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Running Head: MBA SCHOOLS IN ASIA-PACIFIC
Case Study: MBA Schools in Asia-Pacific
Name
Institutional Affiliation
Date
1
MBA SCHOOLS IN ASIA-PACIFIC
2
MBA Schools in Asia-Pacific
The pursuit of higher education has increased over the recent years because of the
increased need for expertise in the job market and the stiffer competition. Due to the increased
number of competent employees and limited vacancies in the job market, more people are
enrolling in a higher education such as a master in various fields. However, the number of people
particularly in Asia-Pacific that pursue a master’s in Business Administration is astounding. In
fact, most Asians are willing to take study leave or even quit their jobs to pursue the degree route
which entails rather tough theoretical courses. Following the surge and popularity of the course
in Asia, most of the University is offering the degree course commonly over the period of two
years. The primary purpose of this paper is to analyze and apply descriptive statistical techniques
in the reading of the data presented by various Universities in Asia who offer a Master’s in
Business Administration.
Analysis of Data
Type of Data
There are two types of data namely qualitative and quantitative which can either be
discrete or continuous. In this regard, most of the data presented by the report were quantitative.
Ten variable were outlined with three of the variables using qualitative data collection methods.
On the other hand, the rest were quantitative with some being discrete data and others continuous
data. These data collection methods were useful in portraying data in a manner that can be
analyzed and investigated. The following table is a simple replication of the classifications
mentioned. It shows the different variables and the types of data used alongside them.
Table 1.1 Showing type of data for each variable
Variables
Full-Time Enrollment
Students per Faculty
Local Tuition ($)
Foreign Tuition ($)
Age
%Foreign
GMAT
English Test
Work Experience
Starting Salary ($)
Type of Data
Quantitative
Quantitative
Quantitative
Quantitative
Quantitative
Quantitative
Qualitative
Qualitative
Qualitative
Quantitative
Discrete/Continuous
Discrete
Discrete
Discrete
Discrete
Discrete
Continuous
N/A
N/A
N/A
Discrete
Statistical Methods- Mean, Standard Deviation et al.
After addressing the different types of data used in collecting and recording the data,
various statistical techniques were employed for further investigation. The statistical techniques
used included were mean, standard deviation, the mode, the minimum and maximum for some of
the variables and the third quantile of the dataset. Out of the twenty-six schools, an average of
165 students enrolled for full-time enrollment with each faculty having at least eight students for
all the school. Conversely, the lowest number recorded for a full-time enrollment was 12
MBA SCHOOLS IN ASIA-PACIFIC
3
students, and the maximum number is 463 students. Indian Institute of Management (Calcutta)
has the highest number of full time enrollment, and the Macquarie Graduate School of
Management (Sydney) had the lowest number of full-time enrollment with the number being 12
as opposed to 463 or Calcutta. When it comes to age, the average age of the students in the
universities was 28 years old and minimum age and the lowest and highest ages being 22 and 37
respectively.
According to the data, students a majority of the students was 28 years with the youngest
student being 22 years old. For potential applicants who intend on pursuing an MBA in any of
the top international business schools, the numbers increase confidence. For instance, the
average number of students for each member of the faculty is eight which means that in the
professor can have a close interaction with such a number which enhances learning and teaching.
On the contrary, the highest number of students assigned to each member of the faculty was
found to be 19 which is also not too high meaning that the quality of education offered is
excellent. The following table summarizes the statistical investigation conducted based on the
variables.
Table 1.2 Showing the mean, median, min/max and standard deviation
Average/Mean
Standard
Deviation
Minimum
Maximum
Q3(Third Quartile)
Full-Time
Enrollment
Student
s per
Faculty
165.16
8.48
5.0589
9
2
19
13.5
140.841
12
463
270
Local
Tuition
($)
12,375
7778.42
1,000
33,060
17263.5
Foreign
Tuition
($)
16,582
9134.8
5
1,000
33,060
22650
Age
28.36
3.7846
2
22
37
31
%Foreign Startin
g
Salary
($)
28.08
25.0086
0
90
45
37,292
23459.
