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Level Of Satisfaction With MBA Program And Starting Salary

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Write a Report on the Level of Satisfaction with MBA Program and Starting Salary.



In the ever-changing global arena, education plays a vital role in developing human resources in the workplace. Pursuit of higher education is attractive to many individuals especially where it leads to high salary. This is because organizations have a propensity to prefer highly trained employees in order to enhance their human resource portfolio and have a competitive edge over other organizations. According to the Economist (2014), salaries of MBA holders have recently been a center of attention for the media. Rydzewski and Eastman note that the major attributes of selecting an online MBA program for post graduate students is certification of the program, flexibility and quality services. When making a choice of the university to enroll in, applicants want to be certain that the school is accredited by the relevant authorities; the study hours can fit into their schedules and that the services offered are worthwhile in terms of quality and time. They postulated that the cost and the type of program offered are vital characteristics and demographics, gender, income level and age contribute to choice of an MBA program.

According to Fortuna (n.d), an MBA an acronym of masters of business administration is a “post-graduate degree offered at many universities around the world. Available in universities both small and prestigious, MBAs are often achieved in around five to seven total years of college and have been offered since the degree was first introduced at Dartmouth in 1900.”

Individuals are offered a chance to study while still in employment through studying part-time courses. Most MBA programs are offered on part-time basis to cater for employed individuals who would like to advance their careers in an effort to gain promotions in their workplace and or look for better paying jobs. Most employers support MBA programs for their employees by funding the programs.


Jones (2003) contends that Master of Business Administration (MBA) provides an opportunity for students to work and at the same time study. This kind of MBA program is preferred by most universities and has attracted many working class individuals to enroll in MBA programs. According to Jones, the average period taken to complete an MBA for part time students is less than three years. He further concludes that is one of the major reasons why employers offer scholarships to MBA students due to the flexibility of time required to be in school does not affect working hours. Some MBA programs are tailor made for corporate executives by arranging the lessons after working hours and weekends. He further concluded that in the United Kingdom between 10,000 and 15,000 individuals enroll in MBA e-learning programs annually. According to Enwistle (2003), the motivation of students plays a key role on how they eventually perform in their studies. He suggested that there is a correlation between the satisfaction level of students and their what they expect to achieve after their studies. The expectation of students has various elements in determining the relationship between motivation and expectations. This elements are broadly divided into inherent and external motivational factors.  

Jensen (2010) inferred that there is variation between remuneration for students who have studied in a private school and those who have studied in a public school in the US. Green et al. (2011) found similar results in his study in the United Kingdom. Several studies on the impact of salary packages to be associated with pursuit of higher learning. Walker and Zhu (2011) investigated the association between the variation of salaries based on the discipline in learning and also the final grading of undergraduate studies. They also sought to determine salary scale of an individual was associated with the propensity to seek postgraduate studies.

Arbaugh (2010) investigated the departmental attributes and demeanors in forty-six MBAcourses carried out in a two-year duration. His results revealed that the prescribed lecturer activities indicated in most online courses as teaching aids, the unofficial activities based on propinquity demeanors had a positive correlation with students perception of learning and their level of satisfaction with the learning system. The study also established that the frequency of lecturers to login to their school accounts and the amount of time spent per login period had a negative correlation with perception of students to learn. He concluded that there is need for online lecturers to systematically structure and categorize the courses earlier so that they can effectively and efficiently cater for the students while in an online learning session.

Endres, Chowdhury, Frye, and Hurtubis (2009) identified course curriculum as a feature of making a choice of various MBA programs. They attributed this phenomena as the fact that individuals want to feel that they are satisfied in their pursuit of intended goals. They suggested that level of satisfaction with course curriculum is based on the program rigidity, equality, and communications with the instructor or fellow students. The characteristics associated with level of satisfaction students derive from an MBA program are technological quality, positive attitude of the instructors who are consistently engaged in students’ progress. These characteristics positively motivate students to take up online MBA courses that fit into their needs and time (Endres et al., 2009). This report therefore sought to investigate the various aspects associated with the claims that enrolling in an MBA program is commensurate with earning a bigger salary. This paper was based on data collected from a survey of students from class of 2012 of The University of Western Ontario. Various tests were carried out based on the concerns raised by Marie Daer, an aspiring MBA applicant.

Association between Age and Starting Salary

It has been held that the age of an individual is likely to impact on the starting salary upon employment. This report sought to investigate whether age was likely to have any association with the remuneration package individuals based on the sampled data and if so was it significant to make inference to the population. This was done by determining whether there was a linear relationship between age and starting salary. If at all there existed a linear relationship was the strength of the relationship strong enough? The variables used for this test were age as the independent variable (IV) and starting salary as the dependent variable (DV). Statistical Package for Social Sciences (SPSS) was used to carry out the statistical tests throughout this report.

