Ranking the schools by LSAT boost, waitlisted candidates included

Hey all.

I finally made it through the initial push in crunching and organizing numbers.  There is still a ton that can be done, and surely a lot I haven't thought of yet, but over the next couple weeks I am going to be posting preliminary results.  The first is from my Model 1, which includes accepted, rejected, and waitlisted applicants (I will shortly release the same ranking for my Model 2 results, based on data that only includes acceptances and rejections).

In the table below you will find schools ranked by the "boost" at each school associated with a one-point increase in LSAT scores of candidates.  The % number associated with each school is the % increase in the likelihood of an applicant being accepted vs. waitlisted or rejected (and also the % increase of an applicant being either accepted or waitlisted vs. rejected) for each additional LSAT point (think 169 vs. 168 here).  There are floors for the LSAT, for sure...a 148 is not X% more likely to get into Harvard than a 147, after all.

One of the problems with this is that there are almost certainly diminishing returns past a certain point with your LSAT score, so it's not a straight linear proposition.  Still, the goal here isn't so much precision (God, if I can just get one more LSAT point I'll increase my chances by X%!) but to both give a rough idea of how schools weigh the LSAT, and comparing the schools among each other.

Again, please remember that this is all done based on user-reported applicant data at Law School Numbers, with all the caveats that go along with it.

I'm not sure how many more ways I can disclaim this whole thing without sounding like I'm actually saying, "Pay not attention to what you're about to read", but again...there's no crystal ball here and I'm not a stats PhD.  Take this for what it's worth, and ask any questions you have.

Without further ado, 100 law schools ranked by LSAT boost:




Some notes:

• Every single solitary school I have data on had a statistically significant boost associated with LSAT scores.  While this is not surprising, the same can't be said (although barely) for the GPA, which I will post soon.

• See Stanford in last place?  That does NOT mean that Stanford doesn't care about your LSAT score!  The groups of applicants applying to each school is going to be reasonably homogeneous, so Stanford being last in the list just means that, relative to other schools, Stanford places less weight on the LSAT in drawing distinctions between largely similar applicants.  

• Eventually, I would love to figure out WHY schools give different boosts for the LSAT, using the numbers from the table as the dependent variable.  Off the top of my head, with six of the bottom ten spots being occupied by T14 schools, I'm thinking USNWR rank might be a good independent variable to include in the model.  If anyone can think of anything else (measurable) that I might include, let me know!

As always, feedback and input is not only welcome, but encouraged.

Stanford Law profile and custom analysis requests

Hey all,

I have finished the profile for Stanford Law School, which is you can get by clicking on that thing you just read that said Stanford Law School, or by finding Stanford in the page list on the right.  It's  pretty interesting, and kind of confirms some of the conventional wisdom about Stanford in terms of its numbers preferences, so have a look.

Also, you might notice that I added a "custom analysis request" form over there on the right sidebar as well.  If you have a certain school that you're just dying to see profiled, or you have an idea of a concept you would like to see analyzed, I'll do my level best to make it happen as quickly as possible (or let you know if it's just outside the scope of my capabilities or the data).  Please hit me with any ideas you have, because that will make it easier to decide what to post here while I continue working on comparative analyses.

Thanks!

Very basic T14 vs. Non-T14 breakdown

I have been plugging away processing the data, putting together school profiles as I get requests, and generally trying to think of the best way to approach this.  I definitely appreciate the feedback I've gotten so far, so keep it coming.

I did want to do something quick to post while I continue working, so I thought it might be interesting to take a quick look at factors as they play a role in the top 14 law schools as compared to the rest of the law schools outside the top 14.  I'm using the same Model 1 as always, in which I regress these independent variables (LSAT score, GPA, earlier month sent, URM status, non-traditional student applications, and female) on the dependent variable, which in this case is the decision result (acceptance, waitlist, or rejection). Results are below:

                                

For those who are reading this blog for the first time, those percentages given correspond to the increase in the likelihood an applicant has of either

• Being admitted as opposed to being waitlisted/rejected
• Being admitted/waitlisted as opposed to being rejected

So, for instance, at a T14 school, you're 26% more likely to be admitted with a 173 LSAT as opposed to a 172 LSAT, all other factors being held equal.

Seems pretty clear that the T14 schools give much bigger boosts for numbers, earlier submission (want to get squared away earlier?), and both URM and female applicants (the URM boost is much bigger than that for the non-T14).  The only group of applicants that seems to fare better outside the T14 are non-traditional applicants, who get a little boost in the non-T14 but nothing at all in the T14.

Of course, there is plenty of variation within these two categories of schools, and that's what I'm working on.  But, I figured this would be worth looking at in the meantime.

Comments, feedback, questions, and requests welcome!

New school profiles added, thoughts on future projects

If you take a look over to the right, you'll see that in the past couple days I have added (by request) school profiles for Harvard, Columbia, and Yale.  If any of these schools interest you, please have yourself a look.

I continue to process that data and organize it, and hopefully shortly we'll be able to look at some comparisons.  I've been thinking about what I want o do with it, and here are some ideas I have come up with:

• Looking at the approaches schools appear to have taken, both within the T14, and T14 vs. the rest, since applications started to crater a couple cycles ago.  Do numbers matter more or less?  How about everything else?  What larger trends do we see, and what school-specific approaches, if any?
• Taking at least a superficial look at just how "epic" the predicted Epic Cycle was, and does this differ from tier to tier?
• Broad comparisons of the importance of numbers for T14 vs. non-T14, and whether or not emphasis placed on numbers correlates with USNWR rankings.
• I look at two basic models, one including waitlists and one excluding them, and I'm starting to see that for many schools, the boosts increase significantly in the admitted vs. rejected only model.  I'm interested in seeing if this gap correlates with the % of waitlist offers schools hand out, and seeing what we might deduce from that.
• Taking a little more in depth look at what goes on for splitters, reverse-splitters, and ED applications at each school, possibly in order to develop a list of splitter and reverse-splitter friendly schools, which may be useful for those candidates who fall into one category or the other.  
• Although it'll take some doing, I'd love to get a handle on scholarship awards and what factors play a role there.
• Finally, I want to develop a page where I just explain how some of this stuff works, what I have done with the data, etc.  The question someone posted about splitters on the UC Berekely page really slapped me in the face with this necessity.
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Okay, that's it for now.  I'll hopefully be adding more stuff soon, and in the meantime, I'll continue working so we can start to look at more interesting comparative analysis.  As always, I'm definitely open for suggestions and requests!

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:

• 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.