A reader wrote me this morning asking me to explore the relationship between writing time and data analysis time. That is, how do we conceptualize the need for data analysis and like tasks and engaging in daily or near daily writing rituals. Does data analysis count towards as writing time?
This is a topic that elicits strong feelings from some. I get the sense that when I say that research tasks do not count toward writing time that they feel I am invalidating the importance of that work.
I am not.
What I am saying is that there is something amazing that happens when we find ways of writing every day. I am also saying that from my experience there are many, many scholars who can "hide" in data analysis and use it as a way of procrastinating on their writing, and ultimately avoiding getting their work "rejected."
I am also suggesting that no matter the type of work we do, we can always have an article to write whether or not we have data to work from. I have addressed this before, so I am not going to make that argument now. I can tell you it is one that I usually win when I can explore it with someone live or in a google hangout :).
So, the goals is to write every day and also set aside blocks of time for data analysis and other scholarly work as needed. There should be a symmetry and balance to this; spending too much time on one task, on all of our tasks, squeezes our ability to be productive.
In general, write first, each and every day. Teaching preparation, grading; these things will take as much time as we allot to them; when we write first, we tend to be more efficient in these tasks as we make those more time limited as well.
In a few weeks I am going to do a "cross training week" where I am going to put out some possible sequencing for scholarly work. For now, try this: write for thirty minutes every day. Data analysis and other research tasks in blocks, as they fit, for two to three hours a week. As a bare minimum, this is enough to be pretty productive. Adjust as energy and time permit.
This is a topic that elicits strong feelings from some. I get the sense that when I say that research tasks do not count toward writing time that they feel I am invalidating the importance of that work.
I am not.
What I am saying is that there is something amazing that happens when we find ways of writing every day. I am also saying that from my experience there are many, many scholars who can "hide" in data analysis and use it as a way of procrastinating on their writing, and ultimately avoiding getting their work "rejected."
I am also suggesting that no matter the type of work we do, we can always have an article to write whether or not we have data to work from. I have addressed this before, so I am not going to make that argument now. I can tell you it is one that I usually win when I can explore it with someone live or in a google hangout :).
So, the goals is to write every day and also set aside blocks of time for data analysis and other scholarly work as needed. There should be a symmetry and balance to this; spending too much time on one task, on all of our tasks, squeezes our ability to be productive.
In general, write first, each and every day. Teaching preparation, grading; these things will take as much time as we allot to them; when we write first, we tend to be more efficient in these tasks as we make those more time limited as well.
In a few weeks I am going to do a "cross training week" where I am going to put out some possible sequencing for scholarly work. For now, try this: write for thirty minutes every day. Data analysis and other research tasks in blocks, as they fit, for two to three hours a week. As a bare minimum, this is enough to be pretty productive. Adjust as energy and time permit.
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