Part of the QualMIP series, introduced here.
Sparse notes today, because we worked with data that's non-public... and I have about 5 minutes to write this blog post before my next meeting.
Today we went through a close reading exercise; we took my data from a prior project and read it -- out loud -- in small increments, with discussion in-between. Each person in the group took a small sample of data from a different respondent, effectively "adopting" them for the duration of the conversation -- with the disclaimer that we were working from a limited dataset from people we didn't know, so our guesses were exactly that: guesses.
In between rounds of reading their words, we talked a bit about what "putting data in conversation" meant. It can mean many things; do you see commonalities between "your" data and "other people's" data? How do you think "your person" (the person who wrote the response you're reading out loud) would reply to notes/thoughts from or about the other data/observations? If you were going back to do a member-check, what would you want to ask -- and how does that help you think about the fieldwork you are doing now, where you still (might) have that opportunity?
In all these responses, we kept on trying to go back to specific phrasings and sections of the data to back up our guesses, working to keep awareness of possible biases we might be bringing to the conversation.
Honestly, right now -- it's hard to sum up in a blog post writing "about" the practice... we're in the middle of doing qualitative analysis/fieldwork, not talking about it. Over the next two weeks, we'll be responding to each other's data (and doing a fair amount of self-care). I'll be modeling half-hour bounded response sprints for each person's data on Thursday, because one of the hardest parts of the semester is learning just how much work goes into qualitative research... how tiny and bounded projects need to be to actually get done.