1/ Themes don't emerge but themes can be emergent?! What? Eh? A quick thread on the differences between emerge(d) & emergent. @ginnybraun & I bang on about themes not emerging. We are critical of the phrase "3 themes emerged..." etc for 2 reasons. First, it can imply themes are
2/ ontologically real things that exist in data independent of a researcher's engagement with the data. If themes are real the researcher's role becomes one of extraction or discovery. In our reflexive TA approach themes aren't real! They're not in data fully formed. Instead,
3/ themes are generated by the researcher through their interpretative engagement with data - created from codes & through coding. Second, "the themes emerged" can imply the researcher is passive in the theme development process when they are anything but. At the same time the
4/ emergent has a long history in qualitative research - I associate it with grounded theory, the first analytic approach I learnt about waaay back in the 90s - meaning specifically inductive, grounded in data. So in IPA lower level themes are called emergent themes. Emergent =
5/ inductive it doesn't mean themes exist in data fully formed or the researcher is passive in theme development. So themes in some approaches are described as emergent & @ginnybraun & I can say themes don't emerge. These aren't contradictory. This is a small example of why
6/ understanding the history of certain terms and what researchers mean by them is really important for navigating the messy swamp of qualitative research.
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1/ I've been having a lot of discussions about getting started with familiarisation & coding in reflexive thematic analysis recently. So here's a 🧵 with the highlights. Familiarisation is about getting to know the contents of your data - if you interacted with participants to...
2/ generate them & you transcribed them this gives you a head start. It's also about starting to engage with your data analytically - what's going on here? & reflecting on your emotional responses to the data/participants - how do you feel when you read them/certain participants?
3/ I encourage students to write familiarisation notes - on individual data items & overall, & after each round of familiarisation - for themselves, not to share with anyone. And they're not limited to writing notes, students have audio recorded notes, written poetry, doodled...
1/ The language & concepts of nonpositivist/Big Q qualitative research - what do we take from quantitative/positivist research, what do we rework, what do we leave behind?
A 🧵 of musings starting with generalisability - a term often associated solely with quant.
2/ I have often equated generalisability with statistical generalisability & argued that it's a concept that doesn't hold relevance for qual. But this paper by @BrettSmithProf convinced me that generalisability is a broader concept & can be reworked: tandfonline.com/doi/abs/10.108…
3/ I've started to avoid data *collection* as it implies things exist as data before researchers arrive on the scene & we are relatively (& ideally in small q qually) passive/unobtrusive in the process - keeping our "influence" to a minimum. For me data *generation* acknowledges
1/ @ginnybraun & I made it to the Build-a-Bear Workshop in Bristol! Here's why we think it works as a great 'metaphor' for thematic analysis & for challenging common misconceptions of TA. We mentioned this in our webinar yesterday eve which you can watch on YouTube (link below).
2/ People often assume that TA can only be used for basic descriptive/summative analyses, that it's atheoretical like qualitative content analysis often claims to be or only realist/essentialist, lacking the sophistication of grounded theory or IPA. Students often contact us...
3/ after being told that TA isn't sophisticated enough for a Masters or Doctoral project... These are all problematic assumptions. TA is different from approaches like grounded theory, discourse analysis & IPA because it is closer to a method than a methodology - a theoretically
1/ I didn't link to *that* paper because of prudishness - I have led/taught on a module on sexuality since the early 2000s & give lectures on explicit topics including masturbation... I didn't link to it for several reasons including not want to encourage nudge nudge "humour"...
2/ which communicates discomfort with the topic... there is a role for humour in talking about sex but not here, not now. The topic is distressing in various ways and doesn't need air time on Twitter. The primary concerns are ethics, why this research was allowed to go ahead...
3/ why the supervisor apparently sanctioned this, why UoM appear to be funding this research, why this research has UoM ethical approval or doesn't, how this paper got through peer review, why the QR editors apparently didn't ask any of these questions & published the paper...
1/ A thread on piloting data generation "tools" in qually research & different ways of thinking about piloting in small q & Big Q qually. First up, what's small q and Big Q qually? Small q is where qually is defined by collecting & analysing qual data but the underpinning values
2/ default to disciplinary norms - typically positivism. It seems to be most often practiced unknowingly - the assumption is that this is what constitutes good practice & there isn't an awareness of other possibilities for qual. Big Q qual involves both qual techniques & the
3/ distinct values & traditions that have developed around qual methods (e.g. interpretavism, phenomenology, constructivism, narrative to name a few). Big Q is typically a rejection of positivism. And this shapes research practice in various ways including around piloting.
1/ A thread on ensuring/assessing quality in qualitative research & whether checklists/guidelines that aspire to be universally applicable have a role to play.
The first problem with universal guidelines is that there isn't a widely agreed on definition of what qually research is
2/ A - over simplified - definition of qually research (well any research) is that it involves tools & techniques for collecting & analysing data & research values (paradigms, 'ologies) that tell you what the data represent, what you can access through them: contextually situated
3/ sense-making, a universal truth of experience, discourses, narratives, social constructions etc. It's very hard to develop a definition that works for all forms of qualitative research. So lots of guidelines/checklists are based on partial definitions but these aren't...