A mind of STEM graduates

 

With a higher education panel, error was caught by STEM graduates, but the reputation  worth less to nothing.

Many things went by in life without consciously been recorded or memorized. With my mind in education and STEM, a recent event is worth noting.

An office draft that is to be published were routed internally and to external experts for review. A section of the document presented the following information:

State and Local Appropriation Per FTE student

Data Source: State Higher Education Executive Officers Association (SHEEO), State Higher Education Finance (SHEF) Report: FY2014

Cost of living adjustment (COLA)
Enrollment mix index(EMI)

(adjusted)  State and local appropriations per FTE student = ([Educational appropriations]/[FTE enrollment...])/[COLA/EMI]
...

The section, basically, describes how much money the state or local government spend on (per FTE, Full-Time Equivalent) student with adjusting factors of COLA and EMI.

When given the above information, for most or all reviewers, there isn't much to suspect the accuracy of the information - for one, the source is authoritative, for two, the formula involves more items/numbers than they want to see. Then, the third, there could be doubts if they could understand the methodology behind the calculation.These kinds of scene happened often. A report/publication often is a mash of information from other sources with limited originality. Lots of times, entities lack the talent and confidence in devising and defending their own methodologies.

STEM graduates, due to their training, confidences in handling complicated situation are high. It became more likely for them to take extra steps to try to seriously understand the limits and implications of a methodology. In other words, the ability of STEM graduates are peers to those that devised the methodology.

With their training, the first thing went through STEM graduates' mind is what [COLA/EMI] trying to show? From the syntax convention used, it seems that square brackets were used to denote items. However, due to their desire of unambiguity, they will either questioning the use of square brackets in '[COLA/EMI]' or questioning what COLA/EMI trying to show - as '/' can mean divide in mathematics but can mean something totally different in casual writings.


In order to answer the requisition mind, quick Googling revealed the info on EMI:
1. Integrated Postsecondary Education Data System (IPEDS) data were usedto develop a national average cost per fall FTE for each of the Carnegie
Classifications of institutions. This calculation used financial information from
FY 2011 and fall 2010 FTE data.
2. The proportion of each state’s FTE in each of the Carnegie Classifications was
calculated for fall 2010, and then multiplied by the national average cost per
FTE in FY 2011 for each respective classification. For each state, the products
for each Classification were summed, which yields the state’s enrollment mix
unit cost for the year.

With the mathematical training and the understanding of the purpose of the formula, it is clear that the EMI have to be divided by the '[Educational appropriation]'. At this point, the question is if the authoritative source made the mistakes? A STEM graduate knows what is right and what is wrong and would not afraid to challenge the authoritarians - after all, there is no authoritarians in the science - it is a matter of truth.

In this particular case, the source did it right. The question is then, what we did wrong and how should it be presented correctly. The turn of this event is that, as expected, among these specialists, all but one notice the error, none but two cares the voice of a STEM graduate.

If this is happened in the higher education, how about the general public? Do you really think our STEM push can be successful while, in most people's mind, STEM graduates do not worth the attention.

Methodology: Higher Education-Workforce Pipeline IPEDS CIP SOC crosswalk

Original Article

To Be Completed!

** 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.

This methodology is much improved over my previous methodology.

In essence, the number of graduates can potentially be hired for a program with CIP code C is:
$$\text{Potentially\_Hired}\left(C\right)=(G_c+\epsilon )\sum _{S_c}{O}_{s_c}\left[\frac{1}{\sum _{C_{S_c}}(G_{C_{S_c}}+\epsilon )}\right]$$
Where $G_x$ is the number of graduates for CIP x, $O_z$ is the number of job opening for a given SOC z, $S_x$ is the SOC S related to a CIP x, $C_z$ is the CIP C related to an SOC z, and $\epsilon$ is a very small number, which play important role when $G_x$ is zero.

Following are drafts and is to be ignored for Now!
Number of graduates for CIP c:

Number of job open for SOC s:
$O_s$
SOC related to a CIP c:
$S_c$
CIP related to an SOC s:
$C_s$
$G_{S_{C_s}}$
$\sum _{-1}^{\mathrm{+1}}$
$\sqrt{{(x_1-x_2)}^2+{(y_1-y_2)}^2}$

An exciting discovery ... Yeh !!

An exciting discovery!

I started this blog to provide my analysis to the Internet society and, especially, as my thanks to wonderful people who contribute contents freely to the Internet society. Every time I am on the Internet reading personal websites, blogs, or the Wikipedia, I appreciate their volunteer works. These individuals may not volunteer in any physical form, but their contribution to the human society is of no less. A while back, I thought about creating some kind of website that can recognize these wonderful people for their volunteer works. Unfortunately, I haven't have time to move forward on the project.

