#	School	#Svys	Score
1	Columbia Southern University	87	23.2 23.2
2	Brown University	203	23.2 23.2
3	University of California -- Irvine	153	23.1 23.1
4	Colgate University	164	23.0 23.0
5	Dartmouth College	100	22.6 22.6
6	Rice University	90	22.5 22.5
7	University of West Florida	80	22.5 22.5
8	Emory University	95	22.4 22.4
9	College of the Holy Cross	83	22.3 22.3
10	Knox College	95	22.3 22.3
11	Duke University	88	22.3 22.3
12	Marist College	198	22.2 22.2
13	Vassar College	76	22.1 22.1
14	Yale University	105	22.1 22.1
15	University of Chicago	138	22.0 22.0
16	University of Notre Dame	97	21.9 21.9
17	Tufts University	93	21.8 21.8
18	University of Pennsylvania	151	21.8 21.8
19	Saint Joseph's University	93	21.8 21.8
20	Mount Holyoke College	95	21.7 21.7
21	Cornell University	214	21.7 21.7
22	Washington University in St. Louis	110	21.7 21.7
23	Milwaukee School of Engineering	89	21.7 21.7
24	Bucknell University	87	21.6 21.6
25	University of Virginia	144	21.6 21.6
26	American University	121	21.5 21.5
27	Villanova University	160	21.5 21.5
28	Massachusetts Institute of Technology	146	21.5 21.5
29	Wake Forest University	92	21.5 21.5
30	University of Missouri Columbia	77	21.5 21.5
31	Hamilton College	74	21.5 21.5
32	College of William and Mary	179	21.4 21.4
33	Lehigh University	116	21.4 21.4
34	Johns Hopkins University	185	21.3 21.3
35	Purdue University	147	21.3 21.3
36	Texas Tech University	123	21.2 21.2
37	Texas A&M University at College Station	225	21.1 21.1
38	University of Rochester	143	21.1 21.1
39	Syracuse University	173	21.0 21.0
40	Northwestern University	151	21.0 21.0
41	Stanford University	87	21.0 21.0
42	University of Oregon	99	20.9 20.9
43	Christopher Newport University	163	20.9 20.9
44	Indiana University Bloomington	135	20.9 20.9
45	Brandeis University	97	20.9 20.9
46	Michigan State University	207	20.8 20.8
47	Princeton University	108	20.8 20.8
48	University of Wisconsin -- Madison	172	20.8 20.8
49	Ohio Wesleyan University	159	20.7 20.7
50	Texas State University-San Marcos	91	20.7 20.7

About The Ranking of Top 50 universities is made up as follows: 50% - Educational Quality 25% = Academic Success is based upon 'understanding' 12% - Creativity Encouragement 5% - Work is Useful 4% = Faculty Accessibility 4% - University Funding Use Filtering     Student Surveys are filtered of duplicate and “invalid” surveys prior to ranking.  Invalid surveys are those that are not self-consistent, reflecting a corrupting effect on the data, either accidental or with intent.  We have found that certain inclined students survey their “competing” schools, giving artificially bad (or good of their own school) reviews.  While we do not wish to point any fingers, we have been able to link up several groupings of falsified data with admissions staff at some universities.      5,000 valid surveys were analyzed statistically, and a gaussian matrix was created to model the survey patterns within and between surveys.  We can now identify those surveys that: vary too little, vary too much, have fields that do not covary properly, or are inconsistent.  (i.e.  rating the university as an A for friendliness, but then complaining either about the people or the social life).  In addition, a rule-base system was created to identify duplicates and model trends of surveys from the same machine.  This allows us to be able to identify if a person is falsifying many surveys.  FFT analysis is employed to determine the “data content” of each survey as well, providing more information for modeling.      The resulting filter, correlation matrix, and survey model is applied uniformly to all surveys.  Out of 7,500 undergraduate student surveys, 483 surveys were rendered invalid.  Inspection of the invalid surveys revealed a failure rate of 5%.  (24 of the 483 surveys were actually “good",2.5). How is rank computed?     The generic quick answer is that it is the average of student opinion ratings minus “variability of score”.  The “variability of score” is larger for low numbers of surveys, meaning that that school's ranking position is less trustably high or low.  Strict statistical variance is not instructive here because 'variance' is computed within a group of surveys — with only 1 survey, there is no variance. The 'Variability' function decreases exponentially with the size of the sample set, applied equally to all institutions, making it an acceptably fair accounting form.  After 5 surveys, the variability of score drops to less than .3; after 10 surveys, it is less than .1.  After 20 surveys, there no significant variability in position.  Essentially, each school's score converges to a position as the number of surveys increases. More specifically, Rank is computed by multiplying the importance of each variable selected by that variable and adding together.  The average of all matching surveys for a particular school is then taken.  From this, a 'variability' is computed — this is based upon the number of surveys.  If there is only 1 survey, and it ranks a school at a 10, then 1 more survey could come in, ranking a '0', which would give the school average a 5 (10/(1+1) = 5).  This is the lowest that the school 'could' be — given 1 more survey.  So this 'variability' is subtracted from the overall score, reducing it.  In this manner, schools that have more surveys have a more believable average than school with only 1 survey.  Actual Equation: score = average(importances[]*preferences[]) - (10*(sum(importances[])))/(#svys + 1)