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.

Tuesday, July 16, 2013

Which schools are splitter-friendly? (Non-URM edition)

The question of which schools are "splitter friendly" comes up pretty often, and it's not really an easy question to answer.  Are we looking for schools to which a high percentage of splitters are admitted relative to non-splitter applicants?  Schools that seem to value an applicants LSAT score much more than his/her GPA?  How about schools that are willing to go really low on the GPA scale to nab those high LSAT scores?

There is actually a lot of overlap between those questions, but they're not all the same thing.  There is a ton of anecdotal evidence out there, but the point of this blog is to try to get to the bottom of what the numbers themselves can tell us.  With the "splitter friendly" question, it's not all that easy.

What I have done here is try to create an index number that incorporates information to answer questions posed at the beginning of the post.  Both that index, and the data used to compute it, are found in the table below.  I have to stress that because there is so little data available - and especially little data available on URM applicants - the following applies to non-URM applicants only, and is based entirely on non-URM applicant data.  I excluded URM data because it can really skew the overall picture, and I thought it best to do them separately (although I'll have to approach analyzing URM splitter data differently, due to a serious dearth of data).  Just to break down the categories for you:

Non-Splitter GPA: This is the mean GPA of admitted non-splitter applicants.
Splitter GPA: This is the mean GPA of admitted splitter applicants.
GPA Differential: This is simply the difference between the previous two categories, and gives an indication of how much lower on the GPA a school will go compared with its average in order to chase high LSAT scores.
LSAT Bump: This is a number from my own regression analysis, and indicates the % increase in the likelihood of admission for each additional LSAT point an applicant has.
GPA Bump: This is the GPA equivalent of the LSAT bump (the % increase for each .10 GPA).
LSAT/GPA Differential: This is the LSAT Bump divided by the GPA Bump, to give us a measure of the relative importance of the two.  The higher the number, the more relative weight the LSAT has.
Splitter Success: This is the % of splitter applicans in the data who were accepted.
Non-Splitter Success: This is the % of the non-splitter applicants in the data who were accepted.
Splitter vs. Non Success:  This is Splitter Success divided by Non-Splitter Success, and gives us a measure of how splitters fare vs. their non-splitter counterparts.  If a school admits splitters at a higher percentage than non-splitter, the number will be greater than 1 (and if the opposite is true, it will be less than one).  The higher the number, the greater indication that the school favors splitter applicants.
Index: This is the number I concocted to take into account the salient data from the other categories.  It is simply (GPA Differential + LSAT/GPADifferential) * Splitter vs Non Success.  The higher the number, the more splitter-friendly a school is.

The mean index number for the schools included is 2.68, so I set that as a benchmark, and then broke the schools down into five categories:

Very Splitter Friendly: These schools have an index number that is more than two standard deviations above the mean (Dark Green)
Splitter Friendly: These schools have an index number that is between one and two standard deviations above mean. (Light Green)
Neutral-Friendly: These schools have an index number that is between the mean and one standard deviation above. (Yellow)
Neutral-Unfriendly: These schools have an index number that is between the mean and one standard deviation below. (Orange)
Splitter Unfriendly: These schools have an index number that is more than one standard deviation below the mean. (Red)

No schools were more than two standard deviations below the mean, although Stanford was right on the cusp.  The neutral-friendly and neutral-unfriendly categories make me a little squeamish.  Since they are all within one standard deviation of the mean, they're all pretty average.  In the end, I decided it was better to distinguish between the above-average and below-average middle, though.

So, there you have it.  And next up, the numbers themselves, with the schools ranked in order of USNWR ranking:

So, there you have it. Remember, this is categorizing schools by their relationships to each other.  As you can see, there's not a whole lot of splitter love, in the grand scheme of things, going on in the Top 14.  Still, just as we can compare all the schools among themselves, we can isolate the Top 14 and do the same thing.  And since someone is surely interested in how that shakes out, why don't we just do it right now?

For the Top 14, I kept the categories and color coding the same, but based everything off the mean index score of just the T14, which was 1.55 (much lower than the overall average).  Here are the results:

Northwestern and Georgetown are the only schools we could call splitter friendly, and Columbia, NYU, Penn, UVA, and Duke all fall on the friendly side of average.  This more or less confirms the conventional wisdom you hear thrown around, but I guess I should stress once again that there's really not a whole lot of splitter friendliness in the T14 (outside Northwestern, I guess).

Thursday, July 11, 2013

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.

Wednesday, July 10, 2013

Cornell profile up, plans

By request, I have posted a profile for Cornell.  In looking at the numbers for Cornell (and yesterday for Penn) it strikes me that some of the numbers are sometimes different for splitters than non-splitters and reverse-splitters, namely the boost associated with applying earlier (splitters get a much bigger boost at both of those schools for earlier applications).  It might take a while, but I'm going to take a closer look at this for all schools eventually, because I think it's information that matters.

Tuesday, July 9, 2013

New school profiles

Hey all!  Thanks for continuing to check out this blog, and special thanks to the people who have sent requests through that form over on the right.  I'm trying to work through some issues, and continue processing data, but I did want to alert you that NYU and Penn profiles are now up, and they are listed in the right sidebar.

Saturday, July 6, 2013

Non-URM and URM Medians

In response to a special request, I have compiled a list of medians by school (at least the schools that I have data for) for both URM and non-URM students.  Unlike the published medians that schools release (which report the medians for attending students), these are the medians for accepted students at the school, and are based solely on the applicant-reported data on Law School Numbers.  The usual caveats apply.

The first table is simply a list of the schools, alphabetically (or roughly alphabetically) with the LSAT and GPA medians for both non-URM as well as URM accepted applicants (these are only accepts, and not waitlisted students who never reported a final decision):

Non-URM and URM LSAT and GPA Medians

The next two tables list, respectively, the LSAT and GPA differences between non-URM and URM applicants, from largest to smallest:

LSAT Differential

GPA Differential

There's really not much to add to the raw data here, except to say that, while the median LSAT for non-URM candidates is invariably higher than it is for URM candidates, 10% of the schools I have data for actually have higher URM GPA medians than non-URM medians, which is kind of fascinating.  I'm interested in what anyone thinks might explain this.  I haven't really thought about it too much yet, and I don't have a lot of time at the moment, so give me a hand, will you?