thematic analysis Flashcards
Thematic analysis (Braun & Clarke, 2006)
• One of the simplest methods of data analysis
• It is flexible - compatible with both experiential and constructionist paradigms
• Provides a rich and detailed, yet complex account of data
• What is it then?:
“Thematic analysis is a method for identifying,
analysing, and reporting
patterns (themes) within data. It minimally
organises and describes your
data set in (rich) detail.” (ibid p.6)
A rich description of the data set, or a detailed account of one particular aspect? (Braun & Clarke, 2006)
choices in thematic analysis
A rich thematic description of your entire data set, so that the reader gets a sense of them predominant or important themes (complexity is lost here but rich description achieved)
Or
A more detailed and nuanced account of one particular theme, or group of themes, within the data
Inductive (data driven)
- ‘Bottom up’ approach
- Focus on the data
- Don’t necessarily link directly to interview schedule themes
- Driven less by researcher’s interest or prior reading of topic
- No pre-existing coding frame (research question can evolve through coding)
What is a theme?
- It represents some level of patterned response or meaning within the data set.
- “Conversation topics, vocabulary, recurring activities, meanings, feelings, or folk sayings and proverbs” that occur across data (Taylor & Bogdan, 1989:131).
- Themes are identified by “bringing together components or fragments of ideas or experiences, which often are meaningless when viewed alone” (Leininger, 1985: 60).
- The “coherence of ideas rests with the analyst who has rigorously studied how different ideas or components fit together in a meaningful way when linked together” (ibid).
Identifying a theme: advice
- Ideally there will be a number of instances of the theme across the data set.
- It typically should be prevalent across data and also across the majority of participants.
steps in thematic analysis
- Transcription of data (if appropriate) – analysis begins here!
- Immerse yourself in data
- Make notes about first impressions, ideas, hunches etc. as you become acquainted with the text.
- Initial coding of data - go through text line-by-line, section-by-section, generating [descriptive] themes as you do so
- Again, make notes as you do this: interpretations
- Pause periodically to organise set of themes into clusters [which themes go together? etc.]
- Gradually move from descriptive to conceptual themes, and from many themes to few.
- Repeat process for other texts and compare themes to produce common themes
Coding and themes
- End up with collection of candidate themes and sub themes (with linked data extracts to support)
- You could produce a thematic map (see Braun & Clarke, 2006).
- Does this reflect data as a whole?
- Review and refine – what aspect does theme capture?
- Idea not just to paraphrase content of data extracts but identify what is of interest about them and why
- Name themes (conceptual)
- Write a detailed analysis of each theme – think about how this fits with your overall research question/focus.
- Point is to develop a coherent argument through analysis of data
Analysis
- Analysis involves a constant moving back and forward between the entire data set and the coded extracts of data that you are analysing.
- Writing is an integral part of analysis, not something that takes place at the end, as it does with statistical analyses.
- Writing should begin in phase one, with the jotting down of ideas and potential coding schemes, and continue right through the entire coding/analysis process.
Refining your themes
- Each individual theme needs a detailed analysis.
- Each theme tells a story and you need to consider how it fits into the broader overall story that you are telling about your data.
- You may need to refine themes.
- Does a theme contain any sub-themes?
When to link analysis to wider literature?
- Some argue that early reading can narrow your analytic field of vision, leading you to focus on some aspects of the data at the expense of other potential crucial aspects.
- Others argue that engagement with the literature can enhance your analysis by sensitising you to more subtle features of the data (Tuckett, 2005).
- There is no one right way to proceed with reading and incorporate that into your analysis.
(READING) What is thematic analysis?
thematic analysis is the analysis of textual material (newspapers, interviews, and so forth) in order to indicate the major themes to be found in it. Thematic analysis seems like a poor relative of other qualitative methods, and often appears sloppily conducted and very subjective in comparison.
(READING) what counts as a theme?
The researcher digs deeper into the text of the lecture to identify a variety of themes which describe significant aspects of the text.
For example, the following themes may be present in a lecture given about eye witness testimony: the unreliability of eyewitness testimony, the ways of improving the accuracy of testimony and methodological problems with the research into eyewitness testimony.
(READING) What is a qualitative method that often competes with thematic analysis?
grounded theory analysis
(READING) Why is thematic analysis more accessible to novices?
Thematic analysis is not dependent on specialised theory as some other qualitative techniques are such as discourse analysis and conversation analysis. Consequently, thematic analysis is more accessible to novices unfamiliar with the general theory underlying qualitative methods. It can be used with textual data of just about any sort including in-depth interviews, focus groups, and so forth.
(READING) why can TA be really good or really bad?
In thematic analysis, researchers identify a limited number of themes which adequately reflect their textual data. Hard to do well, a few superficial themes are easily generated but they do not reflect the required level of analysis adequately. It does not demand a closely detailed analysis therefore could go good or bad.
