Comprehensive qualitative research methods assistant supporting thematic analysis (Braun & Clarke), grounded theory (Strauss & Corbin; Charmaz), interpretative phenomenological analysis (IPA), content analysis, narrative inquiry, ethnography, case study methodology (Yin), coding techniques (open/axial/selective), NVivo-style workflows with Python alternatives, trustworthiness criteria (Lincoln & Guba), reflexivity, and member checking. Use when designing or analyzing qualitative research — thematic analysis, grounded theory, coding data, phenomenology, IPA, ethnography, case study, narrative inquiry, content analysis, qualitative coding, NVivo, trustworthiness, member checking, reflexivity, interview analysis, or focus group analysis. Part of the AlterLab Academic Skills suite.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Um comando direto ignora o prompt de revisão. Verifique a origem antes de executá-lo.
Instruções da origem · Visualização somente leitura
name
alterlab-qualitative-methods
description
Comprehensive qualitative research methods assistant supporting thematic analysis (Braun & Clarke), grounded theory (Strauss & Corbin; Charmaz), interpretative phenomenological analysis (IPA), content analysis, narrative inquiry, ethnography, case study methodology (Yin), coding techniques (open/axial/selective), NVivo-style workflows with Python alternatives, trustworthiness criteria (Lincoln & Guba), reflexivity, and member checking. Use when designing or analyzing qualitative research — thematic analysis, grounded theory, coding data, phenomenology, IPA, ethnography, case study, narrative inquiry, content analysis, qualitative coding, NVivo, trustworthiness, member checking, reflexivity, interview analysis, or focus group analysis. Part of the AlterLab Academic Skills suite.
license
MIT
allowed-tools
Read WebFetch WebSearch Bash(python:*)
compatibility
No API key required. Guidance-focused skill; uses WebFetch/WebSearch and optional Python helpers via `uv run python`.
Qualitative Methods — Qualitative Research Design & Analysis Agent
A comprehensive qualitative research methods tool for faculty and researchers conducting, supervising, or teaching qualitative inquiry. Covers methodology selection, data collection design, systematic coding, analysis techniques, and quality assurance across all major qualitative traditions.
Overview
Qualitative research seeks to understand phenomena through the meanings people assign to them. This skill guides researchers through the full qualitative research process: choosing an appropriate methodology, designing data collection instruments, conducting systematic analysis, ensuring trustworthiness, and reporting findings in ways that meet disciplinary standards.
Unlike quantitative research, which tests hypotheses through numerical data, qualitative research generates rich, contextualized understanding through words, images, and observations. This skill treats methodological rigor as non-negotiable while acknowledging the interpretive nature of qualitative work.
When to Use This Skill
This skill should be used when:
Selecting a qualitative methodology for a research project
Designing interview protocols, observation guides, or document analysis frameworks
Coding qualitative data (transcripts, field notes, documents)
Conducting thematic analysis, grounded theory, IPA, or other analytic approaches
Establishing trustworthiness and quality criteria for qualitative findings
Writing the methodology section of a qualitative paper or thesis
Reviewing qualitative research for methodological rigor
Teaching qualitative methods courses
Transitioning from quantitative to qualitative or mixed-methods research
Working with CAQDAS (Computer-Assisted Qualitative Data Analysis Software)
Does NOT Trigger
Scenario
Use Instead
Statistical analysis
Data science skills
Survey design and validation
alterlab-survey-design
Writing the full paper
alterlab-paper-writer
Systematic review / meta-analysis
alterlab-deep-research
Core Capabilities
1. Methodology Selection Guide
Choosing the right qualitative methodology depends on your research question, philosophical stance, and the kind of knowledge you seek.
Methodology Decision Framework:
What is your primary research interest?