 Table 1: Descriptive Statistics of Age and Starting Salary

According to table 1 above the mean starting salary was 39,025.69 associated with a standard deviation of 50,951.56. The Average age of the students of the class of 2012 was 27.36 years. The correlation matrix as shown in table 2 below revealed a negative weak correlation (-0.063, n = 274) between age and starting salary. This meant that the older an individual the less the starting salary.

Table 2: Correlation Matrix of the Association between Age and Starting Salary

The coefficient of determination to determine the degree of association between the IV and the DV was very weak at .004 (0.4%) meaning there was very little relationship or change in starting salary that can be explained by an individual’s age as shown in table 3 below.

Table 3: Coefficient of Determination of Age and Starting Salary

Table 4 below shows an F statistic at one degree of freedom that was associated with a p value of p = 0.302. The hypothesis decision was to fail to reject the null hypothesis and concluded that there was statistical evidence to support the claim that age was associated with starting salary.


Association between Gender and Salary

The researcher sought to determine whether gender was likely to impact on an individual’s starting salary. To establish this t test was used. This kind of statistical test was used since the independent variable (IV) age was a category type of measurement with only two groups while the dependent variable (DV) starting salary was a continuous type of measurement. However, various assumptions were made namely that the sample was normally distributed, the sample was randomly selected from the population, there were no outliers and that variations were roughly equal across the two groups that is male and female. The bivariate descriptive statistics of starting salary between males and females indicated that on average females had a higher starting salary of 45,121.07 compared with males mean salary of 37,013.62 as shown in table 4 below. The question therefore was whether this variation was brought about by a statistical error of chance or was significant enough to form a basis of conclusion that starting salary differs based on gender. The null hypothesis therefore was that there was no difference in starting salary based on gender.

Table 4: Bivariate Analysis of Starting Salary between Genders

According to the results of the t-test the p value of the Levene’s test for equality of variances was 0.619 which was more than .05. This meant that that we had not violated the assumption of homogeneity of variances as shown in table 5 below. Findings of the test revealed a test statistic of -1.138 at 272 degrees of freedom that was associated with a p value of 0.256. The hypothesis decision therefore was to fail to reject the null hypothesis. In this regard, the conclusion made is that there was no statistical evidence to infer that starting salary had any relationship with gender (t = -1.138, df = 272, p = 0.256).

Table 5: Independent Sample T test for Gender and Starting Salary

Whether total GMAT is Associated with overall GMAT percentile (marks)

There was a concern from the applicant to determine whether GMAT score made a difference in the final marks in the school. To determine this, the variables used were the total GMAT and the overall GMAT percentile. The test statistic appropriate for this was the paired sample t test.  Table 6 below revealed that the sampled participants had an average total GMAT score of 619.45 (standard deviation= 57.54) while the overall GMAT percentile was 84.2 (standard deviation= 14.02, n= 274). The concern however, was to determine whether the differences in the means between the two variables were associated. Put another way, was there any reason to suggest the total GMAT had an effect on the final marks (overall GMAT percentile) achieved by the students? The null hypothesis was that there were no differences between the means of total GMAT score and overall GMAT percentile.

Table 6: Descriptive Statistics of Total GMAT and Overall Percentile

The results revealed a strong positive correlation of .848 (84.8%) between total GMAT score and overall GMAT percentile as shown in table 7 below. The question was whether the association between total GMAT and overall GMAT percentile obtained from the sample data was significant to make inference to the entire population.

                                                                 Table 7: Correlation Matrix for total GMAT Score and Overall GMAT Percentile

The findings revealed a test statistic of 191.56 at 273 degrees that was associated with a p value of p < 0.01. The hypothesis decision was therefore to reject the null hypothesis and thereby conclude that there was in fact evidence to suggest that total GMAT was associated with the overall GMAT percentile as shown in table 8 below.

Table 8: Paired Sample T Test between total GMAT Score and Overall GMAT Percentile

Whether Students Liked the MBA Program

To determine whether past students in the school liked the MBA program offered, the degree of satisfaction variable was used. This was a ranked scale of one through ten where one represented low satisfaction and ten represented the highest satisfaction.  However, those participants who did not answer the survey and those who answered but did not disclose salary data were excluded from the analysis since such data would have given unreliable results. The data was first analyzed descriptively by determining the mean and standard deviation as well as the minimum and maximum scores as shown in table 9 below.

Table 9: Descripive Statistics of Degree of Satisfaction with MBA Program

According to the results above, the maximum score on the degree of satisfaction with MBA program from the sample was seven while the minimum was one. The average score was 5.57 associated with a standard deviation of 0.98. This initial analysis revealed that based on the sampled data, the level of satisfaction with the MBA program was slightly above average. However, it was important to determine whether such a score could be expected in the entire population. In this regard, it was hypothesized that the mean score on level of satisfaction with the MBA program is not different from that of the population. A one sample t test was used to perform the analysis.

