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<p>Dude, what the hell. So basically, they’re taking the total number of PHD students, dividing it by the total number of undergrads and then multiplying by 1000. So what?</p>
<p>If what you’re trying to find is what percentage of undergrads go on to pursue PHDs as an estimator of how “pre-professional” the undergrad population is, then this list is useless.</p>
<p>Example:</p>
<p>School A has undergrad population of 100 and grad population of 10. Assume for the sake of simplicity that under and phd last one year.</p>
<p>All undergrads at school A decide to go to grad school, so 0% are “pre-professional.” But, because the grad school has a max population of 10, we get 10/100*1000=100 = Score A. The other students have to go to another school for grad school.</p>
<p>Now let’s look at school B. It looks exactly the same as School A (# under at A= # under at B and # phd at A=# phd at B) with the excpetion that people at B hate phd and just want to work/go to law/medicine/etc. </p>
<p>Because of this, 0% of undergrads at B are going to get a PHD and 100% are “pre-professional” Size of Phd program stays the same and just recruits from other undergrad colleges for Phd class. Score B = 10/100*1000=100= Score of A.</p>
<p>So two scenarios:
School A in which 100% of undergrads are “pre-professional” and school A gets score of 100.
School B in which 0 % of undergrads are “pre-professional” and school B gets score of 100.</p>
<p>Your score on this list tells us little to nothing about the percentage of undergrads at the school that are “pre-professional”. What affects the scores are the sizes of the under and grad programs.</p>
<p>Admittedly your list is objective in the sense that it is backed up by (old) data, but data is meaningless if you don’t know how to interpret it.</p>