Miserable Teaching Evaluations

I have always disliked teaching evaluations, feeling that they fail to measure true teaching effectiveness. And it's not just sour grapes -- really, I swear, I generally do fine and have won several teaching awards. Rather, I simply think that teaching evaluations create bad incentives. Ask yourself: Is the behavior that maximizes teaching evaluations the same behavior that maximizes true teaching effectiveness? No way.

But it may be much worse than that. Check out the abstract below for a seminar to be presented in Penn Statistics next week by Philip Stark, a Berkeley statistician (and Associate Dean of the Division of Mathematical and Physical Sciences). Paper here.

TEACHING EVALUATIONS (MOSTLY) DO NOT MEASURE TEACHING EFFECTIVENESS

PHILIP STARK - UNIVERSITY OF CALIFORNIA, BERKELEY

Joint work with Anne Boring (SciencesPo) and Kellie Ottoboni (UC Berkeley)

Student evaluations of teaching (SET) are widely used in academic personnel decisions as a measure of teaching effectiveness. We show:

·         SET are biased against female instructors by an amount that is large and statistically significant

·         the bias affects how students rate even putatively objective aspects of teaching, such as how promptly assignments are graded

·         the bias varies by discipline and by student gender, among other things

·         it is not possible to adjust for the bias, because it depends on so many factors

·         SET are more sensitive to students' gender bias and grade expectations than they are to teaching effectiveness

·         gender biases can be large enough to cause more effective instructors to get lower SET than less effective instructors.

These findings are based on permutation tests applied to two datasets: 23,001 SET of 379 instructors by 4,423 students in six mandatory first-year courses in a five-year natural experiment at a French university, and 43 SET for four sections of an online course in a randomized, controlled, blind experiment at a US university. 

Online Volatility Data and Labs

I am reminded that I had planned to post on data/analysis sites that focus on financial asset return volatility measurement and modeling.

To my mind, the key trio is implied vol, GARCH vol, and realized vol. For implied vol it's the VIX at CBOE. For GARCH vol it's Rob Engle's V-Lab at NYU. For realized vol it's Neil Shephard's Realized Library at Oxford.

Yes, conspicuously missing is stochastic volatility. It's an academic simulator's paradise, but largely missing from serious/practical industry application. It's no accident; the benefit/cost ratio is just too low to excite many real financial-market modelers. One could argue that ten years from now things will look different. Perhaps, but I'm not at all sure. 

Heroic Econometrics Teachers: Tom Rothenberg and Dennis Sargan

I'm not sure why this popped into my head just now.

There have been many fine graduate econometrics teachers/mentors; their armies of well-trained students now populate top universities.  But two seem to me to have transcended the rest, achieving an almost mystical status: Tom Rothenberg and Denis Sargan. They trained many dozens of students on both sides of the Atlantic, and more generally they influenced the perspectives and careers of many thousands. (See, for example, the Rothenberg tribute volume edited by two fine Rothenberg students, Don Andrews and Jim Stock, and the Sargan bio by two fine Sargan students, David Hendry and Peter Phillips.)  How did Rothenberg and Sargan do it?  What was their secret?  Surely an ethic of selfless giving played a huge role.

An interesting and puzzling thing (to me at least) is that, perhaps amazingly in our small academic world, I never met Tom or Denis.  My loss, for sure.