The Undercover Economist, Tim Harford, has a good article in today’s Financial Times about the stages in life when different professions are most productive. For example, I did a quick Google/calculation: the average median age of a Nobel Prize winner in physics or chemistry is 55; in the literature and peace prizes, it’s 64. (Sorry, not going to do the full test for statistical difference today). This distinction makes some sense, as the great discoveries in the two scientific subjects are marked by innovation (something that may become replaced by habit with age) and excellence in literature and statesmanship benefits from vast amounts of experience.

But, in keeping with our recent discussions about reform in academia, perhaps the bigger question is whether or not we should be actively targeting funding to match these periods of productivity? A quote from the FT article:

Two of my favourite writers, Malcolm Gladwell and Jonah Lehrer, are worried about this – but from different perspectives. Gladwell, a Galenson fan, worries that our obsession with youthful genius will cause us to reject future late bloomers.

Lehrer has the opposite concern: that funding goes to scientists past their prime. He says the US’s National Institutes of Health (NIH) has been funding ever-older scientists. Thirty years ago, researchers in their early thirties used to receive 10 per cent of NIH grants; by 2006 the figure had fallen to 1 per cent.

From my experience in the UK, I think both groups have good, but different, funding opportunities. Established researchers are well-versed in applying for traditional call-based research grants, whereas young researchers are catered for by a number of fellowship schemes. I haven’t seen much evidence of disciplinary-based bias and to be honest, I think anti-discrimination laws would make it difficult to explicitly exclude a group of talented researchers just because they’ve reached an arbitrary age barrier. Think of Andrew Wiles, who found a proof of Fermat’s Last Theorem but just over the Fields Medal’s age limit of 40.

Ultimately the top performers in these disciplines are so unique that it doesn’t make sense to design generalized development or funding programmes for the rest of us. However we can at least take comfort that our best days may be ahead of us!

Just a quick follow-up to last week’s post on changes in higher education. The New York Times published an article on Friday, highlighting two new books on the future of the American academy and picking up some of the points I discussed last time:

The labor system, for one thing, is clearly unjust. Tenured and tenure-track professors earn most of the money and benefits, but they’re a minority at the top of a pyramid. Nearly two-thirds of all college teachers are non-tenure-track adjuncts like Matt Williams, who told Hacker and Dreifus he had taught a dozen courses at two colleges in the Akron area the previous year, earning the equivalent of about $8.50 an hour by his reckoning. It is foolish that graduate programs are pumping new Ph.D.’s into a world without decent jobs for them.

But the real meat of the article is an overview of some interesting, but slightly terrifying, proposed solutions:

As for the humanities, let professors do research after-hours, on top of much heavier teaching schedules. “In other occupations, when people feel there is something they want to write, they do it on their own time and at their own expense,” the authors declare.

The authors being “Andrew Hacker, a professor emeritus of political science at Queens College, and Claudia C. Dreifus, a journalist (and contributor to the science section of The New York Times)”. You can thank them below.

There’s nothing like an overtly contentious statement to bring in the traffic. And as they go, this is a pretty good one: “Why higher education is like a Ponzi scheme“.

The linked post is actually for a radio program, the content of which was based on this original article by a professor of psychology from the University of Kentucky. In it, she argues that there aren’t enough tenure-track jobs to support the PhD students coming through the system and that students are exploited to prop up the teaching and research of over-stretched professors:

“In short, I think academia shares many of the classic elements of a social trap: It is in most faculty members’ and departments’ best interests to recruit a lot of graduate students. Churning out PhDs is one of the major metrics of departmental ‘success’. Departments need graduate students to teach their classes, and faculty members need them to run their labs. Yet, as in any social trap, when everybody acts in their self-interest, a negative collective outcome ensues.”

Her solution? Not to accept any more PhDs:

“I’m no longer willing to pin my students’ prospects for their futures on an ephemeral job market that shines in the distance like a mirage … I don’t want to be part of the problem any more, and I think I will sleep better knowing that I am no longer contributing to an academic job market that bears an uncomfortable resemblance to a Ponzi scheme on the verge of falling apart.”

