Topic 1

Foundations of Qualitative Analysis

Qualitative analysis transforms raw data—interview transcripts, field notes, documents, images—into meaningful interpretations. Unlike quantitative analysis with its standardized procedures, qualitative analysis is more flexible, iterative, and interpretive.

What is Qualitative Analysis?

Qualitative analysis is the systematic process of examining, organizing, and interpreting non-numerical data to understand meanings, patterns, and experiences. It aims to develop rich descriptions, identify themes, and generate insights grounded in participants' perspectives.

Key Characteristics

Iterative

Analysis happens throughout data collection, not just at the end. You move back and forth between data, codes, and themes.

Interpretive

The researcher actively interprets meaning. Your perspective shapes the analysis, which is why reflexivity matters.

Inductive

Often builds theory from data (bottom-up) rather than testing existing theories (top-down), though deductive approaches exist.

Contextual

Meaning is understood within context. The same words can mean different things in different situations.

Types of Qualitative Data

Interview Transcripts

Verbatim records of interviews, including verbal and sometimes non-verbal cues

Example: "I felt completely overwhelmed when I first started the job..."

Focus Group Transcripts

Records of group discussions, capturing interaction and group dynamics

Includes who said what and how participants responded to each other

Field Notes

Researcher's observations and reflections from ethnographic or observational studies

Descriptions of settings, behaviors, interactions, researcher's thoughts

Documents & Texts

Existing materials like policies, letters, social media posts, news articles

Historical documents, organizational records, online content

Visual Materials

Photos, videos, drawings, diagrams created by or about participants

Participant-generated photos, video recordings, artwork

Open-Ended Survey Responses

Written responses to open questions in otherwise quantitative surveys

"Please explain your answer..." or "Any additional comments?"

The Analysis Process Overview

1

Data Preparation

Transcribe, organize, familiarize

2

Initial Coding

Break data into meaningful units

3

Pattern Finding

Group codes, identify themes

4

Interpretation

Make meaning, connect to literature

5

Reporting

Present findings with evidence

This is not strictly linear—you'll move back and forth between stages

Preparing for Analysis

Transcription

  • Verbatim transcription: Word-for-word, including "um," "uh," pauses
  • Clean transcription: Removes fillers but keeps all content
  • Denaturalized: Corrects grammar for readability

Tip: For most analyses, clean verbatim is sufficient. Conversation analysis requires detailed verbatim with timing.

Data Organization

  • Create consistent file naming: P01_Interview1_Date.docx
  • Keep master copies separate from working copies
  • Use line numbering for easy reference
  • Consider CAQDAS software for large datasets

Familiarization

  • Read through all data at least once before coding
  • Write memos about initial impressions
  • Note questions, surprises, patterns
  • Immerse yourself in the data

CAQDAS: Computer-Assisted Analysis

Qualitative Data Analysis Software (CAQDAS) helps manage and organize analysis:

  • NVivo: Comprehensive, widely used in academia
  • ATLAS.ti: Powerful visualization features
  • MAXQDA: Mixed methods capabilities
  • Dedoose: Cloud-based, affordable
  • Quirkos: Visual, user-friendly

Note: Software doesn't do the analysis—YOU do. It just helps organize.

Topic 2

Coding Strategies

Coding is the fundamental building block of qualitative analysis. It involves labeling segments of data with words or phrases that capture their meaning or significance. Good coding transforms raw data into organized, analyzable units.

What is a Code?

A code is a word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute to a portion of data. Codes are labels that link data excerpts to concepts.

Types of Codes

Descriptive Codes

Summarize the topic of a passage in a word or phrase

"I wake up at 5am, get the kids ready, drop them at school, then commute an hour to work..."

MORNING ROUTINE

In Vivo Codes

Use participants' own words as the code

"I just feel like I'm constantly 'running on empty,' you know?"

"RUNNING ON EMPTY"

Process Codes

Capture actions using gerunds (-ing words)

"Over time, I started to accept that I couldn't control everything..."

ACCEPTING LIMITATIONS

Emotion Codes

Label feelings expressed or inferred

"When they didn't believe me, I just wanted to disappear..."

FEELING DISMISSED

Values Codes

Reflect participant's values, attitudes, beliefs

"Family always comes first, no matter what the job demands..."