│
├── LIVED EXPERIENCE of individuals
│ ├── Focus on meaning-making and interpretation
│ │ └── Interpretative Phenomenological Analysis (IPA)
│ ├── Focus on the essence/structure of experience
│ │ └── Descriptive/Transcendental Phenomenology (Husserl/Moustakas)
│ └── Focus on life stories and identity construction
│ └── Narrative Inquiry
│
├── SOCIAL PROCESSES and how they unfold
│ ├── Want to generate theory from data
│ │ └── Grounded Theory (Strauss & Corbin or Charmaz)
│ └── Want to describe a process in context
│ └── Case Study (Yin) or Ethnography
│
├── CULTURAL PATTERNS in a group or setting
│ ├── Immersive fieldwork possible (months/years)
│ │ └── Ethnography
│ └── Bounded, focused investigation
│ └── Case Study (Yin)
│
├── CONTENT/MEANING in texts or media
│ ├── Seeking patterns and themes across a dataset
│ │ └── Thematic Analysis (Braun & Clarke)
│ └── Systematic categorization of content
│ └── Content Analysis (qualitative or directed)
│
└── A BOUNDED SYSTEM (program, event, organization)
├── Single case with depth
│ └── Single Case Study
├── Multiple cases for comparison
│ └── Multiple Case Study
└── Evaluating a program or intervention
└── Case Study with evaluation framework
The most widely used qualitative analysis method. Braun & Clarke (2006, updated 2019) provide a reflexive approach.
Six Phases of Reflexive Thematic Analysis:
Phase 1: Familiarization with the Data
│ Read and re-read transcripts
│ Note initial observations (not codes yet)
│ Immerse yourself in the data
│ Record preliminary thoughts in a research journal
│
Phase 2: Systematic Coding
│ Generate initial codes across the entire dataset
│ Code inclusively (code everything potentially relevant)
│ Codes are semantic (surface meaning) or latent (underlying assumptions)
│ One extract can have multiple codes
│ Use line-by-line or segment-by-segment approach
│
Phase 3: Generating Initial Themes
│ Collate codes into potential themes
│ Use visual methods: mind maps, affinity diagrams, tables
│ Themes are patterns of shared meaning organized around a central concept
│ Not every code becomes a theme — some are subthemes, some are discarded
│
Phase 4: Reviewing Themes
│ Check themes against coded extracts (Level 1)
│ Check themes against entire dataset (Level 2)
│ Do themes tell a coherent story?
│ Are themes distinct or overlapping?
│ Refine: split, merge, or discard themes as needed
│
Phase 5: Defining and Naming Themes
│ Write a detailed analysis of each theme
│ Identify the "essence" of each theme
│ Create concise, vivid theme names
│ Determine how themes relate to each other
│
Phase 6: Writing Up
Weave together analytic narrative and data extracts
Go beyond description — interpret and contextualize
Embed extracts as evidence, not illustration only
Relate findings to literature and research question
Worked coding example and theme-development example: see references/worked_examples.md.
3. Grounded Theory
Grounded theory generates theory that is "grounded" in systematically collected and analyzed data.
Strauss & Corbin (1998) — Systematic Approach:
OPEN CODING
│ Break data into discrete parts
│ Label phenomena (concepts)
│ Group concepts into categories
│ Identify properties and dimensions of categories
│
│ Example:
│ Concept: "Time pressure"
│ Properties: Source (institutional, self-imposed), Intensity, Duration
│ Dimensions: Mild ←──────→ Severe
│
AXIAL CODING
│ Reassemble data around categories
│ Use the paradigm model:
│
│ Causal Conditions → Phenomenon → Context
│ → Intervening Conditions
│ → Action/Interaction Strategies
│ → Consequences
│
│ Example:
│ Causal conditions: Publish-or-perish culture
│ Phenomenon: Research anxiety
│ Context: Early-career faculty, research university
│ Intervening conditions: Mentorship availability, institutional support
│ Strategies: Strategic collaboration, prioritization, avoidance
│ Consequences: Productivity variation, burnout, career trajectory
│
SELECTIVE CODING
Identify the core category
Systematically relate all categories to the core
Fill in categories that need further development
Validate the emerging theory against data
Write the storyline
Example core category: "Surviving the Tenure Track"
All other categories (research anxiety, teaching load, institutional
politics, mentorship) relate to this central phenomenon
Charmaz (2014) — Constructivist Grounded Theory:
Key differences from Strauss & Corbin:
Emphasizes researcher's role in constructing (not discovering) theory
Uses initial coding and focused coding (not open/axial/selective)
Theoretical sampling continues until categories are saturated
Theoretical Sampling:
Initial data collection (purposive sampling)
│
├── Analyze → Emerging categories
│
├── Identify gaps in categories
│
├── Sample specifically to develop/test categories
│ (Who to interview? What to observe? What documents to examine?)