I take issue with some of the article being rather loosely written. For example, she states that during the admissions interviews, “I ask them [prospective students] what they want to do with their PhDs. They all reply that they want a tenure-track job at a research university.” (emphasis added). Now this may have something to do with psychology as a discipline, and she does comment that applied jobs in that field are in decline, but anecdotally I would say that a large portion of post-docs, let alone PhDs, are in those positions not to gain an academic post, but to acquire unique skills for an increasingly competitive private sector. To pull a number out of the air, I would say maybe 1 in 5 post-docs goes on to an academic post. And in the UK, this is all part of degree inflation: 1385 full-time doctorates where obtained in 1994/95 and 14165 in 2008/9, a 10.2 times increase! In the same period, the total number of full-time HE degrees obtained (only) rose by a factor of 6.7, from 15601 to 104260 (data from HESA). So in this case, we shouldn’t stress too much about providing everyone with tenured positions.

The corollary to the “all PhDs want academic jobs” thing is that, assuming consistent cohort sizes, it implies we should have the same number of PhDs as post-docs as lecturers as professors, lining up along a smooth career path. Clearly this isn’t the case: academia is inherently hierarchical, always has been and always will be.

But this, I think, is where things get interesting. Let’s make the heroic assumption that PhDs (and post-docs) are cheap labour. Is this such a bad thing, provided that these individuals enter into the arrangement with open eyes? This site is, after all, called academic productivity: if PhDs and post-docs can do teaching and research more cost-effectively than professors and tenured staff, wouldn’t that be better from a societal perspective?

For those of us in the UK, this may not be a hypothetical question. There is currently an “Independent Review of Higher Education Funding and Student Finance” underway, led by Lord Browne (ex-chairman of BP). It has a pretty broad remit to consider how higher education should be funded while ensuring access for talented individuals and one idea I’ve heard suggested is that professors should do less teaching and instead leave it to post-docs or dedicated teaching fellows. From an economic perspective, the appeal is obvious: post-docs earn a fraction of the salary of professors, who of course can better use their time securing grants etc. Indeed, this already happens to some extent with tutorials and labs, but why not lectures as well? Why should a world famous professor be teaching first year calculus?

Well there are a couple reasons. First, many professors enjoy it. Teaching enables them to keep in touch with students and the contact is not one way: students can often ask off-kilter questions that inspire new ideas for research. Second, for new lecturers, teaching is an essential part of professional development. It provides a valuable opportunity to practice presentational skills and, through the planning and preparation of course materials and activities, it can help lecturers to solidify their existing specialist knowledge while also learning new material.

Another rebuttal is that students will demand to be taught by professors. I can’t say the extent to which is true but clearly, higher education is increasingly being seen as a paid-for service with the accompanying “client” expectations. If I go to university X for course Y, it may be because of their Nobel prize winning professor and I may be very cross indeed if that person never sets foot in a classroom. To some extent, I think this is an issue of managing expectations. Universities should offer prospective students courses taught to the highest standards, supported by quality assurance mechanisms like training for teachers and regular feedback from students. But this does not mean that the professors should be doing the teaching per se. There’s no reason to think that a well-trained teaching fellow couldn’t develop and deliver a course as effectively (if not more) than a tenured professor who may excel at research but not teaching.

All of this is to say that calling academia a Ponzi scheme is (obviously) a bit sensational. However, academia is certainly a hierarchical institution and its functioning does depend on the cost-effective labour of PhDs and post-docs. Indeed under current budget constraints in the UK, this labour will become increasingly important and may substantially redefine the delivery of undergraduate higher education. It doesn’t mean that potential PhDs should be discouraged from entering the system but certainly there needs to be upfront clarity about their true role and career prospects.

