#	School	#Svys	Score
1	University of California -- Irvine	144	22.7 22.7
2	Marist College	177	22.2 22.2
3	University of Chicago	128	22.0 22.0
4	Brown University	196	22.0 22.0
5	Milwaukee School of Engineering	86	21.9 21.9
6	University of Missouri Columbia	71	21.6 21.6
7	College of William and Mary	158	21.5 21.5
8	Dartmouth College	96	21.5 21.5
9	Lehigh University	103	21.4 21.4
10	Hamilton College	69	21.3 21.3
11	Tufts University	87	21.3 21.3
12	American University	111	21.3 21.3
13	Vassar College	71	21.3 21.3
14	Washington University in St. Louis	100	21.3 21.3
15	Texas Tech University	109	21.3 21.3
16	University of Pennsylvania	144	21.1 21.1
17	Christopher Newport University	136	21.1 21.1
18	University of Oregon	85	21.0 21.0
19	Massachusetts Institute of Technology	144	21.0 21.0
20	University of Virginia	136	20.8 20.8
21	Villanova University	144	20.7 20.7
22	Mount Holyoke College	90	20.7 20.7
23	Colgate University	146	20.7 20.7
24	Rice University	88	20.7 20.7
25	Bucknell University	82	20.7 20.7
26	Brandeis University	88	20.6 20.6
27	Johns Hopkins University	178	20.5 20.5
28	Michigan State University	190	20.5 20.5
29	Clemson University	114	20.5 20.5
30	University of Notre Dame	95	20.5 20.5
31	University of Rochester	126	20.4 20.4
32	Vanderbilt University	146	20.3 20.3
33	Cornell University	211	20.3 20.3
34	Wesleyan University	76	20.3 20.3
35	University of West Florida	66	20.2 20.2
36	Wake Forest University	84	20.2 20.2
37	Syracuse University	153	20.1 20.1
38	Southwestern Assemblies of God University	135	20.1 20.1
39	Tulane University	112	20.0 20.0
40	University of Colorado -- Denver	69	20.0 20.0
41	Texas A&M University at College Station	191	20.0 20.0
42	Saint Joseph's University	87	19.9 19.9
43	University of Dayton	66	19.9 19.9
44	Rensselaer Polytechnic Institute	148	19.8 19.8
45	Drew University	121	19.8 19.8
46	Carnegie Mellon University	97	19.7 19.7
47	Emory University	99	19.7 19.7
48	Purdue University	144	19.7 19.7
49	Northeastern University	153	19.6 19.6
50	Princeton University	112	19.6 19.6

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)