skills/43-wentorai-research-plugins/skills/research/methodology/qualitative-research-guide/SKILL.md
Design and conduct qualitative research using grounded theory and case studies
npx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research qualitative-research-guideInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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.
| 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 |
Data Collection (interviews, observations)
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Open Coding: Line-by-line coding of raw data
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Axial Coding: Grouping codes into categories,
identifying relationships
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Selective Coding: Identifying the core category
that integrates all others
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Theoretical Saturation: Stop when new data
no longer generates new codes
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Substantive Theory: A grounded explanation of the phenomenon
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
| 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 |
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:
- 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"
| 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 |
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.
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.
tools
Recommend AND run open-source AI tools, agents, Claude Code / Codex skills, and MCP servers for any stage of a literature review — searching, reading, extracting, synthesizing, screening, citation-checking, and paper writing. Use when the user asks "what tool should I use to..." OR "install/run/use <tool> to ..." for research/lit-review work: automating a survey or related-work section, PDF→Markdown extraction for LLMs (MinerU/marker/docling), PRISMA / systematic review (ASReview), citation-backed Q&A over PDFs (PaperQA2), wiring papers into Claude/Cursor via MCP (arxiv/paper-search/zotero servers), or chatting with a Zotero library. Ships a launcher (scripts/litrun.py) that installs each tool in an isolated venv and runs it. Curated catalog of 70+ vetted projects. 支持中英文(用于「文献综述工具选型」与「一键安装/运行」)。
development
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
documentation
Use when the project collects primary data or runs a field, lab, or survey experiment, before the intervention begins — write the pre-analysis plan, size the sample from a power calculation, and register with the AEA RCT Registry. Apply after the design is chosen in aer-identification and before any outcome data are seen.
tools
Guide economists to authoritative data sources with explicit, confirmed data specifications before retrieval; interfaces with Playwright MCP to navigate portals and extract real data, not articles about data.