qualitative-research-guide
- Repo stars 224
- Author updated Live
- Author repo research-plugins
- Domain
- Design
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @wentorai · no license declared
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- Python
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: qualitative-research-guide
description: Design and conduct qualitative research using grounded theory and case studies A skill for desig…
category: design
runtime: Python
---
# qualitative-research-guide output preview
## PART A: Task fit
- Use case: Design and conduct qualitative research using grounded theory and case studies A skill for designing and conducting rigorous qualitative research. Covers major qualitative traditions, data collection methods, coding and analysis techniques, and quality criteria for trustworthy qualitative findings. runs entirely locally; runs on Python. Works with Claude ….
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Major Qualitative Traditions / Choosing an Approach / Grounded Theory Process” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “Design and conduct qualitative research using grounded theory and case studies A skill for designing and conducting rigorous qualitative research. Covers major qualitative traditions, data collection methods, coding and analysis techniques, and quality criteria for trustworthy qualitative findings. runs entirely locally; runs on Python. Works with Claude …”.
- **02** When the source has headings, the agent prioritizes “Major Qualitative Traditions / Choosing an Approach / Grounded Theory Process” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Start with a small task and check whether the result follows “Major Qualitative Traditions / Choosing an Approach / Grounded Theory Process”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: qualitative-research-guide
description: Design and conduct qualitative research using grounded theory and case studies A skill for desig…
category: design
source: wentorai/research-plugins
---
# qualitative-research-guide
## When to use
- Design and conduct qualitative research using grounded theory and case studies A skill for designing and conducting ri…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Major Qualitative Traditions / Choosing an Approach / Grounded Theory Process” and keep inference separate from source facts.
- read files, write/modify files; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "qualitative-research-guide" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Major Qualitative Traditions / Choosing an Approach / Grounded Theory Process
rules -> SKILL.md triggers / order / output contract
runtime -> Python | read files, write/modify files | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} Qualitative Research Guide
A skill for designing and conducting rigorous qualitative research. Covers major qualitative traditions, data collection methods, coding and analysis techniques, and quality criteria for trustworthy qualitative findings.
Major Qualitative Traditions
Choosing an Approach
| Approach | Research Question Type | Unit of Analysis | Sample Size | Output |
|---|---|---|---|---|
| Grounded Theory | How does a process work? | Process/action | 20-60 | Theory |
| Phenomenology | What is the lived experience? | Experience | 5-25 | Essence description |
| Case Study | How/why does this case work? | Bounded system | 1-5 cases | Case description |
| Ethnography | How does this culture work? | Cultural group | Extended fieldwork | Cultural portrait |
| Narrative | What is this person's story? | Individual life | 1-5 | Narrative account |
| Thematic Analysis | What patterns exist in this data? | Themes across data | Variable | Theme map |
Grounded Theory Process
Data Collection (interviews, observations)
|
v
Open Coding: Line-by-line coding of raw data
|
v
Axial Coding: Grouping codes into categories,
identifying relationships
|
v
Selective Coding: Identifying the core category
that integrates all others
|
v
Theoretical Saturation: Stop when new data
no longer generates new codes
|
v
Substantive Theory: A grounded explanation of the phenomenon
Interview Design
Semi-Structured Interview Protocol
def create_interview_protocol(research_questions: list[str],
n_questions: int = 10) -> dict:
"""
Generate a semi-structured interview protocol template.
Args:
research_questions: The study's research questions
n_questions: Target number of interview questions
"""
protocol = {
'opening': {
'rapport_building': [
"Thank you for participating. Before we begin, could you "
"tell me a little about yourself and your background?",
"How did you first become involved in [topic]?"
],
'time_estimate': '60-90 minutes'
},
'main_questions': [],
'closing': {
'wrap_up': [
"Is there anything else you would like to share that we "
"have not covered?",
"Looking back, what stands out most to you about [topic]?",
"Do you have any questions for me?"