│
├── Analyze new data → Refine categories
│
├── Repeat until THEORETICAL SATURATION
│ (No new properties, dimensions, or relationships emerge)
│
└── Articulate substantive theory
4. Interpretative Phenomenological Analysis (IPA)
IPA explores how individuals make sense of significant life experiences. It is idiographic, phenomenological, and hermeneutic.
Step 1: Reading and Re-reading
│ Read the transcript multiple times
│ Listen to the audio recording alongside
│ Note initial observations in the margin
│
Step 2: Initial Noting
│ Three types of comments:
│ - Descriptive (content, what is said)
│ - Linguistic (language use, metaphors, pauses, laughter)
│ - Conceptual (interpretive, interrogative)
│
│ Example:
│ Transcript: "It was like hitting a brick wall"
│ Descriptive: Encountered a major obstacle
│ Linguistic: Metaphor of physical barrier — sudden, painful, solid
│ Conceptual: Sense of helplessness? Unexpected nature of the barrier?
│
Step 3: Developing Emergent Themes
│ Transform notes into concise themes
│ Themes capture psychological essence, not just content
│ Balance participant's words with analyst's interpretation
│ (The hermeneutic circle: part ↔ whole)
│
Step 4: Searching for Connections Across Themes
│ Organize emergent themes:
│ - Abstraction: Group similar themes under superordinate theme
│ - Subsumption: One theme becomes superordinate, absorbs others
│ - Polarization: Identify oppositional relationships
│ - Contextualization: Identify temporal or narrative connections
│ - Numeration: Note frequency (but do not reduce to counting)
│ - Function: Examine what themes do for the participant
│
Step 5: Moving to the Next Case
│ Bracket (as much as possible) findings from previous cases
│ Repeat Steps 1-4 for each participant individually
│
Step 6: Looking for Patterns Across Cases
Identify recurrent themes across participants
Note convergence and divergence
Create a master table of themes
Retain idiographic detail — do not flatten into generic themes
IPA master table-of-themes example (superordinate themes, subthemes, per-participant page references): see references/worked_examples.md.
5. Case Study Methodology (Yin, 2018)
Types of Case Study Designs:
Single-Case Multiple-Case
┌──────────────────┐ ┌──────────────────┐
Holistic │ Type 1 │ │ Type 3 │
(single unit │ Critical, unique,│ │ Replication │
of analysis) │ revelatory, or │ │ logic across │
│ longitudinal case│ │ cases │
└──────────────────┘ └──────────────────┘
┌──────────────────┐ ┌──────────────────┐
Embedded │ Type 2 │ │ Type 4 │
(multiple units │ Single case with │ │ Multiple cases │
of analysis) │ embedded sub- │ │ each with │
│ units │ │ embedded units │
└──────────────────┘ └──────────────────┘
Five Components of Case Study Design:
Research questions — "How" and "why" questions are best suited
Propositions (if any) — Direct attention to what should be examined
Unit of analysis — Define the "case" (individual, organization, program, event)
Logic linking data to propositions — Pattern matching, explanation building, time-series
Criteria for interpreting findings — Rival explanations, sufficient evidence
Bias from poor questions, response bias, inaccuracies from recall
Direct observations
Real-time, contextual
Time-consuming, selectivity, reflexivity effects
Participant observation
Insightful about interpersonal behavior
Bias from researcher participation
Physical artifacts
Insightful about cultural features
Selectivity, availability
Chain of Evidence:
Research questions
↓
Case study protocol
↓
Citations to specific evidence in case study database
↓
Case study database (organized notes, documents, tabular materials)
↓
Case study report
6. Coding Techniques
Coding Hierarchy:
Level 1: OPEN/INITIAL CODES
│ Line-by-line or segment-by-segment labels
│ Stay close to the data
│ Example: "feeling unprepared," "seeking guidance," "peer support"
│
Level 2: FOCUSED/AXIAL CODES (Categories)
│ Group open codes into higher-level categories
│ Identify relationships between categories
│ Example: "Navigation strategies" (contains: seeking guidance, peer
│ support, trial and error, online resources)
│
Level 3: THEMES / THEORETICAL CODES
Abstract, interpretive-level patterns
Capture the meaning across categories
Example: "The invisible apprenticeship" (captures how new academics
learn the unwritten rules of their profession)
Full codebook template (definition, inclusion/exclusion criteria, worked example): see references/worked_examples.md.
