ACT score distribution (2013) in charts

Introduction:

While working on our project, we find the need to visualize the ACT score distribution. ACT published the score distribution annually for National and for each state. What is missing is charts that help visualizing the distribution.

Below, we presented the National ACT score distributions for the class of 2013.Please note the math distribution has a very distinctive shape.

The main scores:

Figure 1 - ACT English Score Distribution (2013)

Figure 2 - ACT Math Score Distribution (2013)

Figure 3 - ACT Reading Score Distribution (2013)

Figure 4 - ACT Science Score Distribution (2013)

Figure 5 - ACT Composite Score Distribution (2013)

In addition to the main score, ACT also published distributions for sub-scores that constituted the main score.

Sub-Scores - ACT English Score Distribution (2013):

Figure 6 - English (Usage/Mechanics) Score Distribution (2013)

Figure 7 - English (Rhetorical Skills) Score Distribution (2013)

Sub-Scores - ACT Math Score Distribution (2013):

Figure 8 - Math (Pre-Algebra) Score Distribution (2013)

Figure 9 - Math (Algebra/Coordinate Geometry) Score Distribution (2013)

 

Figure 10 - Math (Plane Geometry/Trigonometry) Score Distribution (2013)

Sub-Scores - ACT Reading Score Distribution (2013):

Figure 11 - Reading (Social Science) Score Distribution (2013)

Figure 12 - Reading (Art/Literature) Score Distribution (2013)

Distance picked, peer institution selected, the nearest IPEDS

Other Peer Institution Selection Article

** If you are using Microsoft Internet Explorer or Google Chrom browser, you would not be able to read the formulas in this article. These formulas were written in MathML, a W3C standard, and can be viewed in FireFox.

Summary
The goal of this article is to provide mathematical insights into the selection of nearest peer institutions. The discussion begins with a general review of distance in mathematics and extended the idea to the measurement of nearness. Examples were used to demonstrate the importance of properly selected distance function.

The idea of peer
In many fields of study, it is a useful practice to tag objects that are similar to a particular object, the Anchor, as peers. The similarity can customarily be measured by the shortness of distance. The smaller the distance, the more similar an object is to the Anchor and the more likely an object to be selected as a peer of the Anchor.

Distances in Mathematics
In mathematics a metric or distance function is a function that defines the distance between two elements/objects (see Wikipedia article). However, as the abstract nature of the mathematics, the 'set theory' set criteria on behavior of the distance function but left the explicit definition of the distance to the case of application since the explicit definition is irrelevant in the content of the set theory.

For cases were the elements are Euclidean geometry points, the distances are commonly defined as:  $\sqrt{{(x_1-x_2)}^2+{(y_1-y_2)}^2}$

However, this is not the only permissible definition for distance.


Distance in a case of study
The lack of explicit definition of distance for a case of study call into question of the distance between the mathematics and the real world.

However, contrary to most believes, mathematics is not isolated in its own abstract world, plenty of mathematical branches grow out of real world problems. For example, the criteria for the distance function are abstracted qualities of the common distance definition of the Euclidean geometry.

The absent of the definition, in essence, offer the opportunity to select a sensible definition for the case of study.

In the land of peer selection, properties are constantly associated with objects and distances are customary defined through properties.

In the case of higher-education-institution objects, possible properties are fall enrollment headcounts, percent of male enrollment, total revenue, ... etc, like those collected by IPEDS ( Integrated Postsecondary Education Data System) survey.

