This is an exciting topic, I would like to give some insights I have on the discussion, but legal disclaimer, I'm discovering ethnography (I have read a lot of posts on the platform these last days but that's it) so forgive my possible bad understanding
It's funny because this issue of the participant-observation, with the need of disclosing one's researcher status and what it possibly implies (influencing the discussion) reminds me of one quantum mechanics theory that says that a measurement on an observation bring changes to this observation. I'm working on creating and then analysing surveys (about stress and well being at work) and when designing it, we have to take into account the bias induced by the question formulation for instance, or the periodicity of the campaign, and there are some methodologies to reduce such error inherent in the survey measurements. But it seems that being a part of the observation should reduce this bias and should allow for more precise analysis, because for example you discover the ideas / concepts covered almost on the fly and intuitively it would converge more naturally to something. (And it is more interesting to participate the subject you are studying, it should be more immersive). I would be very happy to learn more about this field
That being said, to join another topic of this discussion, when dealing with open ended questions, we have to find topics and there is also the issue of scaling when we have many answers. We tried to do it automatically via nlp technics (as a first step, before manually sharpening between topics) and with 4000 answers, and given the noise induced by open ended question (it is as if you give a blank page and ask someone to tell you how does he feel today, for example, and when asked, you are left to the participant's imagination, without the ability to readjust anything), we can have some results, and some visualisation of the topics as well (we are using NMF, for those who are familiar with ML, with a lot of cleanup beforehand).
So maybe it is usable in ethnography, as a first step to define code (the big ones).