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afabmp
Current Student
Joined: 05 May 2013
Last visit: 26 Apr 2018
Posts: 59
Given Kudos: 15
Schools: HBS '16 (M) Tuck '16 (A) Duke '16 (A$) Booth '16 (WL) Wharton '16 (D) Sloan '16 (WD) Kellogg '16 (D)
GMAT 1: 750 Q49 V44
GRE 1: Q161 V165
Products:
Schools: HBS '16 (M) Tuck '16 (A) Duke '16 (A$) Booth '16 (WL) Wharton '16 (D) Sloan '16 (WD) Kellogg '16 (D)
GMAT 1: 750 Q49 V44
GRE 1: Q161 V165
Posts: 59
02 Nov 2017, 19:28
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We did an analysis of accepted/rejected applications & looked at how "splitters" fared--that is, people with much higher GMATs than GPAs, or vice versa. The data isn't good enough for the results to be conclusive, but at the very least it should give all you low-GPA folks aiming for top GMAT marks something to consider 
Here's a few charts, but you can read more in our coverage of clear admit livewire data
A "splitter score" is basically your GMAT percentile minus your GPA percentile (that is: the percentile rank across all applicants for that school). So, for example, if the box on the left is high, the school took a lot of high-GMAT/low GPAs.
Here's a few charts, but you can read more in our coverage of clear admit livewire data
A "splitter score" is basically your GMAT percentile minus your GPA percentile (that is: the percentile rank across all applicants for that school). So, for example, if the box on the left is high, the school took a lot of high-GMAT/low GPAs.
08 Nov 2017, 06:51
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My question here, is how would one differentiate between somebody with 20% gmat and gpa, or 90% gmat and gpa? Both of which are neutral split, but a world apart in terms of application strength. Or 80/100 vs 20/40 etc?
How is this difference displayed in the graph? Maybe I'm interpreting this wrong, but I feel like without that there isn't much value.
How is this difference displayed in the graph? Maybe I'm interpreting this wrong, but I feel like without that there isn't much value.
afabmp
Current Student
Joined: 05 May 2013
Last visit: 26 Apr 2018
Posts: 59
Own Kudos:
Given Kudos: 15
Schools: HBS '16 (M) Tuck '16 (A) Duke '16 (A$) Booth '16 (WL) Wharton '16 (D) Sloan '16 (WD) Kellogg '16 (D)
GMAT 1: 750 Q49 V44
GRE 1: Q161 V165
Products:
Schools: HBS '16 (M) Tuck '16 (A) Duke '16 (A$) Booth '16 (WL) Wharton '16 (D) Sloan '16 (WD) Kellogg '16 (D)
GMAT 1: 750 Q49 V44
GRE 1: Q161 V165
Posts: 59
Kudos:
50
08 Nov 2017, 09:14
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cxa0897
Good catch--you're hitting at one of the big challenges of data like this
There's no direct indication of the GPA/GMAT on the chart, but in general we can make some assumptions given the avg #s for those schools.
One assumption is important I think here: that "splitters" are randomly distributed throughout the GMAT/GPA curve. If, for example, all splitters were 20/40% like you mention, the chart would be pretty misleading.
But since the chart's split into accept / denies, and the schools publish their ranges, I think it's reasonable to assume that most admits are high on both %s, or _very_ high on one and so-so on the other. An applicant who was low on both would probably be rejected.
Another assumption that could save us here is: the distribution of splitters at one school looks more/less like the distribution at other schools. This would mean that, for the purposes of comparing schools, the effects of what u describe would be minimal.
We're playing w/ diff't ways to display the data though, so thinking through stuff like this is really useful for us--so thanks for taking a second to share your thoughts here
