EdLab at Teachers’ College does a lot of interesting things, like creating technologies for the classroom and libraries, blogging, and analyzing data sets in the education sector.
They’re 2 blocks from my office and I’ve never heard of them! And I even work with people at Teachers College. Columbia’s a big place.
P.S. Kaiser also makes an excellent point:
The most intriguing and unexpected question was: to do well in this business, do you have to read a lot? This is where I stumbled into a spaghetti carbonara analogy while mixing metaphors with the gray flannel, with which I have already been associated. Basically, statistics is not pure mathematics, there is not one correct way of doing things, there are many different methodologies, like there are hundreds of recipes for making carbonara. What statisticians do is to try many different recipes (methods), and based on tasting the food (evaluating the outcomes), we determine which recipe to use. Because of this, statisticians need to be well-read, to keep up with what are the new methods being developed.
This is actually the kind of thing that I say–complete with a cooking example!–except that in this case it’s his idea and not mine.
TC is huge. People at TC don't know what is going on at TC.
They hide themselves in an unlabeled area on the upper floor of a library so that when you exit the elevator, you are staring at a bunch of books and wondering where you are.
They say they are just getting into the whole area of using observational data sets to address education issues so I imagine they will be doing interesting work.
Nice – I've stolen the spaghetti carbonara analogy for a course I am putting together.
The "tasting the food (evaluating the outcomes)" could though mean anything from adjusting the prior to get the posterior you want – to noticing where the model is _too_ wrong and making it less wrong.
As for educational researchers using observational data sets to address education issues – they might be thinking quite differently than you.
My wife is a research co-ordinator for a Facluty of Education at a large university and in a panic last fall my son and I helped collate an overview of their research endeavours and capacities — 95+% qualitative methods, case studies, grounded research, historical overviews, etc – almost nothing based on thinking of data as generated from a haphazardly variable (probabalistic) process that can be pragmatically modeled to get a better understanding of the world.
I am not at all adverse to qualitative methods – its where I actually started out – but I was amazed at how out of balance this seemed to me – and apparently this is the norm for educational research (i.e. don't be suprised when some of them have not heard about the differences between an observational studies and RCTs)
And of course – this presents an insurmountable opportunity for those up to the challenge.
K?
TC is also "off-campus"