Lecture 8

Question 1

Thematic analysis

Answer 1

Thematic analysis = “a method for identifying, analysing and reporting patterns (themes) within data. It minimally organises and describes your data in (rich) detail. However, frequently it goes further than this, and interprets various aspects of the research topic” (Braun & Clarke, 2006: 79).

(more on notes)

Question 2

Thematic analysis in a post-positivist way

Answer 2

• Data concerns: concerns for accuracy, reliability, controlling for researcher bias or subjectivity.
• Accuracy of the participant’s account: “Each participant received a copy of her interview transcript by mail and was invited to correct, elaborate upon, or modify her comments to make the transcript a more complete and accurate account of her experience.” (Bond et al., 2008: 52)
• Implies that there is a singular truth of participant’s experiences that research should seek to uncover.
• Themes are often implicitly conceptualized as buried treasure – entities that pre-exist the analysis that the researcher merely uncovers or discovers in their data.
• Research = a process of discovery.

Question 3

Implications for thematic qualitative data analysis

Answer 3

• Overriding concern with accuracy and reliability of coding, and minimising researcher bias or subjectivity.
• Structured codebooks or coding frames – which are then applied to the data.
• Themes that are determined prior to, or early on in, data analysis (i.e. theory-driven, deductive)
• Coding understood as a process of allocating the data to the correct pre-determined theme.
• Multiple coders working independently to code the same data.
• Measures of the level of ‘agreement’ (or inter-coder reliability = inter-rater reliability) between coders
• Consensus coding – coders agreeing the final data coding.

Question 4

Critical questions (tqda)

Answer 4

• Theory-driven codes risk missing out on important information, coding may remain superficial or captures only the more obvious themes.
• Is 100% coding agreement possible? (may depend on the types of date and of codes used, easier to agree on ‘rejected for a job’ than ‘anxiety to start a new job’)
• Depth and complexity of understanding may be sacrificed for reliability and accuracy.

Question 5

Thematic analysis from a constructionist perspective

Answer 5

• Qualitative research is more than collecting and analysing words (or images) as data, it’s about embracing a philosophy or set of values about how we do research, about the role of researcher in research and what counts as meaningful knowledge.
• Themes are not just ‘there’ but are generated as a result of a researcher’s interpretative framework, prior training, skill, assumptions etc.
• Subjectivity is a resource for research not a problem to be managed (compare with lectures on participant observation and ethnography)
• Understandings of the world generated by research are always shaped by the researcher and situated in particular contexts.
• Understandings are always partial – and that’s okay!

Question 6

Steps in thematic analysis

Answer 6

(see on notes)

Question 7

What is a theme?

Answer 7

a theme is a pattern that captures something significant or interesting about the data and/or research question. As Braun & Clarke (2006) explain, there are no hard and fast rules about what makes a theme. A theme is characterised by its significance.

• Themes as ‘topic summaries’
• A theme can be anything…
• Developed early on and guide coding.
• Developed later and represent the outcome of coding.

(exmple on notes)

Question 8

Framework analysis

Answer 8

• Development in 1990s (Ritchie & Spencer)
• Developed for applied research, pragmatic orientation
• Thematic orientation (i.e. substantive)
• Developing thematic framework (data-driven or theory-driven)
• Distinctive features:
  
  • Working with data summaries
  • Making use of a matrix-> quick and easy comparison

Question 9

Steps in framework analysis

Answer 9

( see on notes)

Question 10

Critical points (FA)

Answer 10

• Framework analysis will only work with comparable data – similar themes have to be addressed in the interviews. Semi-structured interviews lend themselves well for this type of analysis.
• Risk of too much data reduction
• Risk of quantification
• Often pretends not to operate in a particular paradigm, but is this possible? (Researchers will always want to claim quality, but what defines quality?)