Rather than stick all the school profiles here, which will really clutter up the page, I'm just going to make separate pages for each school. So, you can find Alabama's (and the others, as I add them) on that page list over to your right. See it? Yeah, there it is.
Also, although I hope to be able to do some stuff with scholarship money, Rob from mylsn.info has pointed out to me that there are potentially some problems with the dataset that it will take a while to work through (if they're solvable at all), so that is taking a back burner for a while.
Thursday, May 30, 2013
Wednesday, May 29, 2013
University of California Berkeley Profile
I am sorting through the data I have, going through schools alphabetically. It's going to be a long project, but I'm making progress day by day. For now, though, by request, I am posting a school-profile for the numbers crunching I did on the UC Berkeley data available from Law School Numbers. Again, there are a variety of important factors to keep in mind when considering this analysis, including the possibility that some of that datapoints are completely bogus, and that the LSN-users might be skewed a little towards the top-end of applicants. Given the number of datapoints we have, though, and that I've done at least a little pre-emptive data cleaning, I think this is worth a look. The first table I present is the results of an ordered logistic regression, which allows me to use folks who reported being acceptances, rejections, and waitlists. Eventually I'll get around to creating descriptions for all of these types of analysis so I can just say "click here to see what I mean by this," but for now, let me just explain it. In an ordered logistic regression, the dependent variable is a categorical variable, and in our case I have coded an acceptance as a 2, a waitlist as a 1, and a rejection as a 0 (in descending order of desirability, although I know plenty of waitlisted candidates who cry that they'd rather just be put out of their misery with a rejection than suffer in purgatory). The data you see below gives the percentage-increase in the likelihood, other variables controlled for, of being either:
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.
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.
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.
- Accepted rather than (combined waitlisted/rejected)
- (Combined accepted/waitlisted) rather than rejected
I know, I know...it'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.
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.
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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