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.