Distance of Interest for this article
Of all the possible choice of distance definitions, the following are of demonstrative interest. The subscript i denoted various properties while the X denoted the Anchor object in the set and x a different object. The D denote the distance

1. Sum of the square of the difference
   $D=\sum _i{d}_i=\sum _i{(x_i-X_i)}^2$
2. Sum of the Ranking of difference
   $D=\sum _i{d}_i=\sum _i$ Ranking of $|x_i-X_i|$
   where the minimum value has a rank of 1 and the next smallest has a rank of 2 ... etc.
3. Sum of the square of the percent difference
   $D=\sum _i{d}_i=\sum _i{(\frac{x_i}{X_i}-1)}^2$
4. Sum of the absolute value of the percent difference
   $D=\sum _i{d}_i=\sum _i|\frac{x_i}{X_i}-1|$

Fabricated demonstrative data
For the sack of demonstration, higher education institution IPEDS like objects were considered. The Anchor institution, My Inst, alone with three other institutions and their fabricated property values were listed in Table 1. Comparisons of these institution were presented in Figure 1. Data are fabricated to demonstrate that a good methodology would not depend on data to produce reasonable result. With Figure 1, it is clearly shown that Inst-1 is the institution that most similar to My Inst, the Anchor institution, followed by Inst-2 and Inst-3.

Table 1 - demonstrative data

Inst	Enrollment	% Men	Revenue
Institution 1	1900	63%	105,000
Institution 2	2050	90%	101,000
Institution 3	1970	35%	104,000
My Institution	2000	60%	100,000

Figure 1 - comparing institutions (click to see the picture)

Distance evaluated with each illustrative definition

Table 2 - Sum of the square of the difference

Inst	Enrollment	Percent Men	Revenue	Distance
Inst 1	10000	0.0009	25,000,000	25,010,000.0
Inst 2	2500	0.09	1,000,000	1,002,500.1
Inst 3	900	0.0625	16,000,000	16,000,900.1

With the 'sum of the square of the difference' approach, the similarity ranking is in the order of Inst-2, Inst-3, and followed by Inst 1. Table 2 demonstrated that, in this model, the property having larger value would overshadow differences in other properties. It is, therefore, important to scale properties to a compatible matter.t


Table 3 - Sum of the Ranking of difference

Inst	Enrollment	Percent Men	Revenue	Distance
Inst 1	3	1	3	7
Inst 2	2	3	1	6
Inst 3	1	2	2	5

The 'Sum of the Ranking' practice considered the Inst-3 as the most similar peer with Inst-2 and Inst-1 following. The problem with this approach may not be obvious. Couple of points can be made, if observe carefully. First of all is the misrepresentation of the true differences with category like integers, which by itself can't even avoid rounding errors. Using ordering also create problem in that distances between adjacent values are been replaced by 1.

Table 4 - Sum of the square of the percent difference

Inst	Enrollment	% Men	Revenue	Distance
Inst 1	0.3%	0.3%	0.3%	0.8%
Inst 2	0.1%	25.0%	0.0%	25.1%
Inst 3	0.0%	17.4%	0.2%	17.5%

Inst-1 is ranked as the most likely followed by Inst-3 and Inst-2 in the 'Sum of the square of the percent difference' process. 

Under this approach, differences are represented by the percent difference from the Anchor with no categorization attempted. Another benefit of this approach is the straightforward approach and the ease of explanation to audiences. Weighting to each property, as will be discussed later, is also plain to see and easy to identify.

Lesson learned
Properties' value varied in magnitudes, invoking values directly undermined the difference in properties with smaller magnitude. The employ of ranking could over-shadow the difference in value and, in effect, assigned a difference of 1 for all adjacent values.

The square vs. the absolute value (definition 3 vs. definition 4) 
The fact that ${\left(\sum _i{A}_i\right)}^2\succ \sum _i{A_i}^2$ implied that the squared method would favor multiple smaller differences than a single larger difference while the absolute value method will weight small differences and single bigger difference equally. Visually, the squared method made sense.

Weighting properties
Once standardize to the 'sum of the square of the percent difference', weighting can easily be done by multiples to the 'square of the percent difference' before the sum.

* While customized distance can be used in Nearest Neighbor analysis, Nearest Neighbor represent a specific topic in the cluster analysis.

