28 Sep 2011
I’m not sure which of the categories this infographic on open data falls into, but I find it pretty exciting anyway. It shows the rise of APIs and how data are increasingly open. It seems like APIs are all over the place in the web development community, but less so in health statistics. Although, from the comments, John M. posts places to find free government data including some health data:
1) CDC’s National Center for Health Statistics, http://www.cdc.gov/nchs/
2) NHANES (National and Health and Nutrition Examination Survey) http://www.cdc.gov/nchs/nhanes.htm
3) National Health Interview Survey: http://www.cdc.gov/nchs/nhis.htm
4) World Health Organization: www.who.gov
5) US Census Bureau: www.uscensus.gov
6) Emory maintains a repository of links related to stats/biostat including online databases
http://www.sph.emory.edu/cms/departments_centers/bios/resources.html#govlist
28 Sep 2011
Stanford is offering a free online course and more than 100,000 students have registered. This got the blogosphere talking about the future of universities. Matt Yglesias thinks that “colleges are the next newspaper and are destined for some very uncomfortable adjustments”. Tyler Cowen reminded us that since 2003 he has been saying that professors are becoming obsolete. His main point is that thanks to the internet, the need for lecturers will greatly diminish. He goes on to predict that
the market was moving towards superstar teachers, who teach hundreds at a time or even thousands online. Today, we have the Khan Academy, a huge increase in online education, electronic textbooks and peer grading systems and highly successful superstar teachers with Michael Sandel and his popular course Justice, serving as example number one.
I think this is particularly true for stat and biostat graduate programs, especially in hard money environments.
A typical Statistics department will admit five to ten PhD students. In most departments we teach probability theory, statistical theory, and applied statistics. Highly paid professors teach these three courses for these five to ten students, which means that the university ends up spending hundreds of thousands of dollars on them. Where does this money come from? From those that teach hundreds at a time. The stat 101 courses are full of tuition paying students. These students are subsidizing the teaching of our graduate courses. In hard money institutions, they are also subsidizing some of the research conducted by the professors that teach the small graduate courses. Note that 75% of their salaries are covered by the University, yet they are expected to spend much less than 75% of their time preparing and teaching these relatively tiny classes. The leftover time they spend on research for which they have no external funding. This isn’t a bad thing as a lot of good theoretical and basic knowledge has been created this way. However, outside pressure to lower tuition costs has University administrators looking for ways to save and graduate education might be a target. “If you want to teach a class, fill it up with 50 students. If you want to do research, get a grant. ” the administrator might say.
Note that, for example, the stat theory class is pretty much the same every year and across universities. So we can pick a couple of superstar stat theory teachers and have them lead an online course for all the stat and biostat graduate students in the world. Then each department hires an energetic instructor, paying him/her 1/4 what they pay a tenured professor, to sit in a room discussing the online lectures with the five to ten PhD students in the program. Currently there are no incentives for the tenured professor to teach well, but the instructor would be rewarded solely by their teaching performance. Not only does this scheme cut costs, but it can also increase revenue as faculty will have more time to write grant proposals, etc..
So, with teaching out of the equation, why even have departments? Well, for now the internet can’t substitute the one-on-one interactions needed during PhD thesis supervision. As long as NIH and NSF are around, research faculty will be around. The apprenticeship system that has worked for centuries will survive the uncomfortable adjustments that are coming. Special topic seminars will also survive as faculty will use them as part of their research agenda. Rotations, similar to those implemented in Biology programs, can serve as match makers between professors and students. But classroom teaching is due for some “uncomfortable adjustments”.
I agree with Tyler Cowen and Matt Yglesias: the number of cushy professors jobs per department will drop dramatically in the future, especially in hard money institutions. So let’s get ready. Maybe Biostat departments should start planning for the future now. Harvard, Seattle, Michigan, Emory, etc.. want to teach stat theory with us?
PS - I suspect this all applies to liberal arts and hard science graduate programs.
28 Sep 2011
I want to start a journal called “P>0.05”. This journal will publish all the negative results in science. These would also be stored in a database. Think of all the great things we could do with this. We could, for example, plot p-value histograms for different disciplines. I bet most would have a flat distribution. We could also do it by specific association. A paper comes out saying chocolate is linked to weaker bones? Check the histogram and keep eating chocolate. Any publishers interested?
26 Sep 2011
“The world is full of texts, more or less interesting; I do not wish to add any more”
This quote is from an article in the Chronicle Review. I highly recommend reading the article, particularly check out the section on the author’s “Uncreative writing” class at UPenn. The article is about how there is a trend in literature toward combining/using other people’s words to create new content.
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The prominent literary critic Marjorie Perloff has recently begun using the term “unoriginal genius” to describe this tendency emerging in literature. Her idea is that, because of changes brought on by technology and the Internet, our notion of the genius—a romantic, isolated figure—is outdated. An updated notion of genius would have to center around one’s mastery of information and its dissemination. Perloff has coined another term, “moving information,” to signify both the act of pushing language around as well as the act of being emotionally moved by that process. She posits that today’s writer resembles more a programmer than a tortured genius, brilliantly conceptualizing, constructing, executing, and maintaining a writing machine.
It is fascinating to see this happening in the world of literature; a similar trend seems to be happening in statistics. A ton of exciting and interesting work is done by people combining known ideas and tools and applying them to new problems. I wonder if we need a new definition of “creative”?
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