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.

Harvard 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 Harvard, 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 44.4% (in other words, a 173 is 44.4% more likely than a 172 to get accepted rather than waitlisted or rejected, all else being equal).  A 173 is also 44.4% more likely to get either accepted or waitlisted than rejected than is a 172, 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 Harvard 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.


URM candidates get a fantastic boost in terms of their chances at Harvard, being 33.3 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 "bump" is pretty big in terms of LSAT points is also massive, at 9.5 (in other words, a URM candidate gets the same boost for that status as that enjoyed by scoring an additional 9.5 points on the LSAT), and at almost half a point, the GPA boost is considerable as well.  This, of course, is also a function of how much of a boost Harvard gives for LSAT and GPA, and though the LSAT boost is pretty big, the GPA boost is just massive.  Non-traditional students don't get any type of bump at all, but females are 63.0% more likely to get in than otherwise identical male counterparts, which is on the bigger end of things. Finally, there seems to be a definite advantage to applying early to Harvard, with your chances of admission increased by almost 17% 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 just keep getting bigger (and significantly so).  When it comes down to either getting in or getting a rejection, the URM boost is just enormous, with URM candidates almost 100 times more likely to get in than an otherwise identical non-URM candidate.  Again, please bear in mind the caveat from the first model about disparate "numbers floors" for URM and non-URM candidates.  Female applicants are almost twice as likely to get in, all else equal, and the LSAT, GPA, and boosts for earlier applicants also increase significantly.  One thing that the significant difference in these boosts between models may point to is Harvard's massive class size, and the fact that it might want to waitlist a sizable percentage of its applicants to make sure there is a good reserve of high-number candidates.  When it comes time to actually pull from that waitlist, perhaps Harvard starts at the top, numbers-wise.

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 Harvard!"

It does appear that Harvard waitlists a pretty significant percentage of its applicants, and it is worth noting that (like most schools) Harvard waitlists a higher percentage of splitters and reverse-splitters than it does non-splitters, probably to make sure numbers balance out to maintain medians.  The trends are pretty predictable for non-splitters, with numbers declining as you move from admitted to waitlisted, and waitlisted to rejected.  It's kind of a mixed-bag when it comes to splitters and non-splitters, although it's pretty clear that once you are above the 75th, that's in and of itself what matters for splitters, and then GPA plays a much bigger role than the LSAT once that threshold is reached.  Same goes for reverse-splitters, although the trend concerning LSATs is not quite as clear.

So, in sum, at the point that your LSAT and GPA are set in stone, the best you can do is apply early, at least in terms of the factors we look at here.  Of course, there is a lot that these models don't account for, so I think it would be quite unwise to prioritize applying early over crafting the strongest possible application, because factors such as personal statements and letters of recommendation obviously matter.  But you've got all summer, right?  Have that stellar application ready to go on the first day applications open, if you can.

No comments:

Post a Comment