Doctor's degree by field of study, race, foreign, and gender

The field of choice for US doctor awardees are similar regard less of race. Non-resident alien accounted for almost 30% of the supply for higher education instructors!

Observing of doctoral degree conferred in the United States provided us a unique look into the US higher education system. It provided implications for our future higher education system. For one, the doctors are the main source of future college instructors, and secondly, they are future leaders of our higher education institutions. They are also the main research talent that will continue to move the country forward. Lawyers also play important roles in the social justice of our society, especially in protecting minorities' legal rights.

The CL education center has just released a set of data processing reports based on the IPEDS (Integrated Postsecondary Education Data System) 2011-12 completer survey - the analysis can be found under the section: United State's Doctor Production.

IPEDS survey classifies Doctors into 3 groups: The Research Doctors, the Professional Practice Doctors, and the Other Doctors. For non-legal, and non-medical field, the research doctors are the main source for college instructors. The professional practice doctors are mainly the lawyers, and the medical doctors, but also includes doctors that are mainly prepared for specialized career practices like ...

Total Doctor Degrees Awarded by Field of Study (CIP)

Figure 1, Doctors' field of study for each race (click to view the whole chart)

The major field/CIP of interested are list in Table 1 below.

Table 1, major field of interest for each race (multiple and unknown race not displayed)

CIP	Title	Native	AsianPI	Black	Hispanic	White	Alien
11	Computer and Info. Sci.	0.3%	0.8%	0.4%	0.2%	0.5%	4.2%
13	Education	8.6%	1.9%	15.9%	6.6%	5.8%	3.4%
14	Engineering	1.6%	3.7%	1.4%	2.0%	2.4%	24.8%
22	Legal Prof. and Studies	37.5%	21.2%	29.1%	38.8%	30.5%	5.7%
26	Bio and Biomedical Sci.	3.1%	4.1%	2.6%	3.7%	4.0%	11.2%
40	Physical Sciences	1.3%	1.5%	0.9%	1.5%	2.4%	11.3%
42	Psychology	3.5%	1.7%	3.9%	5.5%	4.0%	1.8%
45	Social Science	1.1%	1.0%	1.3%	1.5%	1.7%	5.9%
51	Health Professions related	36.3%	58.7%	32.3%	33.3%	39.4%	10.1%

Figure 1 and Table 1 revealed that, for US citizen, almost all races are favoring Legal, and Health Professions roughly equally, except Asian Pacific Islander are skewed more toward Health Professions. Beside the point made above, in general, all races exhibit similar field-of-study pattern. The major disparities in the chart come from the non-residence alien, where the dominated field is the engineering. Their domination on physical science and biology is also noticeable.


Total Doctor Degrees Awarded by Race
Table 2, total doctor degree awarded by race

Native	AsianPI	Black	Hispanic	White	Alien	Unknown	Mult.Rc
0.5%	9.5%	6.1%	5.5%	58.8%	11.5%	7.3%	0.7%

Total Research Doctors Awarded by Race
Table 3, total research doctors awarded by race

Native	AsianPI	Black	Hispanic	White	Alien	Unknown	Mult.Rc
0.4%	5.3%	6.3%	4.1%	49.2%	27.3%	6.8%	0.6%

The research doctor degree, which mainly removed the lawyers and medical doctors from the total doctor count, awarded to non-residence alien comprise a whooping 27% of all the research doctor degree awarded. Research doctors are the major supply of teachers for our higher education system.