The last week or two has seen some serious winter weather here in the UK. Schools have been cancelled, transport delayed, idiots arrested for driving on frozen canals, and all those other things you would typically associate with places like Canada, not the balmy UK. (No, on second thought, I take back that thing about the canals. We don’t do that in Canada, unless you count this).

Anyway, the weather’s meant that a lot of us have started 2010 by working from home. Timely as always, the Guardian published a small article with some helpful tips for those new to home working. As someone who has worked from home for the past two years, I’d say the basic advice is pretty sound and worth repeating here:

1. Have a work space. Prepare a clear work space so that you can mentally, if not physically, separate home and work life. If you have a spare room to use as a home office, great; if not, clear off the kitchen table and do your best.
2. Don’t forget to take regular breaks. It can sometimes feel like you need to prove constantly that you are at your desk working, but remember: it’s very rare that an entire workday at the office would be spent staring at your computer. So listen to the radio, go for a run, do whatever you like: it’s okay to take a break now and then.
3. Be clear about what you’re working on. This is mainly a due diligence tip for those who might not normally work from home and need to demonstrate that they haven’t been wasting their time. But it’s good general advice too. Even if you are a seasoned home worker, it’s always useful to clearly set out what you hope to accomplish during a day. This is especially important for long stretches of home work when it’s all too easy to procrastinate from day to day.

One of the interviewees in the article suggests that people “stick to the work pattern they adopt at the office” and this is a good take-away message. Home work really is like working from the office, with some pros and some cons. In the past, I thought there might be more pros, like listening to music or doing a bit of laundry, but most of the time that doesn’t work. You need to have enough discipline to avoid those distractions and focus on the job at hand. This can be difficult at first but ultimately, it’s worthwhile. Once you get used to the monastic life, you may actually find that you get more done at home as there is very little outside distraction (compared with an open-plan office).

Anyone else worked from home recently? What tips do you have?

When I started my DPhil, I set myself assignments in order to cover the lit review in easy bite-sized chunks. This worked pretty well but the collated material was scattered across different Word documents, which meant that I couldn’t look at everything at one glance or search the content.

However at about the same time, I also started free-writing to generate ideas. If you’re not familiar with the technique, it’s simply writing for a fixed amount of time without stopping. You don’t delete anything on the fly and just go, writing down anything that comes to mind until the timer sounds or your wrists cramp up, whichever comes first.

Instead of putting this writing into Word documents though, I decided to set up a local installation of the blogging engine Movable Type. In retrospect, I think this was probably just an excuse to play with blogging software but it turned out to be a good decision. I could add content from any computer with access to the server, the basic input form meant that I focused on content not style, and of course, I had all the blogging bells-and-whistles attached. Comments could be added at a later date, the information sorted and searched, shared with my supervisor and so on.

I’m not sure why I didn’t think of it at the time but I would now recommend combining the two ideas: use a blog to write your lit review. (I say “write”, but I really mean “draft”. The structure of blog writing is quite different from academic writing and besides, you’ll want to tidy up references, tables, etc. for the final version. But a blog post is still big enough to cover the bulk of the material and help you organize your thoughts.)

The final stumbling block of course is how to get the content out of the blogging engine and into a presentable format. Fortunately, there’s a script called WPTEX that will convert your WordPress blog into a LaTeX document. I found this software about six months ago and it does what it says on the tin: give it some basic details and it will parse all of your posts, tidying up the code and creating LaTeX source files which you can then compile as a standalone PDF book or for inclusion as part of your thesis.

The script’s not perfect and I can think of several improvements, particularly in the way that it converts URLs for paper presentation. But if you’re starting a PhD, I’d recommend giving it a go. A blog-sized lit review is a great way to manage this difficult task and of course, if you make the blog public, you can engage with the wider community in your field, getting feedback and maybe even making a bit of a name for yourself. Happy blogging!

One of the themes we’ve been discussing here is the idea that prestige and attention are the main currencies of academia. So it only makes sense that you want your online presence to be an accessible and positive reflection of your work and, at the very least, you want to be distinguishable from all of the other John Smiths in the world.

