclusion_criteria:
- "Generic nervousness without reference to being judged"
- "Task difficulty anxiety (not social evaluation)"
- "Performance anxiety about grades (use 'grade_anxiety' code)"
edge_cases: "Self-judgment (internal criticism) → use 'self_critical_perfectionism' instead"
example_quotes:
typical_examples:
- "I was scared my classmates would laugh at my pronunciation" [P3, L45]
- "The chatbot doesn't judge me, but people do" [P7, L112]
- "I felt nervous because the teacher would notice my mistakes" [P11, L201]
boundary_examples:
- "I was worried about saying the wrong thing" [P5, L89] - coded because implies judgment from listener
counter_examples:
- "I was nervous because the vocabulary was difficult" [P2, L34] - NOT coded (task difficulty, not judgment)
- "I felt anxious before the test" [P9, L156] - NOT coded (test anxiety, use 'evaluation_anxiety')
related_codes:
parent_code: "affective_barriers"
sibling_codes: ["speaking_anxiety_general", "fear_of_mistakes"]
child_codes: ["peer_judgment_anxiety", "teacher_judgment_anxiety"]
code_metadata:
date_created: "2024-10-15"
created_by: "HY"
source: "deductive (Foreign Language Anxiety Scale)"
frequency: 27
prevalence: "18/24 participants (75%)"
```
### 4. Inter-Rater Reliability
**When IRR is Required:**
```yaml
irr_requirements:
required_for:
- "Content analysis with frequency claims"
- "Deductive coding with structured codebook"
- "Dissertation/thesis research"
- "High-stakes publication (top journals)"
optional_for:
- "Exploratory inductive research"
- "Single-researcher qualitative studies"
- "Phenomenological research"
best_practice: "Always recommended for transparency and rigor"
```
**IRR Process:**
```yaml
irr_process:
step_1_codebook_training:
activity: "Train second coder on codebook"
materials: ["Codebook", "Example coded transcripts", "Decision rules"]
time: "4-8 hours typical"
step_2_independent_coding:
sample_size: "10-20% of total dataset"
selection: "Random or stratified sampling"
independence: "No communication between coders during this phase"
step_3_reliability_calculation:
metrics:
cohens_kappa:
interpretation:
- "< 0.40: Poor agreement"
- "0.40-0.59: Fair agreement"
- "0.60-0.74: Good agreement"
- "0.75-1.00: Excellent agreement"
threshold: "≥ 0.60 acceptable, ≥ 0.80 preferred"
percent_agreement:
formula: "(Number of agreements / Total coding decisions) × 100"
threshold: "≥ 80% acceptable"
krippendorffs_alpha:
use_case: "Multiple coders or complex coding"
threshold: "≥ 0.67 acceptable"
step_4_discrepancy_resolution:
process:
- "Identify disagreements"
- "Discuss rationale for each code"
- "Refine codebook definitions"
- "Re-code if necessary"
output: "Refined codebook, consensus codes"
step_5_full_dataset_coding:
approach: "Primary coder completes remaining data with refined codebook"
spot_check: "Second coder reviews 5% of subsequent coding"
```
**IRR Reporting Template:**
```yaml
irr_report:
sample: "4 of 24 transcripts (17%) independently coded"
initial_reliability:
cohens_kappa: 0.68
percent_agreement: 78%
interpretation: "Good agreement"
discrepancies:
category_1: "Confusion between 'autonomy_support' and 'informational_feedback'"
resolution: "Refined definitions to emphasize choice vs. guidance distinction"
post_discussion_reliability:
cohens_kappa: 0.82
percent_agreement: 89%
interpretation: "Excellent agreement"
final_process: "First author coded remaining 20 transcripts with refined codebook"
```
### 5. Saturation Assessment
**Types of Saturation:**
```yaml
saturation_types:
information_saturation:
definition: "No new information emerging from interviews"
indicators:
- "Repeated stories across participants"
- "Predictable responses"
- "No surprising or novel information"
when_to_assess: "After each interview"
thematic_saturation:
definition: "No new themes identified"
indicators:
- "Theme structure stable"
- "No major reorganization needed"
- "New data confirms existing themes"
when_to_assess: "After coding 50%, 75%, 100% of data"
code_saturation:
definition: "No new codes needed"
indicators:
- "Existing codes cover all data"
- "Minimal new codes per transcript"
- "New codes are minor variations"
when_to_assess: "Real-time during coding"
```
**Saturation Documentation:**
```yaml
saturation_tracking:
method_1_saturation_grid:
structure:
columns: ["Interview #", "New Codes", "New Themes", "Cumulative Codes"]
rows: "One per interview"
example:
- interview: 1, new_codes: 45, new_themes: 8, cumulative: 45
