"More Than Meets The Eye" Animated VR Experience

Phase 4: Reviewing Themes This phase began with revisiting the sub-themes identified and collated against each research question. A focus was given to key points in the research question  e.g In what ways...and...a new film language.....and moving the sub-themes into two new defined groups. By placing semi-transparent circles over each group the sub-themes were visually ' equalised' , which enabled clarity in deciding which one could become the candidate theme. Once decided, the prominent sub-theme became the candidate theme and was placed above the semi-transparent circle, for visual and hierarchical prominence. This phase involves two levels of reviewing and refining your themes. Level one involves reviewing at the level of the coded data extracts. Meaning to read all the coded data extracts for each theme and considering whether they appear to form a coherent pattern. If they do, move onto level two . Which is a similar process, but relates to the entire data se...

Phase 3: Searching for themes. "Phase 3 begins when all data have been initially coded and collated. This phase re-focuses the analysis at the border level of themes, rather than codes, involves sorting the different codes into potential themes, and collating all the relevant coded data extracts within the identified themes. Essentially, you are starting to analyse your codes and consider how different codes may combine to form an overarching theme." Braun, V., Clarke V.  Using thematic analysis in psychology.  Qualitiative research in psychology 2006 Jan 1;3(2):77-101 Using simple visual Powerpoint slides to collate all latent and semantic themes to begin with... Dark slides for latent (hidden meanings) and light slides for semantic (surface meaning)

Although I am not an editor or reviewer I think this checklist provides challenging questions that I  can use to ensure clarity of thought and  argument for my research philosophy, approach and chosen data analysis. Guidelines for reviewers and editors evaluating thematic analysis manuscripts Produced by Victoria Clarke and Virginia Braun (2019) Available: https://cdn.auckland.ac.nz/assets/psych/about/ourresearch/documents/TA%20website%20update%2010.8.17%20review%20checklist.pdf We regularly encounter published TA studies where there are mismatches between various elements of the report and practice. We have developed the following checklist for editors and reviewers, to facilitate the publication of coherent and quality thematic analysis – of all forms. The checklist is split between conceptual and methodological discussion/practice and analytic output. Evaluating the methods and methodology 1. Is the use of TA explained (even if only briefly)? 2. Do t...

Qualitative analysis of interview data: A step-by-step guide for coding/indexing  https://youtu.be/DRL4PF2u9XA Notes: Step 1: Scan transcripts as a whole Make notes about first impressions Re-read carefully, line by line Step 2: Label relevant codes. What to code? It's repeated. It surprises you. The participant states that it's important. You have read about something similar in previously published reports/articles. It reminds you of a theory or concept. For some other reason that you think it's relevant. You can aim for a description of things that are superficial or you can aim for a conceptualisation of underlying patterns. It's your study and your choice of methodology. You are the interpreter and these phenomena are highlighted because you consider them to be important. Make sure to explain and reveal your methodology and the choices you make. Do that under the he...

https://youtu.be/5zFcC10vOVY  - Victoria Clarke Notes: Familiarise yourself with the data. Read and re-read. Do not advance immediately to theme generation. It's not about being an expert data analyst, or an inexperienced data analyst. It's a process of time and reflection that has to unfold Reflexive TA thinks of codes as things - analytic entities. Coding produces little things and then themes are generated from those clustered codes. A code is a label that captures something interesting in the data. Avoid one word and really get into data. Code labels need to work independently from the data - they need to evoke data and be clear, when the data is not present. Go through systematically and go through each data item individually. Then view subsequent data items through the lens of the first. Codes can get bigger and then break up into different codes It's a flexible and organic process and therefore a couple of coding sweeps are recommended. The main disti...

Attride-Stirling, J. (2001). Thematic networks: an analytic tool for qualitative research. Qualitative Research, 1(3), 385–405.  https://doi.org/10.1177/146879410100100307 Braun, V. & Clarke, V. (2006). Using Thematic Analysis in Psychology. Qualitative Research in Psychology, 3(2), 77-101. Coffey, A., & Atkinson, P. (1996). Making sense of qualitative data: complementary research strategies. Sage Publications, Inc. Fereday, J., & Muir-Cochrane, E. (2006). Demonstrating Rigor Using Thematic Analysis: A Hybrid Approach of Inductive and Deductive Coding and Theme Development. International Journal of Qualitative Methods, 5, 1. Guest, G., MacQueen, K.H. & Namey, E. E. (2012) Applied Thematic Analysis. Sage Publications.  Lapadat, J. C. (2010). Thematic Analysis. Encyclopaedia of Case Study Research. http://sk.sagepub.com/reference/download/casestudy/n342.pdf Nowell, L. S., Norris, J. M., White, D. E., & Mou...

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...

Virginia Braun & Victoria Clarke (2006) Using thematic analysis in psychology, Qualitative Research in Psychology, 3:2, 77-101 Process Transcription  The data have been transcribed to an appropriate level of detail, and the transcripts have been checked against the tapes for ‘accuracy’ Codes Each data item has been given equal attention in the coding process. Themes have not been generated from a few vivid examples (an anecdotal approach),but instead the coding process has been thorough, inclusive and comprehensive. All relevant extracts for all each theme have been collated. Themes have been checked against each other and back to the original data set. Themes are internally coherent, consistent, and distinctive. Analysis  Data have been analysed / interpreted, made sense of / rather than just paraphrased or described.  Analysis and data match each other / the extracts illustrate the analytic claims.  Analysis tells a convincing and well-o...

Six phases of thematic analysis (Braun & Clarke, 2006) This should not be viewed as a linear model, where one cannot proceed to the next phase without completing the prior phase (correctly); rather analysis is a recursive process. 1)       Familiarisation with the data : is common to all forms of qualitative analysis – the researcher must immerse themselves in, and become intimately familar with, their data; reading and re-reading the data (and listening to audio-recorded data at least once, if relevant) and noting any initial analytic observations. 2)       Coding : Also a common element of many approaches to qualitative analysis (see Braun & Clarke, 2012a, for thorough comparison), this involves generating pithy labels for important features of the data of relevance to the (broad) research question guiding the analysis. Coding is not simply a method of data reduction, it is also an analytic process, so codes capture bo...