MIT has recently put together a tool called Personas which attempts to figure out this question of online identity. I say attempts because to be honest, it’s a bit hit and miss. The design looks pretty good but the results seem to change each time you run it, you can’t review the underlying data and it doesn’t even have a roll-over to quantify each chunk of your profile (e.g. percent of total, source documents etc).

[click for bigger]

It’s a noble effort though and it got me thinking that there are two sides to the question of online identity.

• User perspective: It’s a pain having a different user name and password for every website so initiatives like OpenID and Shibboleth should make things easier by providing common log-on standards. Similarly APML (Attention Profile Markup Language) is an emerging technology for customizing content based on your interests and habits. Both technologies are valuable for improving your online experience.
• Search perspective: The second issue is being able to search for someone and know that the results pertain to a specific person. The user perspective technologies discussed above can help in this regard, as they establish a common link between all of your online activities. And although I’ve misplaced the link somewhere, I’ve heard of some researchers using generic online data to reverse-engineer a unique identify (e.g. a Joe from California on Facebook here, a Joe who works at UCLA in biology there, etc.).

Clearly there are a lot of privacy issues involved as marketers (and governments) would love to have this sort of detailed record of who went where doing what. But let’s come back to the question of academic online identity. For a user perspective, I think we’re getting there. I can’t speak for everyone obviously but at Imperial, my single sign-on gives access to my publications database, the online journals, administrative data and many other facilities both on the local network and the wider web.

From a search perspective, there’s a way to go before we can amalgamate our various online activities into a consistent verifiable public identity. Yet the academic environment is the perfect place to start building and testing these identity systems. There’s a wealth of metadata available in journals (citations, institutions etc) and one could establish fairly well-defined problem boundaries for example by using the .edu, .ac.uk or journal publisher domains. Google Scholar probably already does this to some extent but when searching for an author, it doesn’t suggest different unique authors. Instead I would love to have one portal, accessed by a single identity which is verified by some official higher education authority, that could crawl the web and aggregate publications, blogs, newspaper articles, conference appearances, etc and combine this with social meta-data from citations or other sources (e.g. LinkedIn). Users could create public profiles and the private data could be useful for determining discipline rankings and influence (e.g. the IDEAS ranking of economists) and so on.

There’s a lot going on in this area and I’ve probably only skimmed the surface. But I wanted to raise the issue and see if anyone had any thoughts about how online identity issues for academics could be handled. At the very least, have a play with the Personas thingy and see if you, like me, are 5% illegal.

If you’re not already aware of the excellent TED conferences and talks, I would strongly recommend going to their site and having a poke around. There are some fascinating people and ideas featured but be prepared to spend a bit of time: most of the content consists of 18 minute video talks.

One of the best ones I’ve seen recently is this talk by Elizabeth Gilbert, the author of a “journey of self-discovery” type novel which is being made into a movie with Julia Roberts. To be honest, from that description, I probably would have skipped it but I’m glad I did watch it: it’s thought-provoking and in places very funny.

She discusses the idea of “genius” and how the pre-Enlightenment concept of attributing outstanding works of art to divine inspiration/intervention (literally, “having” a genius) contrasts with the modern notion that such works come from within the individual (“being” a genius). “Having” a genius helps the artist maintain distance between themselves and their work, deflecting blame from the self when things don’t go so well and keeping one modest when work is well-received. As she says, it helps avoid the depressive thoughts that make one want “to start drinking gin at 9 o’clock in the morning” and generally helps promote creativity.

What do you think? Sounds pretty reasonable to me and a useful construct for digging one’s self out of rut.

A few years ago I visited the Huntington Library in Los Angeles. We spent most of our time poking around the beautiful gardens, enjoying the Californian sun. But the Library collection is pretty remarkable too and it holds copies of the Gutenberg bible, Audubon’s bird drawings, early Shakespeare editions and – a definite highlight – Benjamin Franklin’s autobiography.

