Different Approaches to Thematic Analysis - Braun and Clarke. Youtube lectures Parts 1,2 & 4

Thematic Analysis Lecture - Part 1 https://youtu.be/Lor1A0kRIKU

Notes:

• Reflexive TA - what is it? Look at https://www.psych.auckland.ac.nz/en/about/thematic-analysis.html
• Reference procedures and methodological literature
• Typology clustered TA approaches into three broad types 1) Coding Reliability 2) Codebook 3) Reflexive. Coding Reliability applies a small 'q' of approach of data collection, meaning it uses qualitative techniques with a positivist (relying on scientific evidence) paradigm, largely driven by a desire to demonstrate coding reliability. Codebook would include definition of themes exclusive and inclusive criteria, which would be looked at before looking at the data and then applied to the data. Multiple researchers and coders would work independently and there would be a measure of comparability as coders would moderate their combined results or similar outcomes. The coding is structured but without concerns around reliability. Reflexive TA approaches are fully influenced by qualitative paradigm assumptions. Big 'Q' approach, values and philosophy. Not concerned with structured coding. Achieved by: 1) Start with thorough familiarisation with the data, read through several times and begin process of intense immersion. Then start with coding which is understood as something that is subjective and organic coding. Undertaken by one coder, rather than a consensus of coders and researchers, Reflexive analysis can be achieved with one coder as this retains subjectivity. So themes are the last thing to develop and represent considerable analytic work and themes are refined and developed.
• Coding is the next step in TA.
• Themes are also subjective. See fully realised themes (28:57). Themes are understood as actively created by the researcher. Theme generation occurs at the intersection of the data and the researcher's interpretation, frameworks, prior training, skill and assumptions etc) - as oppose 'buried treasure' that the researcher simply finds and unearths). The pragmatic and active research approach.
• Themes are not something that are IN the data, but rather something that are created from it, using everything we bring to the research process.

Thematic Analysis Lecture - Part 2 https://youtu.be/DzMgUGPl5S0

Notes:

• There's a long-standing association between TA and phenomenology  - why? (phenomenology is an approach that concentrates on the study of consciousness and the objects of direct experience)
• TA can be a (critical) realist (a series of philosophical positions on a range of matters including ontology, causation, structure, persons, and forms of explanation), contextualist (a doctrine that emphasizes the importance of the context of inquiry in a particular question.) to constructionist (that knowledge can only exist within the human mind and does not have to match any real world reality).
• TA is not built into methods
• The researcher needs to articulate how the are using their assumptions that inform their use of TA. Note! Listen to the foundations of qualitative research 1 & 2 in Clarke Resource.
• The benefit is that this encourages and invites theoretical sensitivity because you need to think about the assumptions you are making when you do your analysis - rather than using an 'off the shelf' method.
• IMPORTANT: when writing up theoretical analysis you need to be able to articulate assumptions 
• TA is theoretically flexible  not  atheoretical or inherently realist
• What is story completion? 
• TA works with small data sets e.g case-studies can be either homogeneous or heterogeneous (recommended if a student project and limited on time then a homogeneous sample to facilitate the identification of themes is pragmatic. 
• Questions that are amenable to TA - qualitative researchers ask questions about  

1. Experiences
2. Sense making
3. Practices and behaviours
4. Social processes and factors that influence certain phenomena
5. Cultural rules and norms. 

Then moving into a more critical gear

      6. Representations and constructions

Complex TA

• Tells a story
• Locates data and participants within the wider social, cultural, historical, political and ideological concepts
• Interprets
• Theoretical/conceptual analysis (including theory relationships)
• IMPORTANT: Reflexivity (reflector) is a defining characteristic and crucial to successful conduct of TA.
• Consciously strive to reflect on the assumptions we are making
• Actively make choices
• We need to avoid unacknowledged assumptions
• Reflexivity is difficult and we need to strive and make space in research process not  asking for complete insight
• IMPORTANT: Avoid unacknowledged assumptions; use TA knowingly and reflexively. 
• Themes don't emerge - the researcher is not passive! Themes are generated, developed, created and constructed.
• IMPORTANT: When using TA understand your purpose
• Reflexive TA is creative. You're not discovering things in the world, but your positions and assumptions are wrapped up in knowledge you've created.

Thematic Analysis Lecture - Part 4 https://youtu.be/6uGDc9CQqLU

Common problems: (avoiding)

• Research not addressing research questions that's articulated
• Research questions may evolve and the researcher hasn't revisited the question and adjusted it
• To make sure a good fit. Checking the research question is good for when doing TA
• Are the themes that I'm creating related to the question that I'm asking?
• Will they help me address the question that I'm asking?
• Do they have relevance for some other question that I haven't developed yet?
• Return to questions when analysing data.
• Unconvincing or underdeveloped analysis aka 'analytic foreclosure' e.g lots and lots of themes, which could have been codes as they're tiny. Or too few themes without central focus, or too much overlap between themes. If there is overlap and consider if the right was to organise things.
• Make a choice about how to chop up data
• Themes that are unrelated you are trying to tell a coherent stories
• Themes being vague
• Write theme definitions e.g 100 - 200 words on the theme to make sure you nail down the essence. 
• Analysis is thin. e.g a few sentences of analytic narrative then quite a few extracts illustrating it. The problem is when people stop there, rather than spending time to allow a richness to develop. 
• Not much analysis, just responses summarised. RTA goes further
• Data not contextualised or situated/located in what the important context of data/themes. Think about how they shape what is being told.
• Mismatch between data claims and data extracts. Claims clearly illustrate points. Be thorough and explicit.
• Too many data extracts presented.
• Really need to build up the richness of the analytic narrative. Enough data extracts to convince and illustrate themes that cut across data sets/participants
• At least as much narrative and there are data extracts.
• Paraphrasing data is not analysis
• What you need to unpick is what's interesting and important in data and how this is important in your research question.
• When there seem to be two potential readings of the data and they aren't considered.
• Or when there's contradiction in the data and these aren't highlighted.
• A rich and complex analysis will highlight these contradictions and try to make sense of them. 
• Arguing with data. Don't judge what your participants say or think they are wrong or limited. Come from a position of empathy.
• Analysis and theoretical frameworks are contradictory. Analysis doesn't fit e.g realist, constructionalist etc and then present it as a realist descriptive analysis
• What you say your framework is and how you engage with your data don't contradict.

Good practice

• Data extracts and analysis narrative need to balance 
• Possible insights that are novel, not just a summary
• Good fit between data and analytical claims to exemplify points; that the reader can see what you see
• Each theme has a clear, central organising concept and is distinctive and no overlap
• A central idea that unites the observations that are presented. 
• An appropriate amount of themes. Not too few and not huge or to many
• Not under-cooked
• Enough space and word count to discuss analytic narrative
• Themes tell a story, they are not isolated
• Consistency in theoretical position
• Convey analysis was systematic and thorough
• Able to explain; important to research question
• Move beyond descriptions and are there theoretical concepts and assumptions that underpin
• Data located in social context. Which social ideas are relevant.
• Use data to make a point - (hair-loss story)