Seth’s blog

After posting about Doug Lemov, I ordered Teach Like a Champion. It arrived yesterday. Leafing through it, I came across a section titled “The Irony of What Works,” which begins:

One of the biggest ironies I hope you will take away from reading this book is that many of the tools likely to yield the strongest classroom results remain essentially beneath the notice of our theories and theorists of education.

Lemov continues with an example: Teaching students how to distribute classroom materials, such as handouts. This can save a lot of time. Then he adds:

Unfortunately this dizzyingly efficient technique — so efficient it is all but a moral imperative for teachers to use it — remains beneath the notice of our avatars of educational theory. There isn’t a school of education that would stoop to teach its aspiring teachers how to train their students to pass out papers.

The last chapter of Veblen’s  Theory of the Leisure Class is about just this — the importance that professors (like everyone else) place on status display and how this interferes with their effectiveness. The connection with self-experimentation is that no matter how effective it is, no psychology department would stoop to teach it. Or, at least, that’s the current state of affairs.

The book’s index doesn’t include Veblen, although it does include Richard Thaler.

I’ve blogged many times about the desire of professors to show off and how it interferes with being useful. It doesn’t just make them bad teachers, it makes them bad scientists. Here’s an example from economics (via Marginal Revolution):

“The mainstream of academic research in macroeconomics puts theoretical coherence and elegance first, and investigating the data second,” says Mr. Rogoff. For that reason, he says, much of the profession’s celebrated work “was not terribly useful in either predicting the financial crisis, or in assessing how it would it play out once it happened.”

“[Academic economists] almost pride themselves on not paying attention to current events,” he says.

Pure Veblen, who in Theory of the Leisure Class provided many examples of people, including professors, priding themselves on being useless. Men wear ties, he said, to show they don’t do manual labor (which is clearly useful).

My research is closer to biology, where you can say the same thing: much of the profession’s celebrated work has not been terribly useful. Yesterday I gave an example (the oncogene theory of cancer).

Modern Veblen: Flight From Data.

A new Jeffrey Eugenides short story quotes Derrida. Quote 1:

In that sense it is the Aufhebung of other writings, particularly of hieroglyphic script and of the Leibnizian characteristic that had been criticized previously through one and the same gesture.

Quote 2:

What writing itself, in its nonphonetic moment, betrays, is life. It menaces at once the breath, the spirit, and history as the spirit’s relationship with itself. It is their end, their finitude, their paralysis.

“A little Derrida goes a long way and a lot of Derrida goes a little way,” said a friend of mine who was a graduate student in English. These quotes show why. In Theory of the Leisure Class, Veblen argued that professors write like this (and assign such stuff to their students) to show status. I have yet to hear a convincing refutation of this explanation nor a plausible alternative. Is there a plausible alternative?

Veblen was saying that professors are like everyone else. Think of English professors as a model system. Their showing-off is especially clear. It’s pretty harmless, too, but when a biology professor (say) pursues a high-status line of research about some disease rather than a low-status but more effective one, it does — if it happens a lot — hurt the rest of us. Sleep researchers, for example, could do lots of self-experimentation but don’t, presumably because it’s low-status. And poor sleep is a real problem. Throughout medical school labs, researchers are studying the biochemical mechanism and genetic basis of this or that disorder. I’m sure this is likely to be less effective in helping people avoid that disorder than studying its environmental roots, but such lines of research allow the researchers to request expensive equipment and work in clean isolated laboratories — higher status than cheap equipment and getting your hands dirty. I don’t mean high-status research shouldn’t happen; we need diversity of research. But, like the thinking illustrated by the Derrida quotes, there’s too much of it. A little biochemical-mechanism research goes a long way and lot of biochemical-mechanism research goes a little way.

An assistant manager at Marshall Fields, the Chicago department store, told Gawker the following story:

I was walking through the floor, and I hear a voice call my name. . . . Once she started speaking to me, I realized it was Oprah. Honestly, she is unrecognizable without the spackle/wig. Anyway, she was very nice, and asked me if I would offer my opinion on a china pattern she was looking at for her house. It was Villeroy and Boch (German, middle-range) “Petite Fleur.” Very cute, kind of French-country, with a small, scattered floral design. I said, “What’s not to like?” Oprah responded, “Well, it’s not that expensive, and I don’t want people who come to my house to think I’m cheap.”

Andrew Gelman writes:

If I had to come up with one statistical tip that would be most useful to you–that is, good advice that’s easy to apply and which you might not already know–it would be to use transformations. Log, square-root, etc.–yes, all that, but more! I’m talking about transforming a continuous variable into several discrete variables (to model nonlinear patterns such as voting by age) and combining several discrete variables to make something [more] continuous (those “total scores” that we all love). And not doing dumb transformations such as the use of a threshold to break up a perfectly useful continuous variable into something binary. I don’t care if the threshold is “clinically relevant” or whatever–just don’t do it. If you gotta discretize, for Christ’s sake break the variable into 3 categories.

I agree (and wrote an article about it). Transforming data is so important that intro stats texts should have a whole chapter on it — but instead barely mention it. A good discussion of transformation would also include use of principal components to boil down many variables into a much smaller number. (You should do this twice — once with your independent variables, once with your dependent variables.) Many researchers measure many things (e.g., a questionnaire with 50 questions, a blood test that measures 10 components) and then foolishly correlate all independent variables with all dependent variables. They end up testing dozens of likely-to-be-zero correlations for significance. Thereby effectively throwing all their data away — when you do dozens of such tests, none can be trusted.

My explanation why this isn’t taught differs from Andrew’s. I think it’s pure Veblen: professors dislike appearing useful and like showing off. Statistics professors, like engineering professors, do less useful research than you might expect, so they are less aware than you might expect of how useful transformations are. And because most transformations don’t involve esoteric math, writing about them doesn’t allow you to show off.

In my experience, not transforming your data is at least as bad as throwing half of it away, in the sense that your tests will be that much less sensitive.