- interview: 5, new_codes: 12, new_themes: 2, cumulative: 98
- interview: 10, new_codes: 4, new_themes: 0, cumulative: 134
- interview: 15, new_codes: 1, new_themes: 0, cumulative: 141
- interview: 18, new_codes: 0, new_themes: 0, cumulative: 141
saturation_point: "Interview 15-18"
method_2_saturation_curve:
x_axis: "Interview number"
y_axis: "New codes identified"
visualization: "Line graph showing decline to near-zero"
saturation_indicator: "Curve flattens (asymptotic)"
method_3_constant_comparison:
process:
- "Code new interview"
- "Compare to existing codes"
- "Note: new/variation/duplicate"
- "Track proportion of duplicates"
saturation_threshold: ">90% of data segments fit existing codes"
```
**Saturation Memo Example:**
```yaml
saturation_memo:
date: "2024-11-20"
data_point: "After 18 interviews"
observations:
code_saturation:
- "Last 3 interviews (16-18) added 0 new codes"
- "Total codebook: 141 codes collapsed to 28 focused codes"
- "All new data fit into existing framework"
thematic_saturation:
- "6 major themes stable since interview 12"
- "Minor refinements to theme boundaries only"
- "No structural changes to thematic map"
information_saturation:
- "Participants repeating same experiences"
- "No novel insights about chatbot interaction patterns"
- "Can predict responses based on existing data"
decision: "Data collection complete. Saturation achieved for research question."
transparency_note: "Acknowledge in write-up that saturation is context-dependent and future research may reveal new dimensions"
```
### 6. CAQDAS (Computer-Assisted Qualitative Data Analysis Software)
**Major Software Comparison:**
```yaml
caqdas_comparison:
nvivo:
strengths:
- "Powerful text coding and annotation"
- "Matrix queries for pattern analysis"
- "Video/audio transcript integration"
- "Framework matrices for deductive coding"
weaknesses:
- "Steep learning curve"
- "Expensive ($1400+ academic license)"
best_for: "Large projects, deductive + inductive coding, multimedia data"
atlas_ti:
strengths:
- "Visual network diagrams"
- "Memo system excellent for theory building"
- "Grounded theory tools"
- "Geographic/spatial data analysis"
weaknesses:
- "Interface can be unintuitive"
- "Moderate cost ($700+ academic)"
best_for: "Grounded theory, visual learners, conceptual mapping"
maxqda:
strengths:
- "User-friendly interface"
- "Mixed methods integration (quant + qual)"
- "Team collaboration features"
- "Social media data import"
weaknesses:
- "Less powerful for very large datasets"
- "Moderate cost ($600+ academic)"
best_for: "Beginners, mixed methods, team projects"
dedoose:
strengths:
- "Cloud-based (access anywhere)"
- "Mixed methods focus"
- "Lower cost ($12.95/month)"
- "Real-time team collaboration"
weaknesses:
- "Requires internet connection"
- "Less feature-rich than NVivo"
best_for: "Budget-conscious researchers, collaborative projects, cross-platform work"
manual_coding:
tools: ["Microsoft Word comments", "Excel spreadsheets", "Paper and highlighters"]
strengths:
- "No cost"
- "Deep data immersion"
- "No technical barriers"
weaknesses:
- "Time-consuming"
- "Difficult to reorganize codes"
- "No automated queries"
best_for: "Small datasets (<10 interviews), exploratory studies, students learning coding"
```
**When to Use Software vs. Manual Coding:**
```yaml
decision_tree:
use_caqdas_if:
- "Dataset > 10 interviews/documents"
- "Need to reorganize codes frequently"
- "Want automated code frequency reports"
- "Multiple coders collaborating"
- "Mixed methods (quant + qual integration)"
manual_coding_acceptable_if:
- "Small dataset (< 10 interviews)"
- "Single researcher"
- "Exploratory pilot study"
- "Budget constraints"
- "Simple coding scheme (< 20 codes)"
hybrid_approach:
option_1: "Manual coding for first pass → CAQDAS for refinement"
option_2: "CAQDAS for coding → Manual for theme interpretation"
```
**Auto-Coding Features (Use with Caution):**
```yaml
auto_coding:
text_search_coding:
description: "Software automatically codes all instances of keyword"
example: "Code all mentions of 'chatbot' → 'chatbot_references'"
pros: "Fast for manifest content"
cons: "Misses synonyms, context-dependent meaning"
recommendation: "Use for initial pass, manually review results"
sentiment_analysis:
description: "Software classifies text as positive/negative/neutral"
example: "Auto-code positive statements → 'positive_affect'"
pros: "Quick overview of tone"
cons: "Poor accuracy for complex emotions, sarcasm"
recommendation: "Supplementary only, not sole coding method"