Now my discovery. The other day, I was surprised to find that one of my post is actually referenced by a book. I considered that a great honor and totally flattered. But I would like to attribute this to all the people who volunteered their time and made our life better and easier.

 

Evaluate Nebraska's Education-Workforce Pipeline - CIP SOC Crosswalk

The methodology employed by this article is a major improvement over the previous model in that suggested increase or decrease of award production will have no side effects on related fields!

With the P20 initiatives bobbling up all over the United States, the idea of education pipeline has extended from education to the workforce supply. The idea of linking higher education production to economic prosperity has found its way into legislatures.

The idea of aligning higher education production with the workforce demand isn't new. A crosswalk system that linking the field of study to appropriate occupation was last updated in March 2012 by National Center for Education Statistics (NCES) and the Bureau of Labor Statistics (BLS). The crosswalk, also known as CIP to SOC crosswalk, had been used in various workforce supply studies. However, because the complexity of the crosswalk, most of the studies limited their use to few focused fields of interest.

While the CIP to SOC crosswalk is a useful tool, limitations must be observed. The guiding principle behind the development summarized it well:

• “A CIP-SOC relationship must indicate a “direct” relationship, that is, programs in the CIP category are preparation directly for entry into and performance in jobs in the SOC category. The programs satisfy requirements for entry and/or prepare individuals to meet licensure or certification requirements to work in the occupation.”

An example can provide some clarity to the principle. In the nursing field, the registered nurse program (CIP 513801 Associate degree) is crosswalked to the registered nurse (RN) occupation (SOC 291141 Registered Nurse) and not the Licensed Practical Nurse (LPN) occupation (SOC 292061) even though the registered nurse can certainly worked in the LPN occupation. When interpret the result of this work, please keep this in mind, the result is based on 'appropriate' mapping and not the 'all possible mapping'. In the case of Registered Nurse, State of Nebraska can decide to produce more Registered Nurses to fill the LPN occupation. But that probably isn't the most efficient approach.

This article presents a general approach that is applied to all field of studies. The approach is applied to the state of Nebraska data set but can be easily applied to other states.

The data employed are the 2012-22 long term occupation projection data from Nebraska's Department of Labor and the 2013 degree awarded data from IPEDS' (Integrated Postsecondary Education Data System) completion survey.

While the methodology possessed some very desirable characteristics, like all researches, the data employed played vital role in the outcome. During the development, the job opening advertise data available from Nebraska Department of Labor's website were examined. The data for occupation like 'bus driver, School or Special Client' seems to be OK. The data for hot occupations like nurse or computer related positions post some challenges. For one, there were a lot of entries posted by staffing companies or recruiters and, by reading through some of the entries, some of them are simply phantom entries that mimic the real positions. For the nursing jobs, multi-leveled job listings are common. For example, a position can be advertized as RN/LPN, which begged the question of which category this position is counted under. With these observations, the result presented is based on Labor's projection data which can easily be replaced with more accurate data if such data is available.

The methodology employed by this article is different from that of our previous articles. This new approach eliminated couple of the mentioned limitations of previous methodology. Noticeably, when increase or reduce the number of graduates for a given field following the suggestion of this article would not interfere with the number suggested by this article for other fields. This is a major improvement over the previous model in which additional analysis is needed to make sure no side effect occurred in other related fields.

At the heart of this new methodology is the assumption of equal chance of employment, which means that all graduates that can be walked to an occupation, they all have the same chance of getting hired. If supported by real-life data, this factor can be modified to provide better result.

The top 10 education programs that could be targeted to produce more graduates are:

CIP	Title	Dgr Level	Job Open	UnFilled
490205	Truck and Bus Driver/Commercial Vehicle Operator and Instructor.	LessAssct	1,024	(953)
513999	Practical Nursing, Vocational Nursing and Nursing Assistants, Other.	LessAssct	468	(456)
513801	Registered Nursing/Registered Nurse.	Associate	702	(292)
520301	Accounting.	Bachelor	467	(156)
110101	Computer and Information Sciences, General.	Bachelor	259	(113)
520801	Finance, General.	Bachelor	300	(55)
460302	Electrician.	LessAssct	154	(96)
120401	Cosmetology/Cosmetologist, General.	LessAssct	126	(84)
110701	Computer Science.	Bachelor	121	(75)
480501	Machine Tool Technology/Machinist.	LessAssct	144	(65)
460303	Lineworker.	LessAssct	71	(56)

The top 10 eduction programs that could consider reducing graduates are:

