Qualitative lecture 9- Thematic analysis

Question 1

What are some relevant definitions?

Answer 1

• Data corpus: refers to all data collected for a particular research project
• Data set: Refers to all the data from the corpus that is being used for a particular analysis (any one section/category of all your data; all data that speaks about a specific theme. E.g. interviews with car guards and HSD study)
• Data item: refers to each individual piece of data collected. E.g. each interview
• Data extract: Refers to an individual coded chunk of data.

Question 1

Background on thematic analysis.

Answer 1

• It is a widely-used qualitative analytic method.
• It is the first qualitative analysis method that researchers should learn
• It is a foundational method
• It is a method in its own right and a tool in other analyses approaches.
• Thematic analysis as flexible- it applies across theoretical and epistemological approaches.

Question 2

What is thematic analysis?

Answer 2

• A method for identifying, analysis, and reporting patterns (themes) within data. It organises, describes, and interprets the data in detail. It tells us a particular story about the data.
• It is an account of “emerging themes”- this is a passive statement that denies the active role of the researcher.
• Thematic analysis is not tied to any pre-existing theoretical framework
• The theoretical position of TA needs to be made clear (assumptions about the nature of the data, what counts as data etc.)
• A good thematic analysis is transparent about its theoretical positions.

Question 3

What can thematic analysis be?

Answer 3

• Realist or essentialist: reporting experiences, meanings, and the reality of participants
• Constructionist- examining the ways in which events, realities, meanings and experiences are the effects of a range of discourses
• Contextualist: critical realism, meaning-making, broader social context.

Question 4

What decisions does doing thematic analysis involve?

Answer 4

1. What counts as a theme
2. To present a rich description of and entire data set (1) or a detailed account of one particular aspect (2)?
3. Inductive vs deductive/theoretical thematic analysis?
4. Semantic or latent themes?
5. Epistemology shapes the arguments we make about data

Question 5

What does “What counts as a theme” mean?

Answer 5

• A theme captures something important about the data in relation to the research question
• A theme represents a patterned response within the data set.
• Researcher plays a crucial role in determining what counts as a theme
• E.g. space and prevalence in a data set
• Does the theme capture something important in the data set in relation to the research question?

Question 6

What does “To present a rich description of and entire data set (1) or a detailed account of one particular aspect (2)?” mean?

Answer 6

1- Themes need to be an accurate reflection of the entire data set
2- A more detailed and nuanced account on a specific question or area of interest in the data.

Question 7

What does “Inductive vs deductive/theoretical thematic analysis?”

Answer 7

• Inductive: bottom-up and data driven
• Theoretical/deductive: “top down” and driven by the researcher’s analytical and theoretical interests.

Question 8

What does “semantic or latent themes” mean?

Answer 8

• Semantic- explicit or surface level meanings and nothing beyond what participants said (more descriptive)
• Latent- identifies or examines underlying ideas, assumptions that shape or inform the meanings/data (e.g. social constructionist work)

Question 9

What does “Epistemology shapes the arguments we make about data” mean?

Answer 9

• Essentialist/realist: theorising motivations, experiences and meaning in a straightforward way (based on what participants tell us)
• Constructionist- theorising the sociocultural contexts and structural conditions that enable the individual accounts provided.
  E.g. men saying women are better caregivers.

Question 10

What is Braun and Clarke’s 6-phase guide to doing thematic analysis?

Answer 10

1. Familiarise yourself with the data: transcribing the data if necessary, reading and re-reading the data, noting down initial ideas.
2. Generating initial codes: coding interesting features of the data in a systematic fashion across the entire data set, collating data relevant to each code.
3. Searching for themes: Collating codes into potential themes, gathering all data relevant to each potential theme.
4. Reviewing themes: checking in the theme’s work in relation to the coded extracts (level 1) and the entire data set (level 2), generating a “thematic map” of the analysis.
5. Defining and naming themes: Ongoing analysis to refine the specifics of each theme, and the overall story the analysis tells; generating clear definitions and names for each theme.
6. Producing the report: The final opportunity for analysis. Selection of vivid, compelling extract examples, final analysis of the selected extracts, relating back of the analysis to the research question and literature, producing a scholarly report of the analysis.