I’m not sure why I suddenly remembered this now, almost four years later, but when he wasn’t experimenting with electricity and founding countries, Franklin was also a bit of a productivity guru. Check out this extract from Chapter 8 of his autobiography (click for bigger):

He was pretty keen on everything having its own allocated time, supporting what he called the virtue of Order. He never seemed to be quite satisfied with the progress he made (bit hard on himself really) but the interesting thing, I think, is that you can see him actively reflecting on his progress and acknowledging its benefit. Check out these extracts:

I enter’d upon the Execution of this Plan for Self Examination, and continu’d it with occasional Intermissions for some time. I was surpris’d to find myself so much fuller of Faults than I had imagined, but I had the Satisfaction of seeing them diminish.

And later:

In truth, I found myself incorrigible with respect to Order; and now I am grown old, and my memory bad, I feel very sensibly the want of it. But, on the whole, tho’ I never arrived at the perfection I had been so ambitious of obtaining, but fell far short of it, yet I was, by the endeavour, a better and a happier man than I otherwise should have been if I had not attempted it

So there you go: Benjamin Franklin, the grandfather of personal productivity c. 1791. As if he hadn’t done enough already!

The summer conference season is slowly drawing to a close and we can all put our feet up, right? After all, the papers are done and presented and it’s a least a couple of months before organizers want your abstracts for next year. But before you kick back and relax, it’s worth pausing for a second to reflect on how things have gone and what you might want to do differently next year.

I gave a couple of talks this summer and while they went well, I wasn’t happy with the behind-the-scenes workflow. Some problems:

• Creating figures in one piece of software, exporting to the right image format, inserting them into PowerPoint, discovering that you made a mistake, re-doing the figures…
• Trying to shoe-horn a nice story about the results into 10 tight slides
• PowerPoint, PowerPoint, PowerPoint – having to use someone else’s PowerPoint template, ugly text and math, big files, etc.

A lot of these issues can be fixed using tips here on the site. Outlining your talks for example helps get away from the staccato style of PowerPoint and as commenters have pointed out here, there are lots of ways to mix slides and narrative in one source file. But I want to go step further and show how you can combine narrative, slides and content (i.e. figure creation) in one file.

To achieve this goal, we need use the concept of literate programming, yet another one of Donald Knuth‘s contributions to the world. LP “represents a move away from writing programs in the manner and order imposed by the computer, and instead enables programmers to develop programs in the order demanded by the logic and flow of their thoughts”.

So in the case of a technical presentation, the idea is that we:

• write our talk in a loose conversational style, just as we hope to deliver it
• whenever we think we need to illustrate a point, we create the necessary slide
• wherever we need a figure for the slide, we insert code to perform the analysis and create the figure

Sounds like voodoo? Nope it’s straight-forward thanks to LaTeX, beamer and R/Sweave. Here’s the basic workflow (after you’ve installed all the software).

1. Create a source file with a .Rnw extension. This should contain only the preamble and body of a LaTeX document. Here’s a minimal working example to download: myfile.Rnw.
2. Write your talk in the body of your Rnw file. Wherever you want to insert a slide, use the \begin{frame}{Title}{Sub title} \end{frame} environment as described by the beamer documentation.
3. Wherever you want to perform some R analysis, add an R code block. While Sweave lets you format these blocks in multiple ways, here are two useful templates.

   A basic code block to perform calculations. The code will not show up in the final document.
   
<<echo=FALSE>>=
x <- 1:100
y <- x + rnorm(100,0,1)
@


   A basic code block to show a plot, again without displaying the underlying code.
   
<<echo=FALSE,fig=TRUE>>=
plot(x,y)
@


4. Compile your Rnw file using R. Assuming you’ve started R from the same directory as your Rnw file, the syntax is:
   
Sweave("myfile.Rnw")

   You can change the working directory with setwd("path").

   This will create all the necessary figures and generate a tex file called myfile.tex.

5. Process one of the two following examples with LaTeX to generate either your slides or the lecture notes (e.g. pdflatex slides).