CIP	Title	Dgr Level	Job Open	Over Supplied
540101	History, General.	Bachelor	22	163
513801	Registered Nursing/Registered Nurse.	Master	11	180
230101	English Language and Literature, General.	Bachelor	27	198
510912	Physician Assistant.	Master	43	245
450101	Social Sciences, General.	Bachelor	41	255
130301	Curriculum and Instruction.	Master	13	398
131202	Elementary Education and Teaching.	Bachelor	276	404
260101	Biology/Biological Sciences, General.	Bachelor	70	436
520201	Business Administration and Management, General.	Bachelor	368	588
520101	Business/Commerce, General.	Bachelor	386	618

Now Microsoft has spoken, Microsoft, Yahoo, and Google CEO...

Let truth speaks for itself...

Microsoft CEO Satya Nadella
Yahoo CEO Marissa Mayer
Google CEO Larry Page
How about Apple's Tim Cook?

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.

 

Summary of the IPEDS data release schedule

The Best Known Date for IPEDS data release!

IPEDS stand for Integrated Postsecondary Education Data System and is a set of survey conducted by the National Center for Education Statistics (NCES), a division of the United State Education Department(US ED).

All the IPEDS survey data can be obtained through the IPEDS data center. IPEDS survey data are published in various quality control levels. The most recent guide line are covered in the document: IPEDS Data Release Procedure. In essence, beginning with data submitted in the 2011-12 survey year, data will be released in 3 phases: preliminary, provisional and final, geared toward national, state and institutional level analysis. In tech terms, these are what they mean:

• Preliminary - Edited but unimputed for non-responding school
• Provisional - Fully Edited with Imputing for non-responding institutions
• Final - Includes Edited updates from PYR - were referred to as 'revised data'

However, to track down when each set of data were published, it is not an easy task. One source of this kind of information is the 'This Week in IPEDS' news letter/email. The archive for these news letters can be found at IPEDS news site. However, even with the news letter, sometimes it is still hard to pinpoint the date a certain data set is made available at the IPEDS data center.

The following info is compiled from the news letters mentioned above. The only thing can be said about the date is these are the 'best known date'. Procedures to derive these info are detailed in this document.

Survey Year 2010-11

Survey Year	Survey	Preliminary*	Provisional	Final
2010-11	IC	2/8/2011	9/20/2011	4/27/2012
2010-11	C	2/8/2011	9/20/2011	4/27/2012
2010-11	E12	2/8/2011	9/20/2011	4/27/2012
2010-11	HR	-	11/22/2011	Target ?/?/2013
2010-11	SFA	-	-	4/27/2012
2010-11	EF	-	-	-
2010-11	F	-	-	-
2010-11	GR	-	-	-

Survey Year 2011-12

Survey Year	Survey	Preliminary*	Provisional	Final
2011-12	IC	-	9/26/2012	Target ?/?/2013
2011-12	C	-	9/26/2012	Target ?/?/2013
2011-12	E12	-	9/26/2012	Target ?/?/2013
2011-12	HR	-	9/26/2012	Target ?/?/2013
2011-12	SFA	-	9/26/2012	Target ?/?/2013
2011-12	EF	10/9/2012	Target ?/?/2012	Target ?/?/2013
2011-12	F	10/9/2012	Target ?/?/2012	Target ?/?/2013
2011-12	GR	10/9/2012	Target ?/?/2012	Target ?/?/2013

* Preliminary data will not be accessible once the provisional data is made available.

IC -Institutional Characteristics
C - The Completion Survey
E12 - 12-month Enrollment Survey
HR - Human Resource Survey
SFA - Student Financial Aid Survey
EF - Fall Enrollment Survey
GR - The Graduation Rate Survey

* A personal note: It take about 5 months for preliminary release and about 7-11 months for provisional release.

Homeland: Immigration in America Refugees

Original Article
 

Keep that spirit up - not that the America is a Great country, but that how lucky we are and how to light the spirits of our youth and best our efforts.

My first reaction after watching the show is that this is why America is a Great country - the heart of American people.

It has been a long time, I totally appreciated the America Dream - You will get there as long as you work hard for it - the opportunity. Just imaging how these refugees have to go through to survive in America and compare with our kids that growing up in America. Shouldn't our kids appreciate how fortunate they are - as the old saying goes: Counting your blessings - my feel is that a lot of us have lost that and that big heart. A lot of us seems to forget how fortunate we are and what we should have done our best to advance the world.

Have we done our best? With tones of fortune after the World War II, have we not worked our best to educate our children to keep that spirits up? Or have we been blamed other countries for products and jobs we can do with our ancient education?

America is a Great country, don't let that fade away!

