thematic analysis
Year : Tags : Authors: Braun Clarke
Maybe read this: https://www.tandfonline.com/doi/abs/10.1080/13645579.2016.1195588?journalCode=tsrm20 !./docs/thematic analysis.pdf
The site for thematic analysis:
https://www.psych.auckland.ac.nz/en/about/thematic-analysis.html
identify, analyze, and report patterns within the data. Minimally organize and describe your data set in rich detail
talks a bit about differences with other qualitative analysis and how the researcher is an active participant in turning data into themes
Never say that themes emerge from the data, you were the one who emerged the themes
A theme is a truth across the entire dataset, though it may be applicable to some more than others. e.g. “AR is slower” is true regardless, but some care about it more than others and it affects different people in different ways
how many times does something need to show up before it’s a theme? It’s up to youuuu. Plus if something doesn’t show up a lot it can still be important
inductive thematic analysis and deductive thematic analysis
semantic analysis or latent analysis?
reach across the data set for repeated patterns of meaning. Read and reread the data
start “coding” the data, writing down the basic features that seem interesting
the same data can be coded with multiple things create candidate themes and all the extracts of data that have been coded to them
review your themes. discard ones that don’t have enough data, combine similar ones, etc
themes are formed of subthemes, human subcultures are fractally nested
remember to actually analyze the data and go beyond what it says
The six phases:
- Familiarisation with the data
- This phase involves reading and re-reading the data, to become immersed and intimately familiar with its content.
- Coding
- This phase involves generating succinct labels (codes!) that identify important features of the data that might be relevant to answering the research question. It involves coding the entire dataset, and after that, collating all the codes and all relevant data extracts, together for later stages of analysis.
- Generating initial themes
- This phase involves examining the codes and collated data to identify significant broader patterns of meaning (potential themes). It then involves collating data relevant to each candidate theme, so that you can work with the data and review the viability of each candidate theme.
- Reviewing themes
- This phase involves checking the candidate themes against the dataset, to determine that they tell a convincing story of the data, and one that answers the research question. In this phase, themes are typically refined, which sometimes involves them being split, combined, or discarded. In our TA approach, themes are defined as pattern of shared meaning underpinned by a central concept or idea.
- Defining and naming themes
- This phase involves developing a detailed analysis of each theme, working out the scope and focus of each theme, determining the ‘story’ of each. It also involves deciding on an informative name for each theme.
- Writing up
- This final phase involves weaving together the analytic narrative and data extracts, and contextualising the analysis in relation to existing literature.
!./docs/Reflecting On Reflexive Thematic Analysis-Braun.pdf
This didn’t really add too much, essentially Braun & Clarke had unstated assumptions about their level of qualitative analysis knowledge that they want to go back and fix
!./docs/thematic analysis FAQ.pdf
talks about central organizing concepts but tbh I don’t really understand what it’s talking about…
has some interesting links to other forms of analysis and coding