]
},
'guidelines': [
'Ask open-ended questions (how, what, tell me about)',
'Avoid leading questions',
'Use probes: "Can you give me an example?"',
'Use follow-ups: "You mentioned X, tell me more about that"',
'Allow silences -- do not rush to fill pauses',
'Record field notes immediately after each interview'
]
}
# Generate question structure
for i, rq in enumerate(research_questions):
protocol['main_questions'].append({
'research_question': rq,
'interview_questions': [
f'Grand tour question for RQ{i+1}',
f'Follow-up probe for RQ{i+1}',
f'Example-seeking probe for RQ{i+1}'
]
})
return protocol
Sampling Strategies
| Strategy | Description | When to Use |
|---|---|---|
| Purposive | Select information-rich cases | Most qualitative studies |
| Maximum variation | Select cases that differ on key dimensions | Capture range of experiences |
| Snowball | Participants refer others | Hard-to-reach populations |
| Theoretical | Driven by emerging theory | Grounded theory studies |
| Critical case | Select cases that are pivotal | Testing theoretical propositions |
| Convenience | Readily available participants | Pilot studies only |
Coding and Analysis
Thematic Analysis (Braun & Clarke, 2006)
def thematic_analysis_workflow(transcripts: list[str]) -> dict:
"""
Outline the six phases of reflexive thematic analysis.
"""
phases = {
'phase_1_familiarization': {
'actions': [
'Read and re-read all transcripts',
'Note initial impressions in a research journal',
'Transcribe recordings if not already done'
],
'output': 'Familiarity with data, initial notes'
},
'phase_2_coding': {
'actions': [
'Code every data segment systematically',
'Use open coding (inductive) or deductive codes from framework',
'Code inclusively -- same segment can have multiple codes',
'Maintain a codebook with definitions and examples'
],
'output': 'Coded dataset, codebook'
},
'phase_3_generating_themes': {
'actions': [
'Collate codes into potential themes',
'Create a thematic map showing relationships',
'Distinguish between semantic and latent themes'
],
'output': 'Candidate themes and sub-themes'
},
'phase_4_reviewing_themes': {
'actions': [
'Check themes against coded extracts',
'Check themes against entire dataset',
'Merge, split, or discard themes as needed'
],
'output': 'Refined thematic map'
},
'phase_5_defining_themes': {
'actions': [
'Write a detailed description of each theme',
'Identify the essence of each theme',
'Name themes concisely and informatively'
],
'output': 'Theme definitions and names'
},
'phase_6_writing_up': {
'actions': [
'Weave together analytic narrative and data extracts',
'Select vivid, compelling quotes for each theme',
'Connect themes to research questions and literature'
],
'output': 'Final analysis write-up'
}
}
return {
'phases': phases,
'n_transcripts': len(transcripts),
'estimated_time': f'{len(transcripts) * 4}-{len(transcripts) * 8} hours'
}
Codebook Structure
codebook:
- code: "ADAPT"
definition: "Participant describes adapting their behavior in response to a challenge"
inclusion_criteria: "Explicit mention of changing approach or strategy"
exclusion_criteria: "Passive acceptance without behavioral change"
example_quote: "I started doing things differently after that..."
theme: "Resilience Strategies"
- code: "BARR"
definition: "Participant identifies a barrier or obstacle"
inclusion_criteria: "Something that prevented or hindered progress"
exclusion_criteria: "General complaints without specific barrier"
example_quote: "The main thing holding me back was..."
theme: "Challenges"
Quality Criteria
Trustworthiness (Lincoln & Guba, 1985)
| Criterion | Quantitative Equivalent | Strategies |
|---|---|---|
| Credibility | Internal validity | Member checking, triangulation, prolonged engagement |
| Transferability | External validity | Thick description, purposive sampling |
| Dependability | Reliability | Audit trail, peer debriefing |
| Confirmability | Objectivity | Reflexivity journal, negative case analysis |
Inter-Coder Reliability
For team-based coding, calculate Cohen's kappa or percent agreement on a subset of data (at least 10-20% of the corpus). Aim for kappa > 0.70 before independent coding proceeds.
Software Tools
- NVivo: Full-featured qualitative analysis (commercial)
- ATLAS.ti: Comprehensive coding and analysis (commercial)
- MAXQDA: Mixed-methods capable (commercial)
- Dedoose: Cloud-based, collaborative (subscription)
- Taguette: Free, open-source qualitative coding
- QualCoder: Free, open-source Python-based tool
Reporting Standards
Follow the COREQ (Consolidated Criteria for Reporting Qualitative Research) checklist: report researcher positionality, sampling strategy, data collection methods, analysis approach, and provide sufficient quotations to evidence each theme.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review