Welcome! The goal of this blog is to share my analysis of the free, publicly available user-reported law school applicant data from Law School Numbers. Using the data from Law School Numbers is problematic for a variety of reasons (such as users misreporting their actual information, users creating fake accounts, selection bias, etc.) and if I had access to it, I'd much rather work with the data that schools themselves have on applicants. We have what we have, though. Also, while I do have some facility with the type of statistical analysis I employ in my blog posts, I am far from being a professional statistician. I am doing this solely for the purpose of providing my analysis to interested readers, getting feedback, and generating discussion. What I am not doing is prescribing courses of action for law school applicants, or pretending to actually know what goes on behind closed doors in law school admission committees' meetings. I am, however, interested in looking at the story the numbers seem to portray, and sharing that with people with similar interests. I think I'll be able to provide a lot of interesting, and perhaps even helpful, analysis here, but at the end of the day, it is up to the individual law school applicant to put together applications and application strategies tailored to his or her own hopes and goals.

Thursday, July 11, 2013

Ranking the schools by LSAT boost, waitlisted candidates included

Hey all.

I finally made it through the initial push in crunching and organizing numbers.  There is still a ton that can be done, and surely a lot I haven't thought of yet, but over the next couple weeks I am going to be posting preliminary results.  The first is from my Model 1, which includes accepted, rejected, and waitlisted applicants (I will shortly release the same ranking for my Model 2 results, based on data that only includes acceptances and rejections).

In the table below you will find schools ranked by the "boost" at each school associated with a one-point increase in LSAT scores of candidates.  The % number associated with each school is the % increase in the likelihood of an applicant being accepted vs. waitlisted or rejected (and also the % increase of an applicant being either accepted or waitlisted vs. rejected) for each additional LSAT point (think 169 vs. 168 here).  There are floors for the LSAT, for sure...a 148 is not X% more likely to get into Harvard than a 147, after all.

One of the problems with this is that there are almost certainly diminishing returns past a certain point with your LSAT score, so it's not a straight linear proposition.  Still, the goal here isn't so much precision (God, if I can just get one more LSAT point I'll increase my chances by X%!) but to both give a rough idea of how schools weigh the LSAT, and comparing the schools among each other.

Again, please remember that this is all done based on user-reported applicant data at Law School Numbers, with all the caveats that go along with it.

I'm not sure how many more ways I can disclaim this whole thing without sounding like I'm actually saying, "Pay not attention to what you're about to read", but again...there's no crystal ball here and I'm not a stats PhD.  Take this for what it's worth, and ask any questions you have.

Without further ado, 100 law schools ranked by LSAT boost:

Some notes:

  • Every single solitary school I have data on had a statistically significant boost associated with LSAT scores.  While this is not surprising, the same can't be said (although barely) for the GPA, which I will post soon.

  • See Stanford in last place?  That does NOT mean that Stanford doesn't care about your LSAT score!  The groups of applicants applying to each school is going to be reasonably homogeneous, so Stanford being last in the list just means that, relative to other schools, Stanford places less weight on the LSAT in drawing distinctions between largely similar applicants.  

  • Eventually, I would love to figure out WHY schools give different boosts for the LSAT, using the numbers from the table as the dependent variable.  Off the top of my head, with six of the bottom ten spots being occupied by T14 schools, I'm thinking USNWR rank might be a good independent variable to include in the model.  If anyone can think of anything else (measurable) that I might include, let me know!

As always, feedback and input is not only welcome, but encouraged.

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