Please keep that spirit up - not that the America is a Great country, but that how lucky we are and how to light the spirits of our youth and best our efforts.

It’s time to drop the college-for-all crusade

Original Article

Summary goes here!

Personally, I would attribute some of these kind of  bad American policies to the generosity of American and some to the idea of 'Political Correctness'.

As a foreign born American, I am totally impressed by the generosity of American and, at times, puzzled and resisted. Growing up, I took nothing for granted even though I have supporting parents. I was educated, taught and self-studied, that begging is of no dignity and god only help people that help themselves. Personally, I have been summon all these under the 'responsibility'.

As to the 'Political Correctness', for one thing, I would like to remind the reader that OPPORTUNITIES is all it should be considered. Given the opportunity, in the sense of promote responsibility, it is up to the people to work hard to get what they want.

More practically, we should realized that not all people were born equal, intellectually. The best a society can hopeful is to have everyone do their best. To have people reach their best is to teach them being responsible. To teach responsibility, the operation of the system and the society must, by itself, promote responsibility. For example, to entitle to education as a right, students must demonstrate their wiliness to put in efforts in studying. For a normal people, this can simply be a requirement to reach certain testing scores. This is nothing new, what is new is the infusion of the idea of responsibility.

In theory, I am willing to support the idea of free education for all those who did their best. On the other hand, since resources are limited, we should support those who will benefit the society the most. With the full support of the society, I am visioning the building of a system where the freely educated scholars recognizing the support from and the responsibility to the society. Formula may need be sought to build a practical system, the idea is to promote responsibility as the core value of education.

For people reaching their best but were not able to be the best, this is where the society come in to benefit the constituent of the whole society.

Can Financial Aid Improve Student Success at Louisiana’s Community Colleges?

Original Article

Summary goes here!

An old saying: "Help people who helped themselves."

Financial Aid can help if students were matured enough to realize the value of the extra time they got and put it into study. If not, I don't see how the extra money can improve students' success. It may even distract them from the study to enjoy other things in life.

This is a good demonstration of why building the sense responsibility is the number one task in education. Any attempt to improve the education outcome without promoting students' sense of responsibility is a wrong approach. You can hire personal coaches that following student around to improve his/her education. But does the society have the responsibility to support people this way? Will this ever been sustainable?

The histories of a lot of Federal Aid programs have shown that a lot of people will take advantages of these systems in undesirable ways and a lot of these programs were later re-tweaked or added responsibility strings to it.

US College Graduation Rates by Race by State - 2010 IPEDS

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Data release: College Graduation Rates by State for 2008-09 graduates

Race in United States is a much debated topic. Tying race to discrimination is a sequel of historical events. The subsistence of the racism is continually disputed. The data revealed here is too limited to settle the disagreement. But we do hope the data can pointed to the weak spot in our education system and, hopefully, solutions with emphasis on personal obligation can be developed. In author's opinion, some of today's approach in improving minorities' education attainment is overreaching and is themselves racial biased and undermined the important principle of personal responsibility. Services should be made available to all regardless of race. It takes responsible person to seek helps. Which race group actually uses these services is not a racial issue.

Opinion aside, objective view of the data is definitely in order.

This analysis is based on the data released earlier by the CL Higher Education Center. The analysis dis-regards the non-residence alien. The US total also excludes US minor islands and territories.

The College Graduation Rates by Race for the US states are shown in table 1 while the table 2 shows the same data with the inclusion of US minor islands and territories with differences highlighted. The difference for Asian are mainly caused by the inclusion of the Guam and the Northern Mariana Islands. The changes for Hispanic are the effect of including the Puerto Rico. The rest of the analysis would focus on the US states and Washington DC. Changes from last year's number are included in the parenthesis.

Compare with last year, the public-four-year, public-two-year and the private-not-for-profit-four-year sectors are seeing improvements. Basically, the graduation rate for all races in these sectors are either improved or stay the same. Viewing the data with respect to race, White-non-hispanic improved in all sectors while minorities showing mixed result.