Table 10: One Sample T test for degree of Satisfaction with MBA Program

The one sample t test on degree of satisfaction with MBA program revealed a test statistic of 85.76 at 227 degrees of freedom that was associated a p value of p< 01. The test value was set at zero since under the null hypothesis, there was expected to be no difference between the observed mean and the mean of the population. Since the p value was less than .01 the decision was to reject the null hypothesis and conclude that there was evidence to suggest that the mean degree of satisfaction was different from that of the sample. The confidence interval also revealed that we can be 95% confident that the degree of satisfaction with MBA program will fall between 5.44 and 5.69 based on our sample of 221.


Relationship between Language and GMAT

There was concern as to whether an individual whose first language was not English meant that his or her GMAT score could be low compared to those whose first language was English. To test this variation, first language variable as the independent variable (IV) and total GMAT score as the dependent variable (DV) were used using the independent sample t test.

Table 11: Descriptive Statistics for Language and total GMAT score

The descriptive statistics of the total GMAT score based on language revealed a higher GMAT for students with English as their first language with a mean score of 622.27 (standard deviation= 56.61) compared with students with another language as their first language at 598.13 (standard deviation= 60.88). This initial analysis revealed a variation that seemed to suggest that students with English as their first language were likely to perform better in GMAT than those with another language as their first language. The significance of this variation was therefore sought to identify whether the variation could be expected in the entire population or not. The null hypothesis was there is no variation in the total GMAT score based on language.

Table 12: Independent Sample T Test of Total GMAT Based On Language

It was assumed that the sample was randomized and the Levene’s test for equality of variances was 0.069, which was more than .05. This meant that the assumption of equality of variances had been met. The findings revealed a test statistic of 2.248 that was associated with a p value of p = 0.025 that was less than the alpha level of .05. The hypothesis decision therefore was to reject the null hypothesis. It was concluded that language formed a basis of determining the total GMAT of students.


According to the results in this report, the correlation between age of the participants and their starting salary revealed a weak negative correlation of -0.063. This meant that the older a student was the less starting salary he or she would expect. This relationship, though significant was very weak based on the correlation coefficient and a very weak coefficient of determination.


In terms of gender, there was no statistically significant evidence to suggest that gender was associated with starting salary. The overall GMAT percentile from the sample was 84.2 percent. This meant that out of the sampled data, the overall mean GMAT score of 169.45 was in the 84th percentile. Put another way, the sampled students tested better than 83 percent of those who took the test. The results revealed a positive correlation of 0.848 (84.8%) between total GMAT and GMAT percentile. This correlation was statistically significant (t= 191.56, df = 273, p < .01).

The degree of satisfaction with MBA program on average was 5.57 with a standard deviation of 0.98. This result revealed that the MBA program was satisfactory. There was sufficient evidence to conclude that at 95% confidence level, it would be expected the mean level of satisfaction with the schools MBA program would be between 5.44 and 5.69.

Students with English as their first language got a higher mean total GMAT score of 622.27 compared to those with English as a second language who got 598.13. This variation of mean total GMAT score based on language was found to be statistically significant (t= 2.25, df= 272, p= 0.025).


This report recommended that joining the MBA program for the university under review would be beneficial since the overall percentile of the students is ranked high. This could be attributable to the school having high quality teaching staff, adequate studying resources and an enabling environment conducive for learning.



Arbaugh, J. (2010). Sage, guide, both, or even more? An examination of instructor activity in online MBA courses. Computers & Education,55(3), 1234-1244.  doi:10.1016/j.compedu.2010.05.020

Endres, M., Chowdhury, S., Frye, & Hurtubis, C. (2009). The Multifaceted Nature of Online  MBA Student Satisfaction and Impacts on Behavioral Intentions. Journal of Education for  Business,84(5), 304-312. doi:10.15417/1881

Entwistle, N. (2003.). Concepts and conceptual frameworks underpinning the ETL project (p. 3, Rep.). Retrieved March 11, 2017, from ETL Occasional Reports

Fortuna, M. (n.d.). What Is an MBA Degree? Retrieved March 11, 2017, from

Green, F., Machin, S., Murphy, R., & Zhu, Y. (2011). The Changing Economic Advantage from Private Schools. Economica, n/a-n/a.

Jensen, R. (2010). The (Perceived) Returns to Education and the Demand for Schooling. Quarterly Journal Of Economics, 125(2), 515-548.

Jones, H. (2003). The Independent. The Independent. Retrieved 9 March 2017, from [2011-04-10]

Rydzewski, D. N., Eastman, J. K., & Bocchi, J. (2010). Important Characteristics in an