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* Top Engineering Schools *

Careers in Aerospace Engineering: Aerospace Engineering Salary, Satisfaction, Unemployment, Aerospace Engineering Salary Trend
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8,112 surveys match
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From Surveys Matching: Nuclear Engineering Naval Engineering Mechanical Engineering Industrial Operations Engineering Engineering Department Electrical Engineering Computer Science Computer Engineering Civil Engineering Chemical Engineering Automotive Engineering Aerospace Engineering

#University #Svys
Rating
1
Webb Institute  
15
31.5 31.5
2
Harvey Mudd College  
37
31.4 31.4
3
Dartmouth College  
12
31.0 31.0
4
Bradley University  
11
30.0 30.0
5
United States Air Force Academy  
20
29.9 29.9
6
Brigham Young University Provo  
15
29.7 29.7
7
Mississippi State University  
37
29.6 29.6
8
Cornell University  
60
29.4 29.4
9
Villanova University  
23
29.3 29.3
10
Lehigh University  
40
29.3 29.3
11
Lafayette College  
14
29.3 29.3
12
Purdue University   
72
29.2 29.2
13
Bellevue University  
10
29.2 29.2
14
University of California Irvine  
38
29.0 29.0
15
Tufts University  
12
28.8 28.8
16
Massachusetts Institute of Technology  
70
28.7 28.7
17
Bucknell University  
26
28.6 28.6
18
The University of Texas Austin  
33
28.6 28.6
19
The University of Alabama  
16
28.6 28.6
20
University of Alabama Huntsville  
15
28.6 28.6
21
Duke University  
17
28.5 28.5
22
California State Polytechnic University Pomona  
23
28.5 28.5
23
Miami University Oxford  
11
28.5 28.5
24
University of Idaho  
14
28.5 28.5
25
Texas Tech University  
28
28.5 28.5
26
University of Washington Seattle Campus  
29
28.5 28.5
27
Cedarville University  
12
28.5 28.5
28
University of Notre Dame  
14
28.5 28.5
29
Rice University  
30
28.4 28.4
30
University of Missouri Columbia  
10
28.3 28.3
31
Texas A & M University College Station  
65
28.3 28.3
32
Missouri University of Science and Technology  
30
28.1 28.1
33
University of Pennsylvania  
13
28.1 28.1
34
Michigan Technological University  
51
28.1 28.1
35
California Institute of Technology  
10
27.9 27.9
36
Northwestern University  
30
27.9 27.9
37
Carnegie Mellon University  
40
27.7 27.7
38
Vanderbilt University  
23
27.7 27.7
39
Brown University  
17
27.6 27.6
40
Washington University in St Louis  
22
27.6 27.6
41
Worcester Polytechnic Institute  
55
27.6 27.6
42
Southern Methodist University  
12
27.6 27.6
43
South Dakota School of Mines and Technology  
18
27.6 27.6
44
University of Dayton  
27
27.5 27.5
45
Milwaukee School of Engineering  
64
27.5 27.5
46
Clemson University  
43
27.5 27.5
47
Western New England University  
17
27.5 27.5
48
Western Michigan University  
22
27.4 27.4
49
Florida International University  
16
27.4 27.4
50
University of Maryland College Park  
37
27.4 27.4

 

About
The Ranking of Engineering Schools 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

The rationale behind this ranking structure is as follows:

Educational Quality - (in program).  Educational Quality is a umbrella over many issues designed to capture (overall) the quality and depth of instruction at an institution. 

Academic Success is based upon understanding - While a student may be learning and exceptional amount from his or her program, upon graduation, his or her initial earning power (salary) and job quality will be largely dominated by grades. 
This metric is an estimation of how well the grades a student receives actually match with their competency in the material.  Some schools stake their reputation on their ability to spread out grades ('no grade inflation') and to make very qualified and competent students suffer later in life (earning power) for the sake of the school reputation.  This does students and families a great disservice, as they are making significant investments for both the education and reputation to assist them.  Instead, by spreading grades, the school is taking that investment and punishing the customer.

Creativity Encouragement - Engineering, particularly is noted for its lack of creativity.  Some people have noted that creativity has no place in engineering.  However, this is the exact opposite of being true.  Nowadays, more than ever, practical engineering itself is reduced to an overengineered process.  Unfortunately, many of the tasks taken by engineers, if lacking creativity can be reduced to simple programs.  The engineer is seen more as a 'translator' between the human ideas & management and the physical implementation.  Creativity however, makes the engineer more than just a tool, bringing value (salary/job reliability) to them immediately and further into the future. 

Usefulness of work - Being kept busy, repeating mundane application tasks does not guarantee that a student becomes grounded in both the techniques and theory to be a good engineer.  Rather, it can lead to student burnout, causing them to waste significant amounts of tuition moneys in a degree program that becomes unused.

Faculty Accessibility - Ultimately, faculty interaction is why students attend colleges and universities.  Otherwise, a student could easily view lectures on webcast or simply read the book.  Ideally the faculty provides access to more information, more resources, a network, and a link to the real world that the student can learn from and access.  If the faculty do not make themselves available, then they reduce the deeper educational capacity of the institution and hamstring the students — essentially making them walk up to and dive off of a diving board blindfolded with only the 'theoretical knowledge' that there is water in the pool, there are no alligators, or that the diving board even exists.

University Funding Use - This is how readily the students see that the university funding is being used for their benefit.  The immediate implications ones of facilities and resources that the students can use to grow beyond the classroom.  The long term implication is that if the University continues using its funding in a manner beneficial to students, then the value of the students' degrees, post graduation, will continue to rise. 

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)

 

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