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 the University of Texas, 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 49.4% (in other words, a 173 is 49.4% more likely than a 172 to get accepted rather than waitlisted or rejected, all else being equal). A 173 is also 49.4% 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 Texas 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.
What really jumps out at me here is that Texas's GPA boost is considerably larger than its LSAT boost, indicating that Texas places more weight on GPA than it does on the LSAT (which isn't to say the LSAT is unimportant, which a quick glance at Texas's medians will tell you). URM candidates also get quite a boost in terms of their chances at Texas, being 20.4 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" in terms of LSAT points 7.5 (in other words, a URM candidate gets the same boost for that status as that enjoyed by scoring an additional 7.5 points on the LSAT). This has to be taken, again, bearing in mind the different LSAT and GPA floors for URM vs. non-URM candidates, and how big the URM bump is compared to the relatively small LSAT bump. Applying earlier doesn't seem to get you anywhere, and neither does being a non-traditional applicant. Female applicants, on the other hand, get a pretty handsome boost, being 66.9% more likely to get in than an otherwise identical male applicant.
Model 2: Waitlists Excluded ModelThe 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 get bigger, but nothing much else changes. No boost for applying early or non-trad status, and the same boosts as in the first model apply, but are larger. The LSAT/GPA weighting stays relatively lopsided, as well.
Non-Splitters, Splitters, and Reverse-Splitters: Acceptance/WLs/Rejections, and MeansLast, 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 the University of TExas"
The big deal here is the lack of luck splitters have. At almost all other schools I have looked at so far, splitters are slightly more likely to be admitted than are non-splitters, but that is clearly not the case for Texas. Reverse-splitters are as out-of-luck as they seem to be at most schools. For splitter applicants, there is a pretty clear, though not huge, drop off in GPAs as you move from category to category, and for reverse-splitters, there's not a whole lot of difference between the accepts, the rejects, and the waitlists. Overall, non-splitter candidates fare better, and these numbers may indicate that for splitter and reverse-splitter applicants, the admissions committee at Texas are digging deeper into the application and giving a lot of consideration to essays, recommendations, and the like when making decisions on those applicants.
To summarize, it seems like Texas places a relatively more emphasis on GPA than do most other top schools, and this is partially supported by the surprising lack of success that splitters have at UT (although the typical lack of success of reverse-splitters mitigates this a bit). Applying early makes no discernible difference, so make sure you take your time getting your application in tip-top shape before sending it.
As a parting note, as with most state schools, it would be interesting to look at the effect of residency on an applicant's chances, but a dearth of available data (at least available to me) makes it impossible at this point.
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