3
7,000
87,000
53,750
Analysis based on Foreign Student Population
However, the data also revealed that some school had 0% or 1% population of foreign
students. To prospective students who might not be from the native country, this percentage may
deter their willingness to enroll at the said school. Examples of school with no foreign students
include Indian Institute of Management (Ahmedabad), Indian Institute of Management
(Calcutta), and Jamnalal Bajaj Institute of Management Studies (Bombay). Schools with 1%
representation include Indian Institute of Management (Bangalore), and University Sains
Malaysia (Penang) which had a 1.5% representation of foreign student’s population. Similarly,
there are schools that have high percentages of foreign student population such as Asian Institute
of Management (Bangkok) which had the highest with 90%. Others include International
University of Japan (Niigata) with 60% and Australian National University (Canberra) with
80%. Two other schools had a 50% population of foreign students, and they are Asian Institute
of Management (Manila) and National University of Singapore. Schools with 0% or below 10%
population should enroll more foreign students to encourage diversity and cultural integration.
MBA SCHOOLS IN ASIA-PACIFIC
4
The report also showed that different schools had different requirements such that some
required completion of English tests. While some universities were specific about the GMAT test
others relied on alternative English competency tests such as TOEFL among others. Out of the
twenty-six universities, fifteen required the completion of GMAT tests as proof of competency in
written and spoken English. These schools showed a 57.69% while the percentage of the schools
that mandated the production of passedcompetence English tests for example TOEFL stood at
30.76%. Another requirement measured by the dataset was the requirement of previous work
experience with 73.07% requiring students to have previous work experience. From the data
collected, working experience seems to be a noteworthy factor in obtaining admission at most
schools.
Analysis based on Starting Salaries
Only seven schools out of 26 allowed students to get admitted without previous work
experience. The mean starting salary for schools that did not require working experience is
24,583 dollars. Eight of the 26 schools required English tests while sixteen did not cite English
tests as a requirement for admission. For schools that require English tests, the mean starting
salary was similar to those schools that did not require working experience with the figure being
24,583 dollars. For schools that do not require English tests, the mean starting salary was found
to be 34,816 dollars. These analyses show the difference between requirements and the impact
each has on the mean starting salary. The histogram shows the skewness of the starting salary.
Histogram
7
6
Frequency
5
4
3
Frequency
2
1
0
5000
10000 15000 20000 25000 30000 40000 50000 60000
More
Bin
From the histogram, it is evident that the histogram is skewed right. The graph does not
show a mirror image of itself which shows skewness. The mean of the starting salaries is near the
center towards the right side of the histogram while the median is also on the right on the
histogram. However, the mode is on the left as opposed to the values of the median and mean.
Using the mean and median value, the skewness of the histogram is right. Concerning the
empirical rule regarding the starting salaries data, the rule has been observed. The data forms a
MBA SCHOOLS IN ASIA-PACIFIC
5
normal distribution graph that shows three standard deviation ranges on the graph which is a
common characteristic of the empirical rule when subjected to the analysis of data.
Starting salaries for the various schools in the dataset was also measured with the average
starting salary being about 37,000 dollars. Jamnalal Bajaj Institute of Management Studies
(Bombay) had the lowest starting salary of 7,000 while International University of Japan
(Niigata) had the highest starting salary of 87,000. Based on the estimated starting salaries,
prospective students may prefer to gain admission in school with high starting salaries. However,
a school with higher starting salaries also had more restriction and more requirements. For
instance, International University of Japan (Niigata) needed both TOEFL and GMAT tests. Apart
from that disparity, other disparities highlighted by the survey was the tuition fee between local
and foreign students. The average tuition fee for local students was about 12,500 dollars
($12,375), and that of foreign students was about 16, 500 dollars ($16,582). Investigation of both
values shows a significant difference of about 4,000 dollars ($4,207).
Analysis based on Tuition Fees
Following the appearance of this significant difference, local students have an added
advantage of attending universities within their home region. Further analysis however, showed
that there is a school that did not have any different tuition fees such that both groups paid the
same amount of fees. These school include Indian Institute of Management (Ahmedabad),
Chinese University of Hong Kong, International University of Japan (Niigata), Nanyang
Technological University (Singapore), Hong Kong University of Science and Technology,
Chulalongkorn University (Bangkok), Asian Institute of Management (Bangkok), Jamnalal Bajaj
Institute of Management Studies (Bombay), and University Sains Malaysia (Penang). Foreign
students can take advantage of these schools if raising tuition fee is a concern. In conclusion, the
study helps in analyzing the data concerning the increased pursuance of MBA in Asia and the
Pacific. It gives insight when making decisions as well.
