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Reflexive Thematic Analysis (RTA)

1. Explain the principles of Reflexive Thematic Analysis

1.1. Overview of Thematic Analysis (TA)

  • Purpose: Identifies, analyses, and reports patterns (themes) across qualitative data
  • Focus: Explores what is meaningful about a topic and represents shared experiences/ideas
  • Characteristics:
  • Flexible - applicable to various data types and research questions
  • Not tied to one theoretical framework
  • Accessible and well-suited for student projects

1.2. Three Versions of Thematic Analysis (Braun & Clarke, 2021)

1.2.1. Coding Reliability or Structured Approaches

  • Influenced by quantitative traditions
  • Researcher as relatively neutral observer
  • Aims for coding consistency and replicability
  • Uses shared codebooks and inter-rater reliability checks
  • Values consistency and replicability
  • Common in large team-based projects or applied settings

1.2.2. Codebook or Hybrid Approaches

  • Middle ground between structured and reflexive forms
  • Begins with collaboratively developed codebook
  • Allows flexibility for new ideas to emerge
  • Acknowledges interpretation while maintaining structure
  • Useful for projects needing both rigor and comparability

1.2.3. Reflexive Thematic Analysis (RTA)

  • Emphasizes: Researcher's active and interpretive role
  • No single "correct" set of codes or themes
  • Analysis is: Creative, flexible, and reflexive
  • Values: Depth, transparency, and reflexive awareness
  • Coding: Organic and evolving, not fixed or rule-bound
  • Meaning construction: Through interaction between researcher and data

1.3. Theoretical Foundations of RTA

1.3.1. Constructivist and Interpretivist Assumptions

  • Constructivism: Meaning is constructed through experiences, language, and social interactions
  • Interpretivism: Focuses on understanding how people make sense of experiences
  • Key principle: Meaning is produced, not discovered

1.3.2. Constructing Meaning in Practice

  • Meaning is produced, not discovered: Themes are developed through reflective interpretation
  • Researcher-data connection: Background, assumptions, and interests influence interpretation
  • Themes are interpretive: Explore what accounts mean in research context, not just summarize

Trying to understand how people construct meaning, and how you, as the researcher, also play a role in shaping that interpretation

1.4. From Description to Interpretation

1.4.1. Descriptive vs. Interpretive Analysis

Example: Interviews about cooking and eating experiences

Descriptive Reading (Surface level):

  • “People enjoy eating with others because it feels more sociable and fun”
  • Stays close to surface content of data

Interpretive Reading (Deeper meaning):

  • Explores cultural/emotional meanings of sharing food
  • Examines how food practices relate to identity, care, or social connection
  • Potential themes: "Food as expression of care" or "Cooking as relational work"

1.4.2. Key Distinction

  • Description: Tells us what participants said or did
  • Interpretion: Helps us understand what those words/actions mean in context

1.5. Core Principles of Reflexive Thematic Analysis

  1. Active Researcher Role: Researcher is not neutral but actively constructs meaning
  2. Reflexivity: Continuous awareness of how researcher's perspective shapes analysis
  3. Organic Process: Coding and theme development evolve naturally
  4. Transparency: Clear documentation of analytical decisions and reasoning
  5. Interpretive Depth: Moves beyond description to explore underlying meanings
  6. Contextual Understanding: Considers broader social and psychological contexts

1.6. Practical Implications for Analysis

  • No predetermined codebook: Codes emerge from engagement with data
  • Iterative process: Move back and forth between data and emerging themes
  • Reflexive documentation: Keep notes on analytical decisions and personal reflections
  • Theme development: Focus on meaningful patterns rather than frequency counts
  • Interpretive storytelling: Themes capture core ideas and relationships in data

2. Describe the Six Phases of Thematic Analysis

2.1. Overview of the Six-Phase Process (Braun & Clarke, 2006, 2021)

  • Structured but flexible process for moving from raw data to meaningful themes
  • Iterative and recursive - move back and forth between phases as understanding develops
  • Guides thinking rather than providing rigid formula

2.2. The Six Phases

2.2.1. Familiarisation with the Data

  • Purpose: Immerse yourself in the data to know it thoroughly
  • Activities: Read and re-read transcripts, listen to recordings, note ideas/emotions/recurring issues
  • Goal: Notice what stands out, surprises, or seems relevant (not yet analysis)
  • Importance: Foundation for deeper analysis; skipping leads to superficial coding

2.2.2. Generating Initial Codes

  • Purpose: Identify interesting or meaningful features in the data
  • Codes: Concise labels capturing important aspects of text segments
  • Process: Break dataset into manageable pieces for pattern recognition
  • RTA Approach: Flexible, interpretive coding - multiple codes per extract, revisable

2.2.3. Searching for Themes

  • Purpose: Look across dataset to see how codes cluster together
  • Themes: Broader patterns of meaning that answer research questions
  • Activities: Group codes, rename/merge them, map connections between ideas
  • Characteristics: Exploratory phase requiring creativity and reflection

2.2.4. Reviewing Potential Themes

  • Purpose: Check how well themes fit coded data and entire dataset
  • Process: Combine, split, or discard themes based on scrutiny
  • Goal: Ensure each theme captures coherent, meaningful, distinct pattern
  • Importance: Adds rigor and maintains link between data and interpretation

2.2.5. Defining and Naming Themes

  • Purpose: Refine and articulate what each theme captures and why it matters
  • Activities: Identify theme "essence," consider relationships with other themes
  • Naming: Develop clear names that communicate meaning
  • Outcome: Deepens interpretation and prepares for writing