VALUE: FAMILY PRIORITY

Theoretical Codes

Apply concepts from existing theory

"I learned to fake being happy at work even when I wasn't..."

EMOTIONAL LABOR

Coding Approaches

Inductive (Bottom-Up)

Codes emerge from the data

  • No predetermined codes
  • Let data "speak"
  • Grounded in participants' words
  • Discover unexpected themes

Best for: Exploratory research, new topics, grounded theory

Deductive (Top-Down)

Codes come from existing theory

  • Start with predetermined codebook
  • Apply theory to data
  • Test or extend existing frameworks
  • Structured, systematic

Best for: Theory testing, content analysis, replication studies

Hybrid (Both)

Combine approaches strategically

  • Start with some theoretical codes
  • Remain open to emergent codes
  • Most common in practice
  • Balances structure and flexibility

Best for: Most qualitative studies, framework analysis

The Coding Process

1

First Cycle Coding

Initial pass through data

  • Code everything potentially relevant
  • Be generous—code more rather than less
  • Use multiple code types
  • Write memos about your thinking
2

Second Cycle Coding

Refine and organize codes

  • Merge similar codes
  • Split codes that are too broad
  • Create hierarchies (parent/child codes)
  • Begin grouping into categories
3

Pattern Coding

Identify larger patterns

  • Group codes into themes
  • Look for relationships
  • Develop explanatory categories
  • Move toward interpretation

Creating a Codebook

A codebook is a document that defines each code, provides examples, and establishes rules for when to apply it. Essential for consistency, especially with multiple coders.

Sample Codebook Entry

Element Content
Code Name WORK-LIFE CONFLICT
Definition Expressions of tension, strain, or incompatibility between work responsibilities and personal/family life
When to Use When participant describes work interfering with family/personal time, or vice versa; feeling torn between roles
When NOT to Use General complaints about workload without reference to personal life; schedule descriptions without conflict mention
Example "I missed my daughter's recital because of a deadline. I felt terrible but had no choice."

Coding Best Practices

DO

  • Code for meaning, not just topics
  • Write memos as you code
  • Use consistent naming conventions
  • Code in multiple passes
  • Stay open to revising codes
  • Include context in coded excerpts

DON'T

  • Create too many overlapping codes
  • Use vague, overly broad codes
  • Code mechanically without thinking
  • Force data into predetermined codes
  • Ignore contradictory data
  • Skip the codebook for team projects

Common Coding Pitfalls

  • Code overload: Creating hundreds of codes that become unmanageable
  • Descriptive only: Staying at surface level without interpretive codes
  • Premature closure: Settling on codes too early, missing nuances
  • Cherry-picking: Only coding data that confirms expectations
Topic 3

Thematic Analysis

Thematic analysis is one of the most widely used qualitative methods. It systematically identifies, organizes, and offers insight into patterns of meaning (themes) across a dataset. It's flexible, accessible, and applicable across many research questions.

What is a Theme?

A theme captures something important about the data in relation to the research question, representing a level of patterned response or meaning within the dataset. Themes are not just common topics—they represent meaningful patterns.

Characteristics of Good Themes

Coherent: Hang together meaningfully

Distinct: Different from other themes

Relevant: Address research questions

Grounded: Supported by data

Braun & Clarke's 6-Phase Process

Phase 1

Familiarization

Immerse yourself in the data

  • Read and re-read transcripts
  • Listen to audio recordings
  • Take initial notes
  • Write early impressions in memos

Output: Notes, initial ideas, questions

Phase 2

Generating Initial Codes

Systematically code the entire dataset

  • Work through each data item
  • Code for as many patterns as possible
  • Code extracts with context
  • Code can belong to multiple codes

Output: Full list of codes across dataset

Phase 3

Generating Themes

Collate codes into potential themes

  • Sort codes into groups
  • Consider how codes combine to form themes
  • Use visual maps or tables
  • Identify main themes and sub-themes

Output: Candidate themes with coded data

Phase 4

Reviewing Themes

Refine and validate themes

  • Check if themes work with coded extracts
  • Check if themes work with entire dataset
  • Merge, split, or discard themes as needed
  • Create thematic map

Output: Coherent set of themes, thematic map

Phase 5

Defining & Naming Themes

Finalize the essence of each theme

  • Write detailed analysis for each theme
  • Identify the "story" each theme tells
  • Consider how themes relate to each other
  • Create concise, punchy theme names

Output: Clear definitions, final names

Phase 6

Writing Up

Tell the story of your data

  • Select vivid, compelling extracts
  • Provide analytic narrative, not just description
  • Connect analysis to research questions
  • Relate back to literature

Output: Final report/manuscript

From Codes to Themes: An Example

Research Question: How do new teachers experience their first year?

