Week 3 Study Notes on Thematic Analysis for Research Design & Practice 1

Overview of Thematic Analysis

• Course details: Thematic Analysis in Research Design & Practice 1 at Nottingham Trent University

Learning Outcomes

• Understand what Reflexive Thematic Analysis (TA) is.

• Know how to conduct TA step by step.

• Know how to code data and explore different coding strategies.

• Understand themes and the development process.

• Recognize the differences between data-driven and theory-driven TA.

Introduction to Thematic Analysis

• Reference: Braun & Clarke (2006) defines thematic analysis as:

  • “Thematic analysis is a method for identifying, analysing, and reporting patterns (themes) within data. It organises and describes your data set in rich detail.”

• Characteristics of Thematic Analysis:

  • Simplicity: One of the simplest methods of qualitative data analysis.

  • Flexibility: Compatible with many research paradigms.

  • Detail: Provides a rich and complex account of data.

Reflexive Thematic Analysis (Braun & Clarke, 2021)

• Focuses on researcher self-awareness.

• Emphasizes critical reflection on how personal perspectives shape analysis and conclusions.

  • Important questions to consider:

  • Why might I be reacting to the data in this way?

  • What does my interpretation rely on?

  • What different ways could I make sense of the data?

Six Steps of Reflexive Thematic Analysis

1. Familiarisation with the dataset.

2. Coding.

3. Generating initial themes.

4. Developing and reviewing themes.

5. Refining and naming themes.

6. Writing up.

• Additional context: Referencing a worked example from Byrne (2022).

Step 1: Familiarisation

• Importance of deep knowledge of dataset—immersion is key.

• Critical engagement with the data involves addressing questions like:

  • How does the person make sense of the topic?

  • Why might they interpret it this way?

  • In what different ways do they approach the topic?

  • What assumptions do they articulate?

• Note-taking:

  • Record ideas around data, possibly in a research diary.

  • Maintain a systematic overview of potential patterns.

Step 2: Coding

• Coding defined as:

  • A meaningful piece of transcript/data that captures specific meanings relevant to the research question.

  • Utilizes short labels that encapsulate content.

• Characteristics of Coding:

  • Systematic, organic, evolving, subjective.

  • Codes should connect multiple data segments.

  • Subjective nature influenced by researcher’s interpretation.

  • Validating the richness of codes by potentially tagging one segment under different codes.

Data-Driven versus Theory-Driven TA

Data-Driven (Inductive/Bottom Up)

• Based on data itself.

• Develops themes without a guiding theoretical framework.

• Less influenced by researcher’s prior interest or reading.

• No pre-existing coding frame.

Theory-Driven (Deductive/Top Down)

• Begins with a theoretical framework.

• Aims to test or confirm this framework based on literature gaps.

• May utilize a pre-existing coding frame inspired by prior readings.

Doing Coding

• Process includes:

  • Line-by-line coding of data.

  • Rigorous tagging of meaningful segments.

  • Making use of Word’s ‘New Comment’ feature or handwritten labels.

  • Creating documents for each code and correlating transcripts.

  • Software aids like NVivo for qualitative analysis.

Step 3: Generating Initial Themes

• Definition of a theme:

  • Captures shared meaning by a central organizing concept.

• Themes must be developed/generated rather than simply identified.

• Consideration of sub-themes for deeper insights:

  • Secondary themes exploring specific aspects of the main theme.

Initial Themes Development

• Consider all codes and look to cluster together similar codes.

• Identify core ideas with potential variation for theme development.

• Evaluate what narrative each provisional theme tells regarding the dataset.

• Importance of uniqueness and distinctiveness in themes:

  • Avoiding overly broad topic summaries (e.g., topic summary vs. thematic expression).

Five Key Aspects of Theme Development

1. Themes do not encapsulate every facet of the dataset.

2. Each must have a central organizing concept.

3. Themes should be treated as candidate themes.

4. Anticipate starting with more themes than the final count.

5. Avoid a question-and-answer orientation to ensure exploration of patterned meanings.

Step 4: Developing and Reviewing Themes

• Review process involves:

  • Ensuring themes go beyond simple descriptions.

  • Validating the significance and uniqueness of themes.

  • Assessing overlap and diversity of ideas across themes.

Theme Development Checklist

• Evaluate whether themes convey meaning.

• Review coherence with a central idea.

• Check for clear boundaries, and consider activating subthemes if required.

Step 5: Refining and Naming Themes

• Definition: A clear, concise statement clarifying what a theme embodies.

• Importance of naming themes to accurately convey essence:

  • Poorly named themes lead to misinterpretation of data.

  • Avoid overly descriptive or interpretive names.

Example Study: Foodbank Clients and Volunteers

• Reference: Bowe et al. (2019) study provides thematic insights:

  • Themes include:

  • ‘Here to help’: indicates emotional support and no judgment from volunteers.

  • ‘The legitimate recipient’: strategies clients utilize to counteract stigma.

Step 6: Writing Up Analysis

• Initial easing into this process recommended for early studies.

• Essential practice includes reading other existing thematic analysis papers.

Common Mistakes in TA Write-Ups

• Underestimating difficulty and time commitment.

• Failing to analyze data appropriately.

• Allowing analysis to rely overly on interview schedules.

• Ensuring clarity in the relationship between data and analytic claims.

• Recognizing and addressing overlap in themes.

Conclusion

• Emphasis on systematic engagement with thematic analysis stages for proficient research outcomes.