Table 4, research doctors awarded by fields(CIP) and by race

CIP	Title	Native	AsianPI	Black	Hispanic	White	Alien	Unknown	Mult.Rc
1	Agriculture and Operation	0.7%	2.5%	1.3%	4.1%	34.5%	52.0%	4.5%	0.4%
3	Nature Resource and Conservation	0.4%	3.4%	2.5%	3.4%	56.7%	27.4%	5.6%	0.5%
4	Architecture	0.0%	17.8%	4.0%	3.5%	29.2%	37.1%	6.4%	2.0%
5	Area, Ethnic, Cultural, Gender Studies	2.2%	8.6%	11.9%	5.0%	44.2%	16.9%	10.4%	0.7%
9	Communication, Journalism	0.7%	2.6%	5.7%	2.1%	56.3%	25.3%	6.9%	0.3%
10	Communication Technologies	0.0%	0.0%	0.0%	0.0%	0.0%	100.0%	0.0%	0.0%
11	Computer and Info Sci.	0.2%	8.3%	2.0%	1.2%	33.0%	50.4%	4.7%	0.2%
12	Personal and Culinary Services
13	Education	0.8%	3.1%	16.6%	6.1%	58.3%	7.0%	7.5%	0.6%
14	Engineering	0.2%	7.0%	1.7%	2.2%	28.2%	56.2%	4.2%	0.4%
15	Engineering Technologies	0.0%	5.5%	1.8%	1.8%	49.1%	34.5%	7.3%	0.0%
16	Foreign Languages, Literatures	0.1%	3.6%	1.7%	6.6%	47.0%	32.4%	8.4%	0.3%
19	Family, Consumer Sci.	0.3%	3.4%	9.1%	2.2%	57.2%	25.3%	2.2%	0.3%
22	Legal Prof. and studies	0.0%	1.6%	0.5%	2.1%	11.0%	77.0%	7.9%	0.0%
23	English, Literature	0.4%	3.4%	4.0%	3.8%	70.8%	8.5%	7.9%	1.1%
24	Liberal Arts, Sci. General Studies	0.0%	4.5%	3.4%	1.1%	56.8%	12.5%	20.5%	1.1%
25	Library Sci.	0.0%	2.0%	2.0%	2.0%	48.0%	42.0%	4.0%	0.0%
26	Bio. Biomedical Sci.	0.3%	8.2%	3.3%	4.3%	49.8%	27.2%	6.3%	0.6%
27	Math. And Statistics	0.3%	5.7%	1.4%	1.8%	39.4%	46.5%	4.7%	0.1%
28	Military Sci. Leadership
29	Military Tech.	0.0%	0.0%	0.0%	0.0%	83.3%	16.7%	0.0%	0.0%
30	Interdisciplinary Studies	0.5%	5.3%	10.0%	4.8%	52.3%	20.2%	5.6%	1.4%
31	Recreation, Leisure Studies	0.4%	1.2%	7.0%	2.1%	62.8%	24.8%	1.2%	0.4%
32	Basic, Remedial Education
33	Citizenship Activities
34	Health Knowledge and Skill
35	Social Skills
36	Leisure, Recreational Activities
37	Self-Improvement
38	Philosophy and Religious Studies	0.1%	2.6%	5.4%	2.9%	62.5%	18.1%	7.5%	1.0%
39	Theology, Religious Vocations	0.0%	4.7%	10.8%	3.3%	61.0%	12.6%	7.3%	0.2%
40	Physical Sciences	0.2%	4.6%	1.7%	2.6%	43.7%	40.6%	6.2%	0.5%
41	Science Tech.	0.0%	33.3%	0.0%	0.0%	66.7%	0.0%	0.0%	0.0%
42	Psychology	0.6%	4.7%	6.7%	8.0%	63.1%	6.9%	9.0%	0.9%
43	Protective Services	0.0%	3.8%	11.5%	3.1%	63.4%	12.2%	5.3%	0.8%
44	Public Administration	0.4%	4.7%	15.1%	5.3%	47.2%	17.0%	9.7%	0.7%
45	Social Science	0.3%	4.5%	3.8%	3.9%	47.4%	32.2%	7.2%	0.7%
46	Construction Trades
47	Mechanic, Repair Tech.
48	Precision Production
49	Materials Transportation
50	Performing Arts	0.4%	6.1%	1.9%	3.5%	57.8%	22.1%	7.8%	0.5%
51	Health Professions	0.3%	5.9%	7.7%	3.3%	60.9%	13.3%	8.1%	0.5%
52	Business, Marketing	0.4%	5.2%	12.5%	4.4%	41.2%	26.0%	10.2%	0.2%
53	Secondary Diplomas
54	History	0.5%	2.6%	4.5%	4.5%	64.7%	12.3%	9.6%	1.2%
60	Residency Programs

