A guest ROLSI Blog Entry – from Emily Hofstetter, Loughborough University, UK
Compared with other research students, postgraduates using conversation analysis have to do their work ‘backwards’. Instead of diving into their literature review as their first major hurdle, it often makes sense for CA PhD students to write their literature review last, or nearly last, and use their first year to acquire data and begin analysis. But the more challenging mental hurdle isn’t necessarily getting access to data; it’s wrapping one’s head around a completely new method of research.
As an incoming PhD student, I thought my initial job was to define my research interests and questions. It was only natural; most people have been steeped in the scientific method for decades before getting to a PhD, and so the urge to find a hypothesis, or at least research question, was strong. But unless that research question is ‘How do X interact in Y environment’, it’s almost certain to be misguided. Ultimately, we are at the mercy of our data. It is our blessing and our curse. We cannot predict what people will talk about in any environment, so narrowing down a research question before getting data is truly flawed.
The “perfect question”
Unfortunately, PhD students the world over get drawn into the hunt for ‘The Perfect Question’. For months on end, postgraduate students wander the library stacks and roam the journal archives, seeking through literature for ideas and inspiration, trying desperately to grasp the perfect, ideal PhD question. The perfect question is exactly what you are interested in. It is exactly the right balance between specific and generalizable. It is a question you can build a career on.
The best thing I ever did was simply trust my supervisor and give up the chase. I went out and got some data, and I got on with things. It all became clear. Occasionally I have wistful thoughts about that perfect question, wondering if it’s worth going back into the jungle to get another glimpse of it. But mostly I feel engaged with my work, and with parsing what my data shows.
“Get some data!”
I can only imagine how difficult it must be, as a supervisor, to get one’s student to throw caution (and their enthusiastic research interests) to the wind and simply get some data. Once you’re accustomed to getting data first, your mindset does not match your student’s. You’d be thinking, “Well, get on with data collection! Then we can start,” meanwhile the student is caught in the early PhD angst of, “What am I studying? What will I be as a researcher? How do I even pick? How can I possibly start?” To tell them to hurry up and collect data amounts to saying, “Your interests don’t matter.” While this may be true, it’s bound to cause further angst. The important part is the reason it’s true: we cannot control what is in the data we get. But if you are interested in human interaction, you will be interested in your data.
Most frustrating of all, while an experienced CA researcher may press you to start data collection, there is pressure from all other angles telling you to get a hypothesis in place in order to do valid research. The university, graduate workshops, other PhD students, annual reports, even friends and family – all of these and more can and will press you to give a solid, unqualified definition of your thesis topic. It’s easy to get caught up in the quest for The Question, especially when every other student in your department is busily chasing it down. There is an expectation of control, and it probably comes most strongly from one’s own expectations of the research process. Eventually (most likely once you start to get some data), you will have no choice but to shout to all these pressures, “Sorry, I’m kind of busy looking at real, unpredictable interactions right now. Would you mind bothering me with your preconceived notions of valid research later?”
And with that, you will finally be able to get on with it.