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.

Cornell University Law School Profile

Model 1: Waitlists Included Model

In the table below, you will find a list of variables that theoretically play a role in determining whether a law school accepts you or not.  The dataset used in this model includes users on Law School Numbers who reported being either accepted, rejected, or waitlisted at the school in question (those who were first waitlisted and then accepted or rejected are treated as acceptances or rejections, respectively).  For this model, I used an Ordered Logistic Regression, which allows you to look at how each of the independent variables affects the chances that you will be:
      • Accepted rather than (combined waitlisted/rejected)
      • (Combined accepted/waitlisted) rather than rejected
I know, I's a little confusing.  By way of example: in this model for Cornell, for two otherwise identical candidates, an additional point on the LSAT increases an applicant's chances of being accepted rather than either waitlisted or rejected by 42.3% (in other words, a 169 is 42.3% more likely than a 168 to get accepted rather than waitlisted or rejected, all else being equal).  A 169 is also 42.3% more likely to get either accepted or waitlisted than rejected than is a 168, all else equal.  If you have any questions, just e-mail me, or check out this link to the awesome UCLA stats site. 

In any case, here are the results from this first model, in which I test the impact of LSAT score, GPA, each earlier month the application is sent (and by earlier month, I don't mean month earlier...I mean September vs. October, or October vs. November), URM status, non-traditional status, and female applicant status.  Since Cornell doesn't have a binding-ED option, I left it off.  Everything is based on LSN data from the 2003/2004 cycle to the present.


Unlike most schools, Cornell's GPA boost is larger than its LSAT boost in this model.  The URM boost is substantial, with URMs being more than twenty times more likely to get in than idential non-URM candidates.  Female applicants are about twice as likely to get in as otherwise identical male applicants, and there is no statistically significant advantage associated with being a non-traditional applicant.  Overall, there is a very hefty 35.1% increase in an applicants chances of admission for each month earlier that applicant submits, but this bears further investigation.  For non-splitters, the boost is 34%.  For splitters it is an enormous 93.8%.  For reverse-splitters, the advantage for applying earlier disappears entirely.  The obvious takeaway here is that if you're got a phenomenal LSAT score but a less than stellar GPA, get that application in just as early as you can to give yourself the best chance at acceptance.

Model 2: Waitlists Excluded Model

The next model excludes waitlists, including only applicants that reported either being accepted or rejected (whether that was directly, or after first being waitlisted).  The results in the table should be interpreted in the same way, but the interpretation is a little easier.  The number given for each variable is simply the increase in likelihood of being accepted rather than rejected.


The boosts - except for the earlier application boost - all get bigger in Model 2, where we're dealing exclusively with acceptances and rejections.  Again, Cornell bucks the trend, giving a bigger GPA boost than LSAT boost.  Here, the URM boost gets massive, with URM candidates more than 70 times more likely to be accepted than otherwise identical non-URM applicants.  The boost for being a female applicant grows substantiall, too, with women almost 1.5 times more likely to get in, all else equal.  Again, there is a bump for applying early, though it is smaller in this model.  Also again (although you don't see it in the table), that bump is much bigger for splitters (62.1%) than non-splitters (26.7%), and completely disappears for reverse-splitters.  Same analysis applies to this model as applied to the last one.

Non-Splitters, Splitters, and Reverse-Splitters: Acceptance/WLs/Rejections, and Means

Last, I'm including a table that breaks down how non-splitters, splitters, and reverse-splitters are represented in the data.  This one you really have to be careful with, because the data on LSN does skew towards higher-caliber applicants, and so acceptances are more highly represented than they are in the applicant pool.  Really, the value of this kind of thing will become more clear when we can compare schools, because that same "higher-caliber" applicant caveat will apply across the board, so we can probably draw somewhat valid conclusions by comparing schools.  For now, I'll include it for interested parties, but please do not look at this and say to yourself, "Self, as a splitter I have an X% chance of getting into Alabama!"

Interesting here is that splitters have a lower success rate than non-splitters, since this is not typically the case.  As usual, reverse-splitters have a lower acceptance rate than the other two groups, but the gap between reverse-splitters and splitters is much narrower than it usually is.  If you're a splitter, your LSAT doesn't matter that much, your GPA does.  For reverse-splitters, the opposite seems to be true, although in neither case are there tremendous differences in the numbers between the three categories (accept, waitlist, reject).

In sum, Cornell seems to be one of the more reverse-splitter friendly schools, with its atypically high emphasis on GPA and reverse-splitter success rate (especially vis-a-vis its splitter success rate).  For splitter candidates with their hearts set on Cornell, all hope is not lost; just make sure you have that application in tip-top shape and ready to go just as early as you possibly can to increase your chances (substantially, at that).

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