Academic Productivity

I want to try something a bit different in this post. Here at AP.com, we’ve talked a lot about tools, theory, trends and the general ephemera of academic productivity. But writing as academics, we should probably be trying to take this experience and build it into a cohesive model of productivity. So my goal here is to suggest a general model, one that we might use to understand what we’ve learned from previous posts and hopefully apply to our own work.

My starting point for this post was simple; I wanted to know how my productivity has changed (hopefully improved) since I first started my DPhil. From keeping a research journal, I know that some days are more productive than others and it would very helpful if I could understand when those fits and starts occur, to spot co-occuring events and thereby learn when to say “Forget work, I’m going for a run.”

In other words, I wanted to plot my productivity cycle over time. It might look something like this:

But the obvious problem with this exercise is how to measure productivity. It’s a subject that’s been tackled indirectly on this site before but going through the old posts, I haven’t yet find any attempts at a general theory – and related measures – of productivity. So drawing on the collected wisdom of previous AP.com posts, here’s a rough sketch of such a theory.
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I always wondered how people see the academic world from outside. How do we gauge the interest of the general public on what academics have to say (on average)? One easy way to look at this question is to see the how often people will read an article that has the word ‘academic’ on it.

A proxy on what people read nowadays is digg.com. And the tool to see how often people digg academic posts is now available in Dan Zarella’s blog. Given a keyword, the tool will return data on the average number of links accumulated by stories popular on Digg that mentioned that keyword. This is done with 2007 data.

Well, behold what happens when you enter “academic”:

And compare it to what you get when you type “productivity”:

Why is this important? Well, on average, a single digg increases traffic by 0.10%. So a story that gets 3,000 diggs results in an increase in total traffic to the referring site by 300%.

So, from now on we are a^H^H^H^H^H^H^H^H productivity blog

MINDLESS SIGNIFICANCE TESTING

Decision science news has a post on hypothesis testing that I find relevant.

Some well-made points grow old while no one pays attention to them. One of the most embarrassing for social science is its categorical perception of p-values.

Tender of kindred Web site Andrew Gelman and Hal Stern have an article whose name says it all: The Difference Between “Significant” and “Not Significant” is not Itself Statistically Significant.

Link to The Difference Between Significant and Not Significant is Not Statistically Significant