   Slides (slides.tex)
   
\documentclass[ignorenonframetext]{beamer}
\input{myfile}


   Notes (article.tex)
   
\documentclass{article}
\usepackage{beamerarticle}
\input{myfile}


That’s it. I’ve used this process to prepare my presentation for an upcoming event and it’s much easier than the old PowerPoint/R shuffle. The resulting PDF is fully portable as all you need is a reader of some sort. Load it up, press CTRL+L to get full screen mode and away you go!

Edit:
As a private emailer pointed out, there are a few other beamer tricks that might be worth mentioning:

• With a bit of practice, you can style slides however you want. But there are also some presets: e.g. in the slides.tex file above, insert the package declaration \usepackage{beamerthemesplit} to get a blue format with a title of contents/breadcrumb trail.
• The fragile option and \footnotesize commands can be useful for formatting verbatim text output. For example:
  
\begin{frame}[fragile]{Results of linear regression}
\footnotesize
<>=
summary(lm)
@
\end{frame}


Thanks to Patrick from Berlin for those tips.

In a previous episode, I suggested that productivity is really just an efficiency measure. Since the working currency for academics is arguably prestige, productive researchers are those that can acquire the most prestige for the least effort and this can be formally written as:

where each task t is assigned a prestige benefit (p_t per activity × n activities) and an effort cost (attention units per hour a_t × h_t number of hours).

The comments on the original post suggested that there was a lot of enthusiasm for implementing and testing the theory and so I’ve spent the past month gathering data and preparing for a bit of an empirical assessment. The results are a work-in-progress but I hope to keep the conversation going and get your feedback. Here then is a step-by-step guide to how I’ve analysed my productivity over the last month using the general model.

1. Data collection

   I started by logging all of my work activities into a comma-separated file with three columns: the date, a description of the task, and the amount of time spent on that task. At first this was a pain but after a while, I got in the habit of opening the log file each morning and adding the data. I didn’t worry too much about data normalization at the time and recorded information at 15 minute intervals.

2. Data preparation

   Once the data was collected, I needed to tidy it up a bit. A quick inspection of my data file showed that I had used 83 distinct activity types but this was impractical for Step 3 below. Therefore I reduced these activities to 16 categories at first, then 8, and finally 6 categories. These included:

   • Administrata (email, filing expenses, preparing for conferences, sorting out IT problems etc.)
   • Internal meetings (with students, supervisors, project members)
   • Internal writing (conference summaries, minutes, project reports etc)
   • External writing (conference and journal papers, preparing external presentations)
   • Networking (participating at conferences and external events)
   • Research (reading, programming, data analysis)

   Non-work activities and work-related travel were excluded from the analysis.

3. Calculate productivity costs and benefits

   The next step was to calculate i) the amount of prestige gained (p_t) and ii) effort require to perform (a_t) each activity. In other words, I needed to develop quantitative measures that could distinguish between those activities that take a lot of hard work and yield big rewards and those trivial tasks that need to be done on a daily basis.

   To do this, I used the analytic hierarchy process. First, I coded an R function that performs an AHP analysis for a set of input factors (the code’s at the end of this post). AHP is usually done as a “hierarchy”, i.e. comparing options against different criteria and then successively aggregating the results into an overall score. However I did two separate analyses as a way of developing normalized scores for the prestige benefit and attention cost of each activity.

   For example, let’s consider the question of prestige. Each of the 6 categories defined above has different prestige measures that could be used such as citations for a journal publication. However comparing or aggregating these “native” measures for different activities is difficult and contentious; AHP instead rephrases the question and lets you work out in a rough sense what activities are the most prestigious.

   The function I wrote takes the set of categories as input and then asks you to perform pairwise comparisons. Categories are compared on a reciprocal 9-point scale where 1 means options A and B equally preferred and 9 means A is extremely preferred to B (and if A vs. B = 9, then B vs. A = 1/9). As I noted above, I had to reduce the number of categories because this routine requires n*(n-1)/2 comparisons. So 83 categories would mean 3403 comparisons and 6 requires only 15.