Figure 4 further breaks down the research doctors by field of study (CIP) and by race. The engineering stand out. 56% of the engineering doctor degrees were awarded to non-resident alien. Other non-resident alien heavy fields include Agriculture(52%),
Architecture(37%), Computer Sci.(50%), Engineering Tech(35%), Foreign Languages(32%), Library Sci.(42%), Math. And Statistics(47%), Physical Sciences(41%), Social Science(32%). All of these fields displayed a non-resident alien rate that is higher than the 27.3%, the average rate held by non-resident alien.

Total Research Doctors Awarded by Gender
Men(51%) and women(49%) held roughly the same share of all the research doctor degrees awarded by US institutions. The lowest fields with women recipients are Military Tech(17%), Theology(19%), Computer(20%), and Engineering(22%).

Professional Practice Doctor Degrees Awarded by Gender
In this category, men(53%) held a slightly larger share in the Legal Profession while women(57%) hold larger share in the Medical Doctor field.

Salaries for Professor, Instructor and Graduate Assistant - an IPEDS derivation

** If you are using Microsoft Internet Explorer or Google Chrom browser, you would not be able to read the formulas in this article. These formulas were written in MathML, a W3C standard, and can be viewed in FireFox.

For the 2012-13 IPEDS (Integrated Postsecondary Education Data System) data collection year, National Center for Education Statistics (NCES) changed the information it collected through its Human Resource component. This change in data collection dictates how the average salary for Full-Time instructional Faculty can be calculated.

This article intended to provide a comparison of the new and the old way of calculating the average salary. The discussion is intentionally simplified in order to demonstrate the conceptual differences.

Prior to 2012-13 data collection, headcount numbers and salary outlays were collected for faculty with 9- or 10-month contract and 11- or 12-month contract for each gender and rank. So, for each gender and rank, there are basically 4 numbers: Total Salary Outlay for faculty with 9- or 10-month contract (S₉), Total Headcount for faculty with 9- or 10-month contract (H₉), Total Salary Outlay for faculty with 11- or 12-month contract (S₁₁), and Total Headcount for faculty with 11- or 12-month contract (H₁₁). The suggested way (by NCES) to calculate the annual 9-month average salary for each gender-rank combination is given by:

  $\frac{S_9+\frac{9}{11}{S}_{11}}{H_9+H_{11}}$

, which, in essence, is the average of the monthly salary times 9.

Beginning 2012-13 data collection year, for each gender-rank combination, 5 numbers are collected: the Total Headcount for faculty with 9-month contract (H₉), the Total Headcount for faculty with 10-month contract (H₁₀), the Total Headcount for faculty with 11-month contract (H₁₁), the Total Headcount for faculty with 12-month contract (H₁₂), and the Total Salary Outlay for faculty with all four contract length (S₉+S₁₀+S₁₁+S₁₂). The suggested way (by NCES) to calculate the annual 9-month average salary for each gender-rank combination is given by:

$\frac{S_9+S_{10}+S_{11}+S_{12}}{9H_9+10H_{10}+11H_{11}+12H_{12}}x9$

, which, in essence, is the total salary outlay distributed into the total number of manpower-month.

Logically, the methodology changes begged the explanation of the differences between these two methods.

For simplicity, case with only 9-month and 11-month faculties are considered. Under this condition, the 2012-13 method reduced to:

  $\frac{S_9+S_{11}}{9H_9+11H_{11}}x9$.