Table 1 - US states - including DC

Sector	Native	Asian	Black	Hispanic	White	Total
Public 4Yr	35%	65%	36%	(+1)44%	57%	(+1)54%
NProfit 4Yr	(+3)49%	(+1)76%	44%	(+2)59%	67%	(+1)65%
Profit 4Yr	(-3)38%	(+4)46%	31%	(-2)41%	(+5)45%	(+3)41%
Public 2Yr	17%	(+1)26%	12%	16%	23%	20%
NProfit 2Yr	(-8)15%	51%	(-2)44%	(-1)46%	(+1)56%	(-1)50%
Profit 2Yr	(+1)59%	(-3)69%	(+2)49%	(+2)65%	(+2)65%	(+1)60%
Overall	32%	57%	32%	40%	50%	47%

Table 2 - With US minor islands and territories**

Sector	Native	Asian	Black	Hispanic	White	Total
Public 4Yr	35%	64%	36%	43%	57%	53%
NProfit 4Yr	(+3)49%	(+1)76%	44%	(+2)45%	67%	63%
Profit 4Yr	(-3)38%	(+4)46%	31%	(-2)39%	(+5)45%	(+2)40%
Public 2Yr	17%	25%	12%	(+1)17%	23%	20%
NProfit 2Yr	(-8)15%	51%	(-2)44%	(-1)62%	(+1)56%	(-2)53%
Profit 2Yr	(+1)59%	(-3)69%	(+2)49%	(+3)64%	(+2)65%	(+1)60%
Overall	32%	56%	32%	40%	50%	46%

Fig. 1 shows the US average graduation rates by race by sector. In general, White and Asian stand out in the public-four-year, public-two-year and private-not-for-profit-tour-year institutions. In other sectors, White and Asian college graduation rates are more comparable to other races. Overall, the public two year sector shows the lowest college graduation rates for all races (except the Natives). Within each sector, the Black usually exhibit the lowest rate except at the public four year sector, where the Native American show the lowest college graduation rate. It is also interesting to see that the private not-for-profit sectors have higher college graduation rates than their public counter parts.

Fig. 1 - US average graduation rates for each sector by race

Figure 2. present the same information as in Figure 1 except grouped by each race. Within all races, the private for-profit two year sector demonstrates high rates, only within White and Asian it seconds to the private not-for-profit four year sector. It is also remarkable to see how similar the rates within each race relative to each other - public-two-years are, in general the lowest. The private-not-for-profit-four-year and the private-for-profit-two-year are usually have the highest rate.

Fig. 2 - US average college graduation rates for each race by sector


== The following have not been updated to 2010 data yet =======
Figure 3 is an un-traditional display of a set of histogram for Asian. Each histogram/curve show the number of states at each graduation rate for a given sector. For example, there are 12 states that have college graduation rates for Asian greater than 55% and less than 60% for the public four year sector/curve while there are 9 states have college graduation rates greater than 30% and less than 35% for the private for-profit four year sector/curve.

Figure 3 demonstrates clearly that, for Asian, the average college graduation rates for each sector are representative except for the not-for-profit two year sector, where most of the states have a rate of 0% and rates are spread out without some kind of concentration or locality.

Fig. 3 - College graduation rate for Asian by sector


Figure 4 displayed similar information as in Figure 3 except it is for the Black. Even though the US average college graduation rate for the private not-for-profit four year sector is comparable to other sectors for Black, figure 4 shows that the rate varies a lot in regard to different states.

Fig. 4 - College graduation rate for Black by sector


Figure 5, 6 and 7 illustrate the same kind of information for Hispanic, Native American and White.

Fig. 5 - College graduation rate for Hispanic by sector


Fig. 6 - College graduation rate for Native American by sector


Fig. 7 - College graduation rate for White by sector


Figure 8 modeled after previous figures to display the college graduations rates for each race in the public four your sector. This chart clearly shows that the White and Asian are comparable except that there are some states where Asian exhibits higher rates.

Fig. 8 - College graduation rates for public four year sector by race


Figure 9 to 13 show the similar information as in Figure 8 for each sector.

Fig. 9 - College graduation rates for private not-for-profit four year sector by race


Fig. 10 - College graduation rates for private for-profit four year sector by race


Figure 11 is an very interested one. For one, this is the only one that all races are having good localities. Second of all, the White seems to be the one that is doing better even though the US average college going rates for public two year sector clearly show that the Asian is doing better than the White. The mystery is solved when dig deeper into the data. The data show that the high average college graduation rate for Asian in the public two year sector is caused by a single state with high Asian public two year sector enrollment, the California, which has a college graduation rate of 36% for Asian. This means that even though on average, Asian is doing good in public two year sector, it is not a Nation wide fact. It is also worth to note that the only state with meaningful high college graduation rates for the Native American is Wisconsin which enrolled 199 students. The other two states only enroll a total of 7 Native Americans. The other high rate state worth noting is the South Dakota, which post a rate of 64% for White.

Fig. 11 - College graduation rates for public two year sector by race


Figure 12 demonstrates that value of this kind of charts, where the meaning of average college graduation rates for private not-for-profit two year sector have very limited value.

Fig. 12 - College graduation rates for private not-for-profit two year sector by race


Fig. 13 - College graduation rates for private for-profit two year sector by race