Thematic Analysis Braun et al PART 3 YOUTUBE VIDEO

This is part three of a four-part lecture entitled Thematic Analysis. The materials were created by Virginia Braun, Victoria Clarke and Nikki Hayfield; the lecture is narrated by Victoria Clarke. The materials were created for postgraduate students at the University of the West of England, Bristol, UK, as such there may be reference to our virtual learning environment Blackboard. Any handouts and the lecture slides are avaliable on www.thematicanalysis.net.

[@MissMurmel]

This 4 part video is SO helpful, I am working on my thesis right now and feel very supported. by this material, it is by far the most useful video material I found. THANK YOU!

[@robinhindlefisher6040]

This a brilliant and incredibly useful set of videos. Thank you very much!

[@rachelbassett-dubsky258]

Super-useful: both incredibly thorough AND clear. Invaluable and am using alongside your excellent 2022 book - 'Thematic Analysis: A Practical Guide'. Thank you!

[@silkeboertien4107]

Thank you so much for sharing this, it really evoked my joy in doing research again! This series is so useful and it triggers you to pause and ask yourself and your data 'what is going on?'. Unfortunately, when I'm stressed about the more practical issues, I ask myself these questions less often and this was a good reminder to take the time I need to reflect.

[@FatFreeRevolution]

So glad I found this. Much easier to listen to it than always be reading it

[@joseroyal877]

Better explained than how it was presented in my MBA class. Thank you.

[@dmmath-tutor5769]

Awesome video. Most informative. Thank you.

[@naftalibendavid]

Nice work!

[@PassportGods]

5:24 notation
23:09 codes suit purpose.

[@lavvy2585]

I think anyone doing Qual analysis knows Braun and Clarke 2006. I love surprising them when I show learners that they are more than 2006.

[@BFICinematheque]

Great lecture, thank you, very useful. But i have a question, about latent and semantic codes, in relation to the data you shared. I wonder whether sometimes, in going for the 'conceptual' themes, we can actually just be drawing on pre-existing assumptions we had already, and the corollary, that sometimes the 'superficial' semantic themes might be hiding in plain sight - or harder to see. It's 'common sense' that superficial is easier to read', while 'latent' is harder.. but I think there's a case for te opposite also being true.. Maybe there's no real distinction between semantic and latenet? or it's not as obvious as we think?

[@chloeeze]

Is there any way of selecting a theme that is an interesting individual theme rather than always identifying patterns and then themes across the data? I was hoping to have the flexibility to do that with my dissertation. I know you can do it with the ideographic element of IPA but can I so something similar with TA please?