2.2.6. Producing the Report

  • Purpose: Weave themes into coherent narrative telling analytic story
  • Activities: Select vivid extracts, link findings to research question and literature
  • Writing as Analysis: Writing process itself clarifies and communicates meaning

2.3. Why the Phases Matter

  • Structure and transparency: Systematic work and clear explanation of decisions
  • Iterative nature: Revisit earlier phases as ideas evolve
  • Connected process: Each phase contributes to moving from data to insight
  • Progression: From what participants said → what their words mean in research context

2.4. Iterative Nature of the Process

  • Familiarisation informs coding
  • Reviewing themes may prompt new codes
  • Writing can refine interpretation
  • Continuous movement between phases as understanding deepens

3. Describe and apply the process of data familiarisation

3.1. Overview of Data Familiarisation

  • First phase of thematic analysis - foundation for everything that follows
  • Goal: Become deeply immersed in dataset to understand:
  • What participants said
  • How they said it
  • Contexts that give their words meaning
  • Not analysis yet - getting to know the data

3.2. Process of Familiarisation

  • Careful, repeated engagement with data
  • Activities:
  • Reading and re-reading transcripts
  • Listening to recordings
  • Noticing ideas, emotions, and patterns
  • Where relationship with data begins to take shape

3.3. Conceptual Understanding

  • Purpose: Why immersion matters and how it strengthens later analysis
  • Connection: Links to reflexive, interpretive nature of thematic analysis
  • More than quick read-through: Requires slowing down to really listen and observe

3.4. Practical Strategies for Data Familiarisation

  • Active reading and annotation
  • Listening carefully to tone and emphasis in recordings
  • Making brief analytic and reflexive notes
  • Managing and organising transcripts effectively
  • Building habits of careful observation and reflection

3.5. Key Idea

  • Familiarisation is about: Immersion, curiosity, and reflection
  • Stronger understanding now → deeper analysis later

4. Describe and apply the process of coding qualitative data

4.1. Overview of Coding

  • Next stage after data familiarisation
  • Purpose: Begin organising data by identifying and labelling key features relevant to research question
  • Bridge between raw text and deeper analysis
  • More than highlighting: Making analytic choices

4.2. Nature of Codes

  • Capture meaningful elements: Ideas, emotions, or concepts
  • Help make sense of the data
  • Transform large, unstructured dataset into meaningful building blocks

4.3. Conceptual Understanding of Coding

  • Essential to thematic analysis process
  • Two types of codes:
  • Semantic codes: Stay close to surface meaning of what participants said
  • Latent codes: Interpret underlying ideas or assumptions
  • Iterative process: Revisit and refine as understanding deepens

4.4. Practical Coding Process

  • Reading through transcripts and identifying data extracts worth coding
  • Writing clear, concise code labels
  • Examples of semantic and latent codes
  • Practical ways to keep coding consistent
  • Systematic and confident coding approach

4.5. Key Idea

  • Coding is the bridge between data and analysis
  • Transforms what participants said into meaningful insights

5. Identify the qualities of a strong thematic analysis

5.1. Understanding Quality in RTA

  • Not about rigid checklists or "right" answers
  • About: Careful thinking, systematic work, and honest reflection
  • Acknowledges researcher's role in the process

5.2. Four Principles of Quality (Braun & Clarke, 2021)

5.2.1. Coherence

  • Clear and convincing story about data
  • Themes fit together logically
  • Aligns with research question and theoretical approach
  • Visible "thread" connecting data extracts to overall interpretation

5.2.2. Transparency

  • Shows how conclusions were reached
  • Makes analytic process visible
  • Describes generation, review, and refinement of codes and themes
  • Helps others understand reasoning behind decisions

5.2.3. Depth

  • Goes beyond summarising what participants said
  • Interprets patterns of shared meaning
  • Identifies "fully realised themes" - not just topic summaries
  • Reveals important insights about how people make sense of experiences

5.2.4. Reflexivity

  • Acknowledges researcher's active role
  • Recognises how background, values, and assumptions shape interpretation
  • Adds honesty and depth to work
  • Not about eliminating influence but reflecting on it

5.3. Foundation of Quality in RTA

  • Interpretive, creative, and transparent process
  • Constructing meaning rather than mechanical analysis
  • Combined principles form foundation for rigorous qualitative work

6. Reflect on and articulate your role in the analysis through a reflexive statement

6.1. Importance of Reflexivity

  • Researchers are not neutral observers
  • Background, beliefs, and experiences shape data interpretation
  • Reflexive statement: Helps think about these influences for more thoughtful, transparent work

6.2. What is a Reflexive Statement?

  • Short piece of writing about how background, assumptions, and experiences affect analysis
  • Not about "right answers" - about awareness of interpretive influence
  • Helps:
  • Recognise own perspective
  • Stay open to new patterns
  • Be transparent about analytic choices
  • Strengthen quality and honesty

6.3. Timing of Reflection

  • Before beginning analysis - approach with more awareness and care
  • Saves time later - material ready for write-up stage

6.4. Reflective Workbook Activity

6.4.1. Reflection Prompts

  • Ideas or expectations brought to the topic
  • Background, experiences, or identity influences (age, culture, education)
  • Feelings during data collection (interviewing or being interviewed)
  • Strengths brought to analysis
  • Challenges perspective might create
  • Strategies to stay open and reflective during coding

6.4.2. Format Guidelines

  • Brief notes or bullet points acceptable
  • Detailed enough to remind of thinking at this stage
  • Will be returned to when writing lab report
  • Normal discomfort - goal is careful thinking, not personal sharing

6.5. Practical Benefits

  • Increases awareness of interpretive lens
  • Enhances transparency in research process
  • Strengthens analytical rigor
  • Supports development as reflexive researcher