thematic analysis Flashcards
What is thematic analysis?
Thematic analysis is a qualitative method used to identify, analyse, and interpret patterns of meaning (themes) within qualitative data.
What kind of data is thematic analysis used on?
It is used on qualitative data, such as interviews, open-ended questionnaires, diaries, or transcripts of conversations.
What is a ‘theme’ in thematic analysis?
A theme is a patterned response or meaning that captures something important about the data in relation to the research question. It must be recurring and meaningful.
What are the six stages of thematic analysis (according to Braun & Clarke)?
- Familiarisation with the data
- Generating initial codes
- Searching for themes
- Reviewing themes
- Defining and naming themes
- Producing the report
What does the coding stage involve in thematic analysis?
Coding means identifying repeated patterns or significant points in the data and labelling them with short phrases or keywords that summarise their content.
What is the purpose of reviewing themes in thematic analysis?
To ensure themes are internally coherent and distinct from one another, and that they accurately represent the data set.
What’s the difference between content analysis and thematic analysis?
• Content analysis often quantifies themes (frequency counts)
• Thematic analysis is more interpretative, focusing on the meaning behind patterns without reducing them to numbers.
What are strengths of thematic analysis?
• Flexible and can be applied across various data types
• Captures rich, detailed insights
• Useful for exploratory research
• Helps identify implicit meanings
What are limitations of thematic analysis?
• Can be subjective and influenced by researcher’s bias
• Lacks scientific rigour if not systematically applied
• Difficult to replicate (low reliability)
How can researchers ensure reliability in thematic analysis?
• Use inter-rater reliability (multiple coders)
• Keep a clear audit trail of coding and theme development
• Operationalise themes clearly
• Be transparent about method and researcher reflexivity
What do examiners say about common mistakes in thematic analysis questions?
• Students often describe content analysis instead
• Failing to mention stages of analysis
• Not linking themes back to research aims
• Being too vague with what the “themes” actually are
How could you apply thematic analysis in an exam scenario?
- Describe data source (e.g., interview transcripts)
- Explain coding process
- Identify how themes would be grouped
- Discuss how you’d interpret and report them
- Address ethical and reliability concerns
Why is reflexivity important in thematic analysis?
It involves being aware of how the researcher’s beliefs and experiences may influence data interpretation. Enhances credibility and transparency.
What is meant by data saturation in thematic analysis?
Data saturation occurs when no new themes or insights emerge from the data — an indicator that the analysis is complete.