FullTime
Students Local
Foreign
Enrollm per
Tuition Tuition
Business School
ent
Faculty ($)
($)
Melbourne Business School
200
5
24,420
29,600
University of New South Wales (Sydney)
228
4
19,993
32,582
Indian Institute of Management (Ahmedabad)
392
5
4,300
4,300
Chinese University of Hong Kong
90
5
11,140
11,140
International University of Japan (Niigata)
126
4
33,060
33,060
Asian Institute of Management (Manila)
389
5
7,562
9,000
Indian Institute of Management (Bangalore)
380
5
3,935
16,000
National University of Singapore
147
6
6,146
7,170
Indian Institute of Management (Calcutta)
463
8
2,880
16,000
Australian National University (Canberra)
42
2
20,300
20,300
Nanyang Technological University (Singapore)
50
5
8,500
8,500
University of Queensland (Brisbane)
138
17
16,000
22,800
Hong Kong University of Science and Technology
60
2
11,513
11,513
Macquarie Graduate School of Management (Sydney)
12
8
17,172
19,778
Chulalongkorn University (Bangkok)
200
7
17,355
17,355
Monash Mt. Eliza Business School (Melbourne)
350
13
16,200
22,500
Asian Institute of Management (Bangkok)
300
10
18,200
18,200
University of Adelaide
20
19
16,426
23,100
Massey University (Palmerston North, New Zealand)
30
15
13,106
21,625
Royal Melbourne Institute of Technology
30
7
13,880
17,765
Jamnalal Bajaj Institute of Management Studies (Bombay)
240
9
1,000
1,000
Curtin Institute of Technology (Perth)
98
15
9,475
19,097
Lahore University of Management Sciences
70
14
11,250
26,300
Universiti Sains Malaysia (Penang)
30
5
2,260
2,260
De La Salle University (Manila)
44
17
3,300
3,600
Average/Mean
Standard Deviation
Minimum
Maximum
Q3
Median
Modal
165.16
140.841
12
463
270
8.48
5.05899
2
19
13.5
12,375
7778.42
1,000
33,060
17263.5
16,582
9134.85
1,000
33,060
22650
Age
28
29
22
29
28
25
23
29
23
30
32
32
26
34
25
30
29
30
37
32
24
29
23
32
28
28.36
3.78462
22
37
31
29
29
%Foreig
English
n
GMAT Test
47
Yes
No
28
Yes
No
0
No
No
10
Yes
No
60
Yes
Yes
50
Yes
No
1
Yes
No
51
Yes
Yes
0
No
No
80
Yes
Yes
20
Yes
No
26
No
No
37
Yes
No
27
No
No
6
Yes
No
30
Yes
Yes
90
No
Yes
10
No
No
35
No
Yes
30
No
Yes
0
No
No
43
Yes
No
2.5
No
No
15
No
Yes
3.5
Yes
No
28.08
25.0086
0
90
45
Work
Starting
Experien Salary
ce
($)
Yes
71,400
Yes
65,200
No
7,100
No
31,000
No
87,000
Yes
22,800
No
7,500
Yes
43,300
No
7,400
Yes
46,600
Yes
49,300
Yes
49,600
Yes
34,000
Yes
60,100
Yes
17,600
Yes
52,500
Yes
25,000
Yes
66,000
Yes
41,400
Yes
48,900
Yes
7,000
Yes
55,000
No
7,500
Yes
16,000
Yes
13,100
37,292
23459.3
7,000
87,000
53750
41,400
7500
No
Working
Experien
ce
Starting English Test
Salary
Starting
($)
Salary ($)
7,100
87,000
31,000
43,300
87,000
46,600
7,500
52,500
7,400
25,000
7,500
41,400
48,900
16,000
24,583
24,583
No English
Test
Starting
Salary ($)
71,400
65,200
7,100
31,000
22,800
7,500
43,300
7,400
46,600
49,300
49,600
34,000
60,100
17,600
66,000
7,000
55,000
7,500
13,100
34,816
Starting Salary
Data
Salary Bin
71,400
65,200
7,100
31,000
87,000
22,800
7,500
43,300
7,400
46,600
49,300
49,600
34,000
60,100
17,600
52,500
25,000
66,000
41,400
48,900
7,000
55,000
7,500
16,000
13,100
5000
10000
15000
20000
25000
30000
40000
50000
60000
…
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