By carrying out the difference of the new and old methods, we arrived at:

$\frac{\frac{2}{11}{S}_{11}-\frac{2}{9}x\left(\frac{S_9+\frac{9}{11}{S}_{11}}{H_9+H_{11}}\right)x{H}_{11}}{9H_9+11H_{11}}x9$.

The difference indicates if the new number is higher or lower than the old number and by how much. The value represented by the parenthesis is that of the old method - the average monthly salary times 9 month. By multiplying it by two over nine and times the number of faculty with 11-month contract, the result represents the amount of money needed to bring the 11-month faculties' average salary to that of the old method for the two months (9-11). The leading term in the numerator indicates the two month allocation from the total salary outlay for the 11-month faculties. The net value of the numerator is, therefore, the amount of money that can be used the raise or lower the value of the new method apart from the average monthly salary of the old method. By solving the inequality equation:

$\frac{2}{11}{S}_{11}-\frac{2}{9}x\left(\frac{S_9+\frac{9}{11}{S}_{11}}{H_9+H_{11}}\right)x{H}_{11}>0$
$\mathrm{ }$
, it can be proofed that higher monthly salary for the 11-month faculties would result in higher value for the 2012-13 formula than the older formula and the reverse is also true.

By consideration above and by making the same assumption NCES had made in the past (i.e. assuming all faculty with 9- or 10-month contracts are actually 9-month contract and that all faculty with 11- or 12-month contract are actually 11-month contract), it is possible to apply the new method to the pre 2012-13 data with predictable discrepancy.

Even though NCES had used the 9- and 11-month assumption in the past, the new 2012-13 data can be used to gauge if that assumption is a valid one. For example, the 2012-13 data revealed that majority of Nebraska's colleges are either have 9-month contracts or 12-month contracts. There are some 10-month contracts, but the 11-month contracts are nearly none. With these observation, the following formulas is a better estimate for the pre 2012-13 data:
$\frac{S_9+\frac{9}{12}{S}_{12}}{H_9+H_{12}}$ 

At the same time, by discounting the minorities, the following formula can be applied to all years:
$\frac{S_9+S_{12}}{9H_9+12H_{12}}x9$

This would show the effects and differences caused by the new formula and also provide a ( reasonably ) compatible trend from the past to current.

On Obama’s Bold Plan to Reshape American Higher Education

Original Article

Summary goes here!

A step finally taken, which I have advocated for a long time.

Jest few points about things the author of the article like to see to be included in Obama's plan.

On 'Nobel Prize winner wants to get in the Physics 101 business' should get more weight:

• Personally, I don't think a Researcher is necessary a better teacher. They may have their own way of understanding things, it does not necessary mean those understanding are on all levels and that they can transfer those idea effectively especially to students that may have totally different mindsets.
• If there is going to have an objective measurements of outcome, why do we try to add some un-objective impurity to the formula? This is more like going back to the old system where the established (regional accredited) are carrying more weights.
• By providing this loophole to encourage high level researcher to teach low level courses, aren't we promoting inefficiency? I am not saying there would have no benefits to the students, but I am saying that this by-law is not needed. If they can provide extra benefits to lower level courses, those benefits should be build into the objective measurement formula and, therefore, it will show if the benefits is there and, would, therefore, render the by law unnecessary.

A song for the Ed community

Educate future generation and change the world!

 Gem like you, Gem like you, here is a song for you.

Give me a song, give me a song, give me is the name of the song.

Give me the word, give me the word, give me has taken the world.
Game boring girl, game boring girl, give me has taken them all.
Bragging boy, bragging boy, give me is their whole.

Boring game, bragging game, give me is all their game.
The give me will never end their game.

Gem like you, gem like you, may the give me never got you.
Gem like you, gem like you, may the ring never got you.

May the ring never replace you. May the ring never replace a gem like you.





- Of cause, there is a story behind it.