Initial Codes

  • Feeling unprepared
  • Long work hours
  • Exhaustion
  • No time for self
  • Constant stress
  • Questioning career choice

Sub-themes

  • Reality shock
  • Burnout
  • Self-doubt

Theme

"Drowning, Not Waving": The Overwhelming First Year

Thematic Map

A thematic map visually represents relationships between themes and sub-themes:

New Teacher Experience
Theme 1: "Drowning, Not Waving"
Reality shock Burnout Self-doubt
Theme 2: "Finding My Feet"
Mentorship Small wins Growing confidence
Theme 3: "The Support System"
Colleague bonds Family support Online communities

Writing Up Themes

Good Theme Write-Up Structure

  1. Introduce the theme: State what it captures
  2. Provide data extract: Illustrative quote
  3. Analyze the extract: Explain what it shows
  4. Show pattern: Additional quotes showing consistency
  5. Address variations: Note any exceptions or nuances
  6. Connect: Link to research question or literature

Example Paragraph

Theme 1: "Drowning, Not Waving"

The dominant experience described by participants was one of overwhelming struggle. As Sarah explained: "I felt like I was just trying to keep my head above water every single day. There was never a moment to breathe." This sense of barely surviving was echoed across interviews. The metaphor of drowning appeared repeatedly, suggesting that the first year felt like a survival challenge rather than a learning opportunity. This finding aligns with Veenman's (1984) concept of "reality shock," where new teachers' idealistic expectations collide with classroom realities.

Reflexive vs. Coding Reliability Approaches

There are two major approaches to thematic analysis:

Reflexive TA (Braun & Clarke) Coding Reliability TA
Themes developed through interpretation Themes often predetermined
Researcher subjectivity is a resource Aims for objectivity
No inter-rater reliability needed Measures inter-rater reliability
Single coder can be valid Multiple coders preferred
Topic 4

Other Analytic Approaches

While thematic analysis is versatile, other qualitative approaches offer different lenses for understanding data. Each approach has distinct philosophical foundations, procedures, and types of findings.

Qualitative Content Analysis

Content Analysis

Purpose: Systematically categorize textual data, often with quantification of categories

Key Features
  • Can be quantitative (counting) or qualitative (interpreting)
  • Often uses predetermined categories
  • Systematic, rule-based coding
  • Good for large datasets
Process
  1. Define research question
  2. Select sample of texts
  3. Define coding categories
  4. Code all material
  5. Analyze patterns (with or without frequencies)

Best for: Media analysis, policy documents, systematic reviews of qualitative literature, comparing across sources

Grounded Theory

Grounded Theory

Purpose: Develop theory that is "grounded" in data, explaining a process or phenomenon

Key Features
  • Theory generation, not just description
  • Constant comparison method
  • Theoretical sampling
  • Aims for theoretical saturation
  • Produces concepts and categories
Key Concepts
  • Open coding: Initial labeling
  • Axial coding: Relating categories
  • Selective coding: Core category integration
  • Memos: Analytical notes throughout

Best for: Understanding processes, developing explanatory theory, when little existing theory exists

Interpretative Phenomenological Analysis (IPA)

IPA

Purpose: Explore how individuals make sense of significant life experiences

Key Features
  • Phenomenological: focus on lived experience
  • Hermeneutic: interpretive, double hermeneutic
  • Idiographic: detailed analysis of each case first
  • Small, homogeneous samples (3-6 typical)
Process
  1. Read and re-read transcript
  2. Initial noting (descriptive, linguistic, conceptual)
  3. Develop emergent themes
  4. Search for connections across themes
  5. Move to next case
  6. Look for patterns across cases

Best for: Health psychology, illness experiences, identity, life transitions, deep personal experiences

