data visualization
Phase 4: Developing & Revising Themes and Phase 5: Refining, Defining and Naming Themes
(bonus track: with data extracts!)
What is a Theme in Reflexive Thematic Analysis?
A pattern of shared meaning organized around a central concept
Markers of good themes in reflexive TA
are built around a singular central idea or argument
are not topic summaries
illustrate richness and diversity in the manifestation of that idea within the dataset
are not too fragmented or multi-layered
are distinctive from one another– each theme has own focus, own boundaries. Themes don’t merge
weave together to tell an overall story that addresses the research question
Phase 4A: Theme development and revision with coded extracts
Review your tentatively developed themes against all the data that have been tagged with any of the codes clustered for each theme.
Your Guiding Question at this point:
Is this pattern a viable theme – a pattern that has an identifiable central organizing concept, as well as different manifestations of that idea?
Review and development questions for your candidate themes:
Can I identify boundaries of this theme?
Are there enough (meaningful) data to evidence this theme?
Are the data contained within each theme too diverse and wide-ranging?
Does this theme convey something important?
Expect some Revision to your candidate themes
Expect tweaking – refining boundaries, clarifying central organizing concepts
You might expand a theme
You might combine two potential candidate themes into one broader theme.
You might narrow a theme
You might realize part of one theme doesn’t fit, so you pull it out and redraw the theme boundary.
You might split a theme into two, or even three themes. And, sometimes, you might just reject one or more of your initial themes completely.
Phase 4B: Theme development and revision with full data set
Now go back to your full data set
Make certain your themes fit the full data set
If not, revise your themes
Phase 5: Refining, Defining, and Naming Themes
Write a Theme Definition
Is typically 3-5 sentences
Function as an abstract for the theme
Clarifies the theme's central organizing concept or key take-away point
Clarifies the theme’s scope and boundaries, what it includes (and maybe excludes)
Clarifies how the theme relates to other themes and its subthemes
Clarifies how the theme relates to your research question
Name your Themes
A good theme name is a short phrase, or perhaps a heading and subheading, that captures the essence of the theme and engages the reader.
Selecting Data Extracts
two main purposes of data extracts
1. Evidence your themes
2. Allows the reader to judge the fit between your data and your theme definitions and names
Guidelines for selecting data extracts are:
Select vivid examples
Select clear and concise extracts
Select 3 data excerpts that evidence the central organizing concept for each theme (Note: one extracts needs to come from each of your three interviews)
Select other extracts to illustrate the different facets of the theme’s expression.
Edit out unnecessary detail from Data Extracts
Remove irrelevant material in the middle of an excerpt
Indicate the removed text with […]
Do not remove text which contradicts the analytic claim you are making.
This is not about making the data fit your point.
Clarify and Contextualize your Data Extracts
Explain your interpretation – how do you see this specific extract exemplifying your theme??
Provide clarifying information for the extract
For example, quote contains ambiguous reference to ‘her’ (e.g. ‘I told her…’). Include clarification in square brackets within the quoted data (e.g. ‘I told her [officemate]’ or ‘I told [officemate]’).
Provide contextualization with relevant information when you introduce the extract
What does the reader need to know to understand the extract?
Explain where it came from in the data –
e.g. in response to a particular interview question
Don’t forget Interview code and line numbers for each data extract!!