11 Dec 2013
People know I have taught a MOOC on Data Analysis, so I frequently get emails about updates on the “state of MOOCs”. It definitely feels like the wild west of education is happening right now. If you make an analogy to air travel, I would say we are about here:
So of course I feel like it is a bit premature for quotes like this:
Two years after a Stanford professor drew 160,000 students from around the globe to a free online course on artificial intelligence, starting what was widely viewed as a revolution in higher education, early results for such large-scale courses are disappointing, forcing a rethinking of how college instruction can best use the Internet.
These headlines are being driven in large part by Sebastian Thrun, the founder of Udacity, which has had some trouble with their business model. One reason is that they seem to have had the most trouble with luring instructors from the top schools to their platform.
But the main reason that gets cited for the "failure" of MOOCs is this experiment performed at San Jose State. I previously pointed out one major flaw with the study design: that the students in the two comparison groups were not comparable.
Here are a few choice quotes from the study:
Poor response rate:
While a major effort was made to increase participation in the survey research within this population, the result was disappointing (response rates of 32% for Survey 1; 34% for Survey 2, and 32% for Survey 3).
Not a representative sample:
The research team compared the survey participants to the entire student population and found significant differences. Most importantly, students who succeeded are over-represented among the survey respondents.
Difficulties with data collection/processing:
While most of the data were provided by the end of the Spring 2013 semester, clarifications, corrections and data transformations had to be made for many weeks thereafter, including resolving accuracy questions that arose once the analysis of the Udacity platform data began
These ideas alone point to an incredibly suspect study that is not the fault of the researchers in question. They were working with the data the best they could, but the study design and data are deeply flawed. The most egregious, of course, is the difference in populations between the students who matriculated and didn't (Tables 1-4 show the dramatic differences in population).
My take home message is that if this study were submitted to a journal it would be seriously questioned on both scientific and statistical grounds. Before we rush to claim that the whole idea of MOOCs are flawed, I think we should wait for more thorough, larger, and well-designed studies are performed.
05 Dec 2013
NYC mayor-elect Bill de Blasio is expected to name William J. Bratton to lead the NYPD. Bratton has been commissioner before (1994-1996) so I was curious to see the crime rates during his tenure, which was within the period that saw an impressive drop (1990-2010). Here is the graph of violent crimes per 100,000 inhabitants for NYC NY state for year 1965-2012 (divided by commissioner). Will Bratton be able to continue the trend? The graph suggests to me that they have hit a “floor” (1960s levels!).
Data is here.
04 Dec 2013
Editor’s note: This is a slightly modified version of a previous post.
Job hunting season is upon us. Openings are already being posted here, here, and here. So you should have your CV, research statement, and web page ready. I highly recommend having a web page. It doesn’t have to be fancy. Here, here, and here are some good ones ranging from simple to a bit over the top. Minimum requirements are a list of publications and a link to a CV. If you have written software, link to that as well.
The earlier you submit the better. Don’t wait for your letters. Keep in mind two things: 1) departments have a limit of how many people they can invite and 2) admissions committee members get tired after reading 200+ CVs.
If you are seeking an academic job your CV should focus on the following: PhD granting institution, advisor (including postdoc advisor if you have one), and papers. Be careful not to drown out these most important features with superflous entries. For papers, include three sections: 1-published, 2-under review, and 3-under preparation. For 2, include the journal names and if possible have tech reports available on your web page. For 3, be ready to give updates during the interview. If you have papers for which you are co-first author be sure to highlight that fact somehow.
So what are the different types of jobs? Before listing the options I should explain the concept of hard versus soft money. Revenue in academia comes from tuition (in public schools the state kicks in some extra $), external funding (e.g. NIH grants), services (e.g. patient care), and philanthropy (endowment). The money that comes from tuition, services, and philanthropy is referred to as hard money. Within an institution, roughly the same amount is available every year and the way its split among departments rarely changes. When it does, it’s because your chair has either lost or won a long hard-fought zero-sum battle. Research money comes from NIH, NSF, DoD, etc.. and one has to write grants to raise funding (which pay part or all of your salary). These days about 10% of grant applications are funded, so it is certainly not guaranteed. Although at the institution level the law of large numbers kicks in, at the individual level it certainly doesn’t. Note that the break down of revenue varies widely from institution to institution. Liberal arts colleges are almost 100% hard money while research institutes are almost 100% soft money.