ai_assisted_coding:
tools: ["NVivo with AI", "Atlas.ti GPT integration"]
description: "AI suggests codes based on content"
pros: "Can identify patterns human might miss"
cons: "Black box, not transparent, ethical concerns"
recommendation: "Experimental stage, use human oversight"
```
## Key Deliverables
```yaml
deliverables:
codebook:
format: "Excel/Word table or CAQDAS export"
contents:
- "Code names and definitions"
- "Inclusion/exclusion criteria"
- "Example quotations"
- "Code relationships (parent/child)"
audience: "Research team, peer reviewers"
coding_audit_trail:
contents:
- "Initial codebook version"
- "Coding memos"
- "Code refinement decisions"
- "IRR results and resolutions"
purpose: "Transparency and trustworthiness"
thematic_map:
format: "Visual diagram (mind map, concept map)"
elements:
- "Themes as nodes"
- "Relationships as arrows"
- "Subthemes nested within"
purpose: "Show holistic structure of findings"
saturation_documentation:
format: "Table or graph"
contents:
- "Saturation grid (codes per interview)"
- "Saturation curve graph"
- "Saturation memo"
purpose: "Justify sample size adequacy"
findings_table:
columns: ["Theme", "Description", "Frequency", "Representative Quote"]
rows: "One per theme/subtheme"
purpose: "Summary table for manuscript"
```
## Best Practices
```yaml
best_practices:
coding_consistency:
- "[ ] Code in dedicated time blocks (avoid fatigue)"
- "[ ] Re-code 10% of data after 1 week to check consistency"
- "[ ] Keep coding manual open during all sessions"
reflexivity:
- "[ ] Write memos about own biases and assumptions"
- "[ ] Discuss potential influence on code interpretation"
- "[ ] Seek peer debriefing on coding decisions"
transparency:
- "[ ] Provide codebook in appendix or supplementary materials"
- "[ ] Describe coding process in methods section (not just 'data were coded')"
- "[ ] Include sufficient quotations in findings (not just summaries)"
data_management:
- "[ ] Backup coded data daily (multiple locations)"
- "[ ] Version control for codebook (track changes)"
- "[ ] Anonymize transcripts before sharing with coders"
```
## Common Pitfalls
```yaml
pitfalls:
thin_coding:
problem: "Codes too broad, not specific enough"
example: "Code: 'motivation' applied to 100+ segments"
solution: "Break into specific types (intrinsic, extrinsic, autonomous, controlled)"
code_proliferation:
problem: "Too many codes, no conceptual organization"
example: "200+ codes, many overlapping"
solution: "Collapse similar codes, create hierarchy"
insufficient_examples:
problem: "Codebook definitions without example quotes"
solution: "Minimum 2-3 examples per code"
ignoring_negative_cases:
problem: "Only coding data that fits expected themes"
solution: "Actively search for disconfirming evidence"
over_reliance_on_software:
problem: "Let CAQDAS dictate analysis, not researcher insight"
solution: "Software is tool, not method. Interpretation remains human."
```
## Human Checkpoint
**CP_CODING_FRAMEWORK**: Review proposed codebook structure, coding approach, and saturation plan before data collection begins.
## Example Output
When researcher asks for help with coding:
```markdown
# Qualitative Coding Plan for [Research Topic]
## Recommended Approach: Hybrid (Deductive + Inductive)
### Phase 1: Deductive Framework (Based on Literature)
**A priori codes from [Theory Name]:**
- [Code 1]: [Definition]
- [Code 2]: [Definition]
- [Code 3]: [Definition]
### Phase 2: Inductive Open Coding
- Remain open to emergent codes beyond framework
- Track source of each code (deductive vs. inductive)
### Phase 3: Thematic Development
[Insert phased coding process based on method]
## Codebook Template
[Provide Excel template or CAQDAS structure]
## Inter-Rater Reliability Plan
- **Sample**: 20% of transcripts (n = X)
- **Metric**: Cohen's Kappa (target ≥ 0.75)
- **Process**: [Detailed IRR steps]
## Saturation Tracking
- Method: [Saturation grid / curve]
- Assessment points: After interviews 10, 15, 20
- Expected saturation: 18-22 interviews
## Software Recommendation
**Recommended**: [NVivo / MAXQDA / Dedoose]
**Rationale**: [Justify based on project needs]
## Next Steps
1. [ ] Refine codebook with advisor
2. [ ] Pilot code 2-3 transcripts
3. [ ] Train second coder (if IRR required)
4. [ ] Begin systematic coding
```
---
**HANDOFF TO:**
- **E3-MixedMethodsIntegration**: For advancing from codes to interpretive themes and integration
- **G2-PublicationSpecialist**: For writing findings section with coded themes