   The result is a series of weights for each category and a consistency ratio. The weights indicate the relative importance of each activity category and the consistency ratio indicates the extent to which the judgments were consistent. In other words, if you say that apples are tastier than pears and pears tastier than oranges, but oranges tastier than apples, then your results are inconsistent and the weights can’t be trusted. Ideally the consistency ratio should be less than about 10%. In my first attempt at this, I had a consistency ratio of 0.15 which is why I further reduced the number of categories to 6.

   The table below shows the results of the analyses. I’ve used the prestige weights directly from the AHP analysis; that is, the weights add up to one but don’t reflect any real units. However I’ve normalised the effort weights so that the largest weight represents one hour of maximum concentration. By taking the ratio of prestige to effort, we can work out which activity is most productive on a per-hour basis. Perhaps not surprisingly, networking is twice as productive as writing a paper or attending internal meetings.

   

   However there is some path dependence hidden in here: research may have a very low productivity ratio but clearly the more productive activities must be supported by good quality research results. One can’t happen without the other. This also explains why it’s easier for professors to accumulate prestige: the hard work is often done by grad students and researchers while the authorship and networking opportunities come more readily once your name is established.

4. Calculating the productivity index

   These weights can now be used to calculate the productivity of each day. Using the equation above, I’ve assumed that n_t is 1 for each entry in the database and then calculated the total productivity index for the day. The figure below shows how the productivity scores varies over time, with a three day moving average. Interestingly the low point during late June and early July coincided with a conference when I was doing a lot of traveling (the spike on 29 June represent the day I was presented a paper.)

   

   The data can also be used to examine work patterns. The first figure shows the average amount of time worked on each day of the week, not including meals, tea breaks etc. Out of interest, I typically commute to work on Fridays (and during this month, Wednesdays). This is nearly 3 hours a day on trains which, unless I’m reading work material, is “wasted” time (I do get through a lot of novels).

   

   The second figure shows how this corresponds to productivity. Again because I only have a couple days per week in the office, these tend to be my “necessary evil” days. Hence Wednesdays and Fridays score lower because I’m often busy with student supervisions and other administrative matters. And clearly, taking the weekends off helps to make Mondays more productive.

   

There are other analyses that I could perform with the data, for example breaking up productivity by task, so please feel free to add your suggestions to the comments. But I wanted to end by flagging up a couple issues that struck me while doing this:

• Simply recording your productivity increases productivity; it’s a question of reflective learning and feedback. Several times I caught myself writing “Surfed web” in the log file and then duly chastened, spent the next few hours knuckling down to do some hard work.
• The key to all of this is data collection so you need to find a system that works for you. The flat text file I’ve used is not a bad way of doing it but the excellent Flowing Data blog suggests that a person might use a private Twitter feed to record such information. Could be an interesting experiment.
• When performing the AHP analysis, it’s extremely difficult to evaluate certain activities. What is the prestige value of networking for example? If it contributes immediately to a grant proposal (e.g. in a funding workshop), then that obviously has a benefit. But if it’s simply meeting people, it may be a long time before any measurable prestige comes out of it. (Which of course is not to say that you should only meet people when you get something immediate out of it!)
• Work-related travel is a big drain on productivity, but a necessary evil. Although it’s obvious, using this time for reading is by far the best use of your time. All those research activities with low productivity ratios have to happen at some point and sometimes the office is too distracting.

I’m going to keep recording my productivity data and perhaps once I’ve got more data, I’ll be able to tease out some larger trends. But for the moment, feel free to try out the method and add your comments below.

The code
Here’s the R code I’ve used to perform the AHP analysis (AHP.r). It can be called using something like:

ahp <- AHP(c("Apples","Oranges","Pears"))

It will ask you compare all of the categories: answer using 1,3,5,7,9 or 1/3,1/5,1/7,1/9. Then to get the consistency ratio, type ahp$cr and to get the weights, ahp$weight