When you discuss law school applications, 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 applicant’s 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.
By the way, if you’re not sure what a “splitter” is, then make sure to check out Dave Killoran’s post that breaks it all down: What are Splitters, Reverse Splitters, and Super Splitters? And, if you know what all of those terms are and know you are actually a Reverse Splitter, see our article about Reverse Splitter Friendly schools.
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 unfortunately had to exclude URM data because it can really skew the overall picture. I also excluded any schools for which I did not feel that I had sufficient data-points for analysis. Just to break down the categories for you:
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.
Non-Splitter GPA: This is the average GPA of admitted non-splitter applicants.
Splitter GPA: This is the average 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 GPA a school will go compared with its average in order to chase high LSAT scores.
Splitter Success: This is the % of splitter applicants 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 is splitter-friendly.
Index: This is the number I devised to take into account the salient data from the other categories. It is simply (GPA Differential + LSAT/GPA Differential) * Splitter vs Non Success. The higher the number, the more splitter-friendly a school is.
The mean index number for the schools included is 1.85, 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.
Neutral-Unfriendly: These schools have an index number that is between the mean and one standard deviation below.
Splitter Unfriendly: These schools have an index number that is more than one standard deviation below the mean.
Stanford, Berkeley, and Pepperdine were more than two standard deviations below the mean, as you can see. 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, just to give readers a relative comparison.
So, there you have it. And next up, the numbers themselves, with the schools ranked in order of USNWR ranking:
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, and in general there is a lot more towards the middle/bottom of the Top 100. 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 1.29 (much lower than the overall average). Here are the results:
Northwestern and UVA are the only schools we could call splitter friendly (again, compared only to the other Top 14 schools), and Columbia, NYU, Penn, Michigan, Duke, and Georgetown all fall on the friendly side of average. This – combined with the relative splitter-unfriendliness of the California schools, 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 and UVA, I’d say).
As always, please keep in mind that this analysis is based on numbers taken from publicly available applicant-reported data (which I have cleaned to make as accurate as possible), and does not come from the law schools themselves. In other words, I wouldn’t look at any of the numbers in this table as perfect representations of reality, but it does make for interesting comparison and reflection, and hopefully is of some use to those pondering applying to law schools.
If you have any questions or comments, please post them below.