7. Python-Based Qualitative Analysis Workflows
For researchers who prefer open-source tools over NVivo/Atlas.ti, ready-to-run Python recipes cover keyword-based initial coding, bigram/trigram frequency and collocation analysis, and thematic-network visualization.
Full code recipes: see references/python_workflows.md.
Catalytic authenticity: Research stimulates action
Tactical authenticity: Participants are empowered to act
Triangulation Types:
1. Data triangulation — Multiple data sources (interviews + documents + observations)
2. Investigator triangulation — Multiple researchers coding/analyzing
3. Theory triangulation — Multiple theoretical lenses applied to the same data
4. Methodological triangulation — Multiple methods (interviews + surveys + observations)
Full member-checking procedure and reporting template: see references/worked_examples.md.
9. Reflexivity
Reflexivity is the researcher's ongoing critical self-examination of their role, assumptions, and influence on the research process.
Reflexive journal prompts spanning before, during, and after data collection and analysis: see references/worked_examples.md.
Best Practices
Choose methodology before collecting data. Your methodology shapes your research question, sampling, data collection, and analysis. Do not collect data and then decide how to analyze it.
Maintain an audit trail. Document every decision: why you chose a methodology, how you recruited participants, how codes evolved, why you merged or split themes. This is your evidence of rigor.
Code systematically. Whether using software or paper, code the entire dataset consistently. Do not cherry-pick excerpts that confirm your expectations.
Write memos throughout. Memos are where analytical thinking happens. Write early, write often, write about codes, categories, themes, puzzles, and surprises.
Achieve saturation thoughtfully. Saturation is not a number (e.g., "12 interviews"). It is a judgment that new data are no longer generating new insights relevant to your research question.
Use participants' words. Direct quotes are the evidence of qualitative research. Weave them into your analysis, not as decoration, but as the foundation of your argument.
Distinguish description from interpretation. Description says what the data contain. Interpretation says what the data mean. Both are needed; do not confuse them.
Triangulate. Use multiple data sources, methods, or analysts to strengthen credibility. No single source is sufficient.
Practice reflexivity. Your positionality shapes what you see and how you interpret it. Make this explicit, not as a confession, but as a methodological practice.
Report transparently. Include your codebook, describe your analysis process in detail, justify your sample, and acknowledge limitations. Readers should be able to evaluate your rigor.
Common Pitfalls
Pitfall
Why It Happens
How to Avoid
Methodology-method confusion
Conflating a method (interviews) with a methodology (grounded theory)
Study the philosophical foundations of your chosen methodology
Anecdotalism
Selecting quotes that support a preconceived narrative
Code the entire dataset; report disconfirming evidence
Premature closure
Stopping data collection too early
Use theoretical sampling; document saturation decisions
Superficial coding
Staying at descriptive level without interpretive depth
Use latent as well as semantic codes; write analytical memos
Theme = topic
Calling a topic a theme (e.g., "communication" is a topic, not a theme)
Themes are patterns of shared meaning — they make a claim
Ignoring context
Treating excerpts as decontextualized data points
Preserve context in your codebook; use thick description
No reflexivity
Assuming the researcher is a neutral instrument
Maintain a reflexive journal from day one
Over-reliance on frequency
Treating qualitative analysis as counting code occurrences
A theme's importance is about meaning, not frequency
Mixed methodology without justification
Combining elements of grounded theory and thematic analysis without explanation
Be clear about your methodological commitments and why
Poor data management
Transcripts scattered, no version control, no backup
Use a systematic folder structure; back up regularly; use CAQDAS
References
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101.
Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589-597.
Charmaz, K. (2014). Constructing grounded theory (2nd ed.). Sage.
Clandinin, D. J., & Connelly, F. M. (2000). Narrative inquiry: Experience and story in qualitative research. Jossey-Bass.
Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). Sage.
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage.
Smith, J. A., Flowers, P., & Larkin, M. (2009). Interpretative phenomenological analysis: Theory, method, and research. Sage.
Strauss, A., & Corbin, J. (1998). Basics of qualitative research: Techniques and procedures for developing grounded theory (2nd ed.). Sage.
Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). Sage.
See also: references/qualitative-frameworks.md for expanded methodology details.
