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.

University of California at Berkeley 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 Berkeley, 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 29.3% (in other words, a 170 is 29.3% more likely than a 169 to get accepted rather than waitlisted or rejected , all else being equal).  A 170 is also 29.3% more likely to get either accepted or waitlisted than rejected than is a 169, all else equal.  If you have any questions, just e-mail me or something, and I'll try to explain better.  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 Berkeley 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.


The thing that really stands out to me here, although it may be hard to see if you haven't yet seen what these numbers look like for other schools, is the emphasis placed on GPA vs. LSAT.  At least judging by the schools I have looked at so far, Berkeley gives a lot more weight to the GPA vis-a-vis the LSAT than other schools do.  That boost for a .10-point increase in GPA is massive, and the LSAT boost is relatively small compared to most schools.  The boost for each month earlier the application is sent is also very substantial - in fact, many schools seem to give no boost for this.  The URM boost is pretty substantial, too, but one thing you have to 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, while the "boost" indicates that, all else equal, a URM is almost nine times as likely to get in as a non-URM applicant, this is inflated a bit because below the numbers "floors" for non-URM applicants, a URM is pretty much infinitely more likely to get in.  The increases for non-traditional applicants and female applicants surprised me a little, too.  The final numbers - URM equivalents in LSAT and GPA points - is simply the number of extra LSAT points a non-URM candidate would have to have to a boost equivalent to that of URM status.  It's an interesting way to look at how much the "URM bump" is really worth.

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.


Not a whole lot of difference between the two models, although there are slightly bigger boosts for most of the factors if we just consider acceptances vs. rejections, without considering waitlists (which are problematic both for yield-protection reasons - more prevalent at some schools than others, to be sure - and because a lot of "waitlist" profiles belong to people who didn't bother to update with a final status, so we can't just treat them as rejections).

Normally, the next thing I'd do here is take a look at how different factors influence scholarship awards, but because the number of observations for Berkeley is so low, and because it seems like a couple of datapoints really throw the whole thing off due to the small sample size, I'm going to leave that out.  If you're really interested, and promise to not read too much into it, e-mail me and I'll let you know.

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 Boalt!"  Promise?  Ok!

So there you have it.  I'm interested in thoughts anyone has.  For me, the real takeaways of this entire thing is that it pays big to apply to Berkeley as early as you possibly can, and that it's a pretty friendly place for non-traditional students and female applicants.  Also, the relative weight Berkeley gives to the LSAT and GPA is different from what we usually see, so if your LSAT isn't up to snuff but you've got a stellar GPA, it might be worth throwing a hail-mary Berkeley's way.

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