So to simplify, your salary will come from teaching (tuition) and research (grants). The percentages will vary depending on the department. Here are five types of jobs:
1) Soft money university positions: examples are Hopkins and Harvard Biostat. A typical breakdown is 75% soft/25% hard. To earn the hard money you will have to teach, but not that much. In my dept we teach 48 classroom hours a year (equivalent to one one-semester class). To earn the soft money you have to write, and eventually get, grants. As a statistician you don’t necessarily have to write your own grants, you can partner up with other scientists that need help with their data. And there are many! Salaries are typically higher in these positions. Stress levels are also higher given the uncertainty of funding. I personally like this as it keeps me motivated, focused, and forces me to work on problems important enough to receive NIH funding.
1a) Some schools of medicine have Biostatistics units that are 100% soft money. One does not have to teach, but, unless you have a joint appointment, you won’t have access to grad students. Still these are tenure track jobs. Although at 100% soft what does tenure mean? I should mention at MD Anderson, one only needs to raise 50% of ones salary and the other 50% is earned via service (statistical consulting to the institution). I imagine there are other places like this, as well as institutions that use endowments to provide some hard money.
2) Hard money positions: examples are Berkeley and Stanford Stat. A typical break down is 75% hard/25% soft. You get paid a 9 month salary. If you want to get paid in the summer and pay students, you need a grant. Here you typically teach two classes a semester but many places let you “buy out” of teaching if you can get grants to pay your salary. Some tension exists when chairs decide who teaches the big undergrand courses (lots of grunt work) and who teaches the small seminar classes where you talk about your own work.
2a) Hard money postions: Liberal arts colleges will cover as much as 100% of your salary from tuition. As a result, you are expected to teach much more. Most liberal arts colleges weigh teaching as much (or more) than research during promotion although there is a trend towards weighing research more.
3) Research associate positions: examples are jobs in schools of medicine in departments other than Stat/Biostat. These positions are typically 100% soft and are created because someone at the institution has a grant to pay for you. These are usually not tenure track positons and you rarely have to teach. You also have less independence since you have to work on the grant that funds you.
4) Industry: typically 100% hard. There are plenty of for-profit companies where one can have fruitful research careers. AT & T, Google, IBM, Microsoft, and Genentech are all examples of companies with great research groups. Note that S, the language that R is based on, was born in Bell Labs. And one of the co-creators of R now does his research at Genentech. Salaries are typically higher in industry and cafeteria food can be quite awesome. The drawbacks are no access to students and lack of independence (although not always).
5) Government jobs: The FDA and NIH are examples of agencies that have research positions. The NCI’s Biometric Research Branch is an example. I would classify these as 100% hard. But it is different than other hard money places in that you have to justify your budget every so often. Service, collaborative, and independent research is expected. A drawback is that you don’t have access to students although you can get joint appointments. Hopkins Biostat has a couple of NCI researchers with joint appointments.
Ok, that is it for now. Later this month we will blog about job interviews.
02 Dec 2013
In a New York Times op-ed titled Migration Hurts the Homeland, Paul Collier tells us that
What’s good for migrants from poor places is not always good for the countries they’re leaving behind.
He makes the argument that those that favor open immigration don't realize that they are actually hurting "the poor" more than they are helping. This post is not about the issue of whether migration is bad for the homeland (I know of others that make the opposite claim) but rather about the opinions I have formed by leaving my homeland to become an academic in a US research university.
Let me start by pointing out that an outstanding 470 Nobel prizes have been handed out to residents of the US or the UK. About 25% of these are to immigrants. These Nobel laureates include academics born in Egypt, Venezuela, and Mexico. In contrast, only one of the 20 prizes handed to Italy was to an immigrant (none in the last 50 years). I view my university as international, not american.
Throughout my career I have encountered several foreign graduate students/postdocs that ponder passing on academic jobs in the US to go back and help the homeland. I was one of them and I admire the commitment of those who decide to go back. However, I think it's important to point out that the accomplishments of those that take jobs in American research universities are in large part due to the unique support that these universities provide. This is particularly true in the sciences were research success depends on low teaching loads, lab infrastructure, high-performance computers, administrative support for grant submission, and talented collaborators.
The latter is by far the most important for applied statisticians like myself who depend on subject matter experts that provide quantitative challenges. Having a critical mass of such innovators is key. Although I will never know for sure, I am quite certain that most of what I have accomplished would not have happened had I returned home.
It is also important to point out that my homeland benefits from what I have learned during 15 years working in top research universities. I am always looking for an excuse to visit my friends and family and I also enjoy giving back to my alma mater. This has greatly increased my interactions through workshops, academic talks, participation in advisory boards, and many other informal exchanges.
So, if you are an up-and-coming academic deciding if you should go back or not, do not let guilt factor into the decision. Humanity benefits from you maximizing your potential. Your homeland will benefit in indirect ways as well.
ps - Do people from Idaho feel guilty for leaving their brain-drained state?
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