#	School	#Svys	Score
1	Dartmouth College	74	23.8 23.8
2	University of California -- Irvine	114	23.1 23.1
3	University of Missouri Columbia	54	22.7 22.7
4	Furman University	46	22.6 22.6
5	Vassar College	60	22.4 22.4
6	Kalamazoo College	47	22.4 22.4
7	Mount Holyoke College	65	22.3 22.3
8	Harvey Mudd College	40	22.1 22.1
9	Colgate University	102	22.1 22.1
10	Brown University	158	22.0 22.0
11	Bryn Mawr College	47	22.0 22.0
12	University of Virginia	105	22.0 22.0
13	American University	83	22.0 22.0
14	State University of New York College at Geneseo	39	22.0 22.0
15	Emory University	78	22.0 22.0
16	College of William and Mary	146	21.9 21.9
17	Johns Hopkins University	148	21.9 21.9
18	Washington University in St. Louis	86	21.8 21.8
19	University of Chicago	100	21.7 21.7
20	Villanova University	121	21.7 21.7
21	Texas A&M University at College Station	159	21.7 21.7
22	Milwaukee School of Engineering	66	21.6 21.6
23	University of Pennsylvania	123	21.5 21.5
24	Bucknell University	64	21.5 21.5
25	Rice University	79	21.5 21.5
26	Northwestern University	118	21.3 21.3
27	Texas Tech University	65	21.2 21.2
28	Tufts University	79	21.2 21.2
29	Massachusetts Institute of Technology	123	21.1 21.1
30	Brandeis University	76	21.1 21.1
31	Cornell University	174	21.0 21.0
32	Texas State University-San Marcos	61	21.0 21.0
33	Ohio Wesleyan University	119	20.9 20.9
34	Michigan State University	144	20.9 20.9
35	University of Notre Dame	74	20.9 20.9
36	Hamilton College	54	20.9 20.9
37	East Carolina University	47	20.9 20.9
38	Wake Forest University	68	20.8 20.8
39	Purdue University	113	20.8 20.8
40	United States Naval Academy	39	20.8 20.8
41	Simon's Rock College of Bard	44	20.7 20.7
42	Northeastern University	102	20.7 20.7
43	Indiana University Bloomington	83	20.7 20.7
44	Tulane University	100	20.7 20.7
45	Lehigh University	74	20.7 20.7
46	Princeton University	89	20.6 20.6
47	Franklin and Marshall College	42	20.6 20.6
48	Vanderbilt University	131	20.6 20.6
49	University of South Carolina Columbia	41	20.5 20.5
50	Southwestern Assemblies of God University	133	20.4 20.4

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)