The following questions covered so far:
Q: My GMAT score is less than 700, should I submit my stats?
A: YES! There is no shame if yours is less then 700 or even 600. You are a unique person, no one knows your conditions, and probably for you 600 is as achievement as for some is 700. Your successful or unsuccessful example of application process is very important. We want to know whether people with "deficiencies" in their application like low GMAT or GPA still can make up in the competitive schools. You don't have to give neither your name nor Email address. There is nothing to be afraid of, while you can advance our understanding of the addmission process.
Q: I am afraid to submit my data bacause of privacy considerations!
A: Please have a look at the privacy statement.
Q: Submission is buggy! I am submitting my stat's but it seems to lag out.
A: It is. This is just a first release, fields are not actually validated yet. So, if you put some words instead of numbers in the GMAT field, it may mess-up the thing. Try again if submission didn't work. Technical problems will soon be fixed.
Q: What does the rank mean?
A: This is an attempt to consolidate all important quantitative properties of every MBA applicant. Frankly saying it doesn't mean a thing because it is widely accepted that at least in the highly selective schools qualitative side such as essays and recommendations play the most important role. However, having a single rank reflecting overall quantitative side of an application might be useful. In particular if we will try to correlate it with the actual results of the applications, ie whether those with high ranks succeed getting into the schools they desired. It is essential to understand that the ranking system is not to find who of us is the best, not at all. The system is an attempt to predict success in the application process (in respect of schools attempted) and should be viewed as such. All admission offiers use some sort of the ranking system in their work. We actually have no idea about their algorithms but we know that they are logical and use quantitative data (at least for screening). So, why not to try to reverse engineer them by creating similiar indicator? All we need is to collect quantitative and preferably qualitative data and comprare against results (rejected/accepted/waitlisted). While the only guarantee you can get is from an insurance cerfiticate, we can try to mastermind something close. Only the loosers don't try, so let's do it!
Q: What is the formulae of the rank?
A: The following ranking system was loosely designed by Alex Chudnovsky. It is by no means complete and comprehensive and I would be pleased to hear suggestions and improvements.
Fundamental assumption is that we should compare everyone against a perfect applicant. Perfect applicant is defined as a person with the maximum possible test scores (where maximum exists) and optimal quantitative indicators (maximum is undefined). Optimal quantitative indicator such as age and work experience are taken from the average admitted student in profiles of the Top10 schools. Optimal in this sense means that the perfect applicant will fit a school needs. For international (non-native English) students we consider TOEFL and TWE (if any).
The following variables are used in calculations:
Tests or indicators with feasible maximum value:
1. GMAT percentile (%)Optimal quantitative indicators:
2. Verbal %
3. Quantitative %
5. GPA (standard scale of 4.0)
6. Age (optimal assumption is 27 years)International only tests
7. Work experience (optimal assumption is 4 years)
8. TOEFL (maximum of either 300 for computer test of 677 for paper based)Each parameter bears same weight (yet) in the final formulae. Perfect score in each category is assumed to be 1.000, calculated as either percentile (if it exists) of the parameter or result of a value of the parameted divided by it's possible maximum value, ie:
9. TWE (maximum of 6.0)
1. for the GMAT total we may have 88%, which gives us: 0.880
2. for the age variable we may have 23, which gives us: 23/27=0.852
Consequently we will have 9 values ranged from 0.000 to 1.000, now we calculate their average.
However, taking the average as a final answer is way too easy and clearly wrong. We want to award consistently performing people and degrade those who luckily achieved some high scores (like the GMAT). Hence, we get average less standard deviation of the the data we used. This approach nicely degrades those who've had suspiciously "jumping" quantitative indicators. Finally we multiple the result we have by 1000, to make it in range from 0 to 1000 (the bigger is the better).
Some problems in my mind with the formulae so far:
Ideally, after collecting enough statistics in the main database we could find some neat coefficients, to adjust each and every case, thus giving somehow precise indication of a possible success in the application process. Therefore, we need as much as details submissions as possible! Act right now, submit yours and ask your friends to do so too! The value of the database might be really high!
Umm, I should have applied Wharton or MIT, well, it's never late. Wonder where did I apply? Check it out here.
Any comments will be appreciated via E-mail: Alex
Chudnovsky (although I prefer either English or Russian languages in
my correspondence you can use Spanish, German, French, Japanese, Hindu,
or any other language, but do not expect I will understand you!) :-/