The best predictors would appear to be a high math GRE score, being foreign, and having recommendation letters from "quality professors." Here is the paper and data. Those indicators also predict research productivity. Thanks to Craig Newmark for the pointer; here is his source.
Do beware the general problem with this kind of study. It is easy to set up a model where GRE scores do not predict later academic success, precisely because GRE scores are used as a dominant criterion for admission. An admitted student with low scores presumably has some other virtue in his favor. Yet it would be wrong to conclude that scores, in the population pool, are not correlated with quality. The real trick would be to look at a broader pool of applicants, not just those who were accepted and attended.
How upset should you be if you don’t meet all of those criteria, given that the R-squareds indicate that less than 10% of the variation in outcomes is explained by their 12 explanatory variables?
Ah, Tyler, but this paper does exactly what you suggest: The sample is all applicants, not just those who attend. The paper isn’t quite explicit about that, but look at the sample size: 344 from the cohort applying to enter in 1989. Princeton admits closer to one-tenth that many. For reasons that I haven’t been able to decipher with a quick glance, the analysis sample seems to be only 281, but again that is far too many to be just admitted students.
So the purely statistical problem that you worry about doesn’t seem to be present. There’s still a related, substantive issue: GREs may predict future productivity simply because they help students get into better programs, and better programs influence productivity. So the result doesn’t have to mean that high math GREs are indicators of a student’s ability; it could simply mean that admissions committees see them as such. Of course, this interpretation flies against evidence (at the undergraduate level) that selective schools don’t do much for their students…
How do you measure sheer determination or drive? That is what dominates..
Oh, I would give an arm and a leg (more like a few toes, really) to get to pursue a PhD in the US.
Unfortunatelly I’ll have to settle for whatever institution will have me, here.
(Off-topic, I know.)
Note to anyone facing the GRE: The general exam (math & language) is very, very much like the SAT. I suspect its actually somewhat easier. (I expect that general skills atrophy during college.)
I did really poorly on the first math portion–I failed to pace myself. It wasn’t a serious issue–my degree is in mathematics.
Finally, I would point out that not finishing is not necessarily a bad outcome. I was accepted into the PhD program, then withdrew (taking a terminal MA) four months later when I realized that I really didn’t want to do that…
I think it’s odd, a mistake, to post on a topic — David Irving and free speech — which demands conversation and yet to close comments.
KP is correct but the relation is weak anyway. The more important thing is that the GRE Math scores are useful as an indicator of a minimum level of competency. At second and third tier programs, a marginal admit with low math scores will struggle to even pass the first year. I have known of one or two cases of really low scorers who struggled and made it through but given how technical the material of the standard first year core is even for students who are all in the >720 range of the GRE it is not surprising that low scorers would get knocked out or underperform.
The rare exceptions are the clever kids who, knowing that they’re relatively weak in math, still tough it out and then configure their papers to focus on interesting applied issues requiring less sophisticated econometrics.
But it takes an awful lot of sweat and self-confidence to pull through the hard first year, pass prelims, and then remain positive even if one ends up at the bottom of the class on the first year exams.
I have a somewhat different datum to offer, as I am a refugee from a CS PhD program. In Computer Science, the higher-GRE students were more likely to leave, due to the number of companies actively seeking to lure smart PhD students away. Foreign students were much more likely to complete degrees due to visa rules, i.e. the risk of deportation if you don’t find a job; the extra costs companies pay to hire someone on a visa; and the less-tempting, lower salaries companies offer H1B’s.
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