Narrative Analysis

Narrative Analysis

Purpose: Analyze stories people tell, focusing on structure, content, and how stories create meaning

Approaches
  • Structural: How is the story organized?
  • Thematic: What is the story about?
  • Performative: What does telling accomplish?
  • Dialogic: How is the story co-constructed?
Labov's Narrative Structure
  • Abstract: What's this about?
  • Orientation: Who, what, when, where?
  • Complicating action: What happened?
  • Evaluation: So what?
  • Resolution: How did it end?
  • Coda: Return to present

Best for: Life histories, identity research, how people make sense of experiences over time

Discourse Analysis

Discourse Analysis

Purpose: Examine how language constructs social reality, identities, and power relations

Types
  • Discursive Psychology: How people use language in interaction
  • Foucauldian DA: How discourses construct subjects and regulate behavior
  • Critical DA: How language reproduces power and ideology
Focus Areas
  • Word choice and metaphor
  • What's present vs. absent
  • Subject positions offered
  • Ideological assumptions
  • Power relations

Best for: Policy analysis, media studies, power/inequality research, social constructionist studies

Framework Analysis

Framework Analysis

Purpose: Systematic analysis particularly suited for applied research with specific questions and timeframes

Key Features
  • Matrix-based approach
  • Allows within and across case analysis
  • Highly systematic and transparent
  • Good for team research
  • Can accommodate deductive frameworks
Process
  1. Familiarization
  2. Identifying thematic framework
  3. Indexing (applying framework)
  4. Charting (creating matrix)
  5. Mapping and interpretation

Best for: Policy research, applied health research, when team needs shared framework, government-funded projects

Choosing an Approach

Approach Main Question Output
Thematic Analysis What patterns exist across data? Themes
Content Analysis What categories appear and how often? Categories (± frequencies)
Grounded Theory What explains this process? Theory/model
IPA How do individuals experience X? Experiential themes
Narrative What stories do people tell? Story types/structures
Discourse How does language construct X? Discourses/positions
Framework What does the data show about X? Framework-organized findings

Matching Method to Question

  • "What is the experience of...?" → IPA or Thematic Analysis
  • "What are the main themes?" → Thematic Analysis
  • "What theory explains...?" → Grounded Theory
  • "How do people story...?" → Narrative Analysis
  • "How is X constructed through language?" → Discourse Analysis
  • "What does the policy say about...?" → Framework or Content Analysis
Topic 5

Rigor and Trustworthiness

How do we ensure qualitative research is credible and high-quality? Unlike quantitative research with validity and reliability, qualitative research uses different criteria— collectively called trustworthiness—to evaluate rigor.

Lincoln & Guba's Trustworthiness Criteria

Credibility

(Parallel to internal validity)

Question: Are findings believable and accurate representations of participants' views?

Strategies:
  • Prolonged engagement: Sufficient time with data/participants
  • Triangulation: Multiple sources, methods, or analysts
  • Member checking: Participants verify findings
  • Peer debriefing: Colleague reviews analysis
  • Negative case analysis: Seek disconfirming evidence

Transferability

(Parallel to external validity)

Question: Can findings be applied to other contexts?

Strategies:
  • Thick description: Rich, detailed account of context, participants, and findings
  • Clear sampling: Describe who, why, and how participants were selected
  • Context details: Enable readers to judge applicability to their setting

Note: Transferability judgment is made by the reader, not the researcher—you provide the information they need to judge.

Dependability

(Parallel to reliability)

Question: Is the research process logical, traceable, and clearly documented?

Strategies:
  • Audit trail: Document all decisions, changes, methods
  • Research diary: Record methodological decisions
  • Clear procedures: Describe analysis steps in detail
  • Code definitions: Maintain codebook with examples

Confirmability

(Parallel to objectivity)

Question: Are findings shaped by participants rather than researcher bias?

Strategies:
  • Reflexivity: Acknowledge and examine your influence
  • Audit trail: Show how findings derive from data
  • Triangulation: Multiple perspectives reduce individual bias
  • Grounding claims: Support interpretations with evidence

Reflexivity

Reflexivity is the process of critically examining your own role, assumptions, and influence on the research. In qualitative research, the researcher is the instrument—your background, values, and perspectives shape data collection and interpretation.

Personal Reflexivity

How do my background, experiences, and values influence the research?

  • Your identity (gender, culture, class)
  • Your experiences with the topic
  • Your assumptions and beliefs

Epistemological Reflexivity

How do my theoretical and methodological choices shape findings?

  • Research design decisions
  • Questions asked (and not asked)
  • Analytical framework chosen

Example Reflexivity Statement

"As a former teacher myself, I approached this study with insider knowledge of the profession, which helped build rapport with participants. However, I was mindful that my own positive experience might lead me to overlook or minimize negative aspects participants described. I used memoing throughout to examine my reactions and sought feedback from a colleague outside education to challenge my interpretations."

Triangulation

Triangulation involves using multiple sources, methods, or perspectives to enhance credibility and provide a more comprehensive understanding.

Data Triangulation

Multiple data sources

Interviews + observations + documents

Investigator Triangulation

Multiple researchers

Two analysts code independently, then compare

Theory Triangulation

Multiple theoretical lenses

Interpret data using different frameworks

Methodological Triangulation

Multiple methods

Combine interviews with surveys

Member Checking

Member checking (respondent validation) involves returning to participants to verify that your interpretations accurately represent their views and experiences.

Approaches:

Transcript Review

Send transcript for accuracy check

+ Easy to implement

- Doesn't check interpretation

Summary Feedback

Share summary of findings

+ Checks interpretation

- May overwhelm participants

Follow-up Interview

Discuss interpretations together

+ Dialogue deepens understanding

- Time-intensive

Caution

Member checking has limitations. Participants may not recognize their experience in abstracted themes, may have changed views since the interview, or may not want to disagree with the researcher. Treat member feedback as additional data, not final verification.

Quality Criteria Summary

Qualitative Research Quality Checklist

Area Questions to Ask
Research Design Is the method appropriate for the research question?
Sampling Is the sample appropriate and described clearly?
Data Collection Are methods described in sufficient detail?
Analysis Is the analytic process transparent and systematic?
Reflexivity Has the researcher examined their own influence?
Evidence Are claims supported with data (quotes)?
Credibility Were strategies used to enhance trustworthiness?
Ethical Were ethical considerations addressed?

Common Threats to Quality

  • Anecdotalism: Cherry-picking quotes without systematic analysis
  • Over-claiming: Generalizing beyond your data
  • Thin description: Insufficient context for reader judgment
  • Missing deviant cases: Ignoring data that doesn't fit
  • No reflexivity: Not acknowledging researcher influence
Summary

Module 11 Key Takeaways

What You've Learned

  • Qualitative analysis is iterative, interpretive, and requires immersion in data before coding
  • Coding transforms data into analyzable units; types include descriptive, in vivo, process, emotion, and theoretical codes
  • Thematic analysis follows six phases: familiarization → coding → generating themes → reviewing → defining → writing
  • Different approaches (grounded theory, IPA, narrative, discourse) suit different research questions
  • Trustworthiness is established through credibility, transferability, dependability, confirmability, and reflexivity

Next Steps

In Module 12: Mixed Methods Research, you'll learn how to combine quantitative and qualitative approaches to gain more comprehensive insights into research questions.

Continue to Module 12
Practice

Qualitative Analysis Practice

Applied Analysis Tasks

  1. Coding Practice: Read the following excerpt and apply at least 3 different types of codes:
    "When I first started working from home, I thought it would be amazing—no commute, flexible hours. But six months in, I realized I was working more than ever. The boundaries just disappeared. I'd be answering emails at 10pm, feeling guilty if I wasn't always available. My partner started calling me a 'work zombie.'"
  2. Theme Development: Given these codes, propose a theme name and brief definition:
    • Losing boundaries
    • Always-on mentality
    • Work invading home
    • Relationship strain
    • Guilt about disconnecting
  3. Reflexivity: Write a brief reflexivity statement (3-4 sentences) for a study on student stress, considering:
    • Your own experience as a student
    • How this might influence your interpretation
    • What you'll do to address potential bias
  4. Method Selection: Which analytic approach would you use for:
    • Understanding the experience of cancer diagnosis
    • Developing a theory of leadership in startups
    • Analyzing how media frames climate change
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