When I first started looking in to student and academic productivity, one of the first bloggers I stumbled upon was Cal Newport.  His blog and books are primarily targeted toward undergraduates, but periodically he gives us insight into his own system.  (Cal graduated from MIT with a PhD in Computer Science and starts as an Assistant Professor at Georgetown in the fall.)

He recently put up a great post that looks at his framework for putting together a coherent research agenda and for supporting that agenda.  My first thought was that I wished I had seen this earlier in my grad student career.  (You know; like before I had graduated.)  My second was that the system could be adopted at ANY point in one’s career, although it might take some time to really get everything into it.

What I like the best about this conceptual system is that it uses a mission statement (which includes the primary area of research) in order to coordinate and direct the work, ensuring that there is a thread of coherency to the work.  My work is a primary example of what happens when you lack that coherency;  I have publications in history of ed, higher ed, charter schools, and looked at a national data set for my dissertation.  I can force them all together if I have to, but it takes some explaining.

The second thing I like about this is that he doesn’t try to suggest that you can go away for months and come back with some type of genius product.  The entire process is iterative and in manageable chunks.  He talks about learning new things in his field (although at a rate of 1 item per week, that must be fairly tightly defined) on a weekly basis, and using that to brainstorm new ideas.  This is something I could easily adopt myself, although my field (possibly due to the presence of numerous think tanks and such) puts out far more than 1 new item per week.

The new ideas need to be timeboxed into testable chunks, and small enough to take under a month, but big enough to be something that could turn into a talk or something on which he can get feedback.  This might be challenging for some social scientists, but could well be possible if you are mining an existing data set.  (All the more reason to ensure that when you take the time to collect data, you go beyond just collecting the minimum.)  Notice that this step isn’t required to be publishable on it’s own; the idea is to use these short projects to create the building blocks of something bigger and to vet the pieces.  I would imagine that some don’t work out and get tossed; if so, limiting the time spent on them is an incredibly important component of long-term success.

Finally the new items are used to support grant applications and papers.  By the time this point is reached, you’ve gotten some feedback on the ideas and have a selection of pieces that can be combined into something bigger.

If I were a full-time academic I would be working to implement this already.  Because I’m not, I have to be a bit more patient about how I implement something like this.  My research agenda is only partially my own.  My day job informs many of  my research questions and, realistically, expects answers to them that are not publishable due to the proprietary data I have access to.

Nonetheless, the approach (if not the speed) is something I am going to try to work in to  my own approach to research.  There is no reason I can’t apply a process like this at any speed, and this approach would be exceptional for a student working on their dissertation.  Yes, they may need to read more than one new thing per week, but the idea of regularly brainstorming small, testable ideas is one that has merit for any researcher.