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!