Thing

DISCLAIMER


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.

Boston 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
By way of example: in this model for Boston University, 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 40.7% (in other words, a 167 is 49.6% more likely than a 166 to get accepted rather than waitlisted or rejected, all else being equal).  A 167 is also 49.6% more likely to get either accepted or waitlisted than rejected than is a 166, 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.  Although Boston University does have a binding-ED option, it is only in its second year of existence and data is scarce, thus I haven't included it.  Everything is based on LSN data from the 2003/2004 cycle to the present.

                              

URM candidates at BU are a a little more than 5 times more likely to be accepted than an otherwise identical non-URM candidate.  One thing to always keep in mind when interpreting this is that there are almost certainly different "floors" for LSAT and GPA for URM applicants than non-URM applicants.  This matters because below the numbers "floors" for non-URM applicants, a URM is pretty much infinitely more likely to get in.  The URM boost in terms of its LSAT equivalent is a little on the low side at 4.1 points, and the 0.34 GPA equivalent is pretty average, based on what I've seen.  Boston University gives for a bigger boost for .10 extra GPA points than it does for a 1-point increase in the LSAT, which is not the norm.  BU gives no advantage to non-traditional students, but female applicants are 31.1% more likely to get in than otherwise identical male counterparts, which is a not the biggest boost you'll see, but it's something. Finally, there seems to be a definite advantage to applying early to Boston University, with your chances of admission increased by 17.8% for each month earlier you apply.

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.

                              

When we drop waitlists and only look at straight accepts or rejects, the boosts generally get quitea bit bigger, and the GPA and LSAT boosts almost entirely even out.  This possibly indicates that BU likes to keep a decent reserve of GPA applicants above the median just in case they get a lot of withdrawals from high GPA admits, but maybe they're not as nervous about losing their higher LSAT applicants to other schools?  As always, I am interested in any thoughts on why this might be.  The URM boost explodes in the second model, increasing to almost 2000%, meaning a URM candidate is almost 20 times more likely be accepted rather than rejected as compared to an identical non-URM applicant.  Again, please bear in mind the caveat from the first model about disparate "numbers floors" for URM and non-URM candidates.  The advantage for female applicants also increases considerably, as they're about twice as likely to get in rather than be rejected as are identical male candidates.  The advantage for applying early drops a bit, but is still definitely there and worth considering.

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 Boston University!"



Interesting here is that splitter applicants to BU don't appear to fare any better than non-splitter applicants (based on what I've seen so far, splitters usually have a little - and sometimes a lot - higher acceptance rate on LSN as do non-splitters).  When it comes to being a splitter, it seems like once you're above the 75th, they're not necessarily looking for higher and higher LSAT scores, but your GPA definitely makes a difference for you, as we can see by the quickly declining GPAs for splitters as we move from accept to waitlist to reject (and especially from waitlist to reject: 3.23 to 2.87!)  The inverse is true for reverse-splitters; once your GPA is over the 75th, that matters a whole lot less than your LSAT, as mean LSATs fall as we go from accept to waitlist to reject.

More or less standard stuff for Boston University.  The only things that really stand out is the weight given to the GPA vis-a-vis the LSAT in the first model but, again, that disappears when we are just considering acceptances vs. rejections with waitlisted candidates left out.  Otherwise, a decent little boost for applying early in both models, and significant boosts for females and URMs that just get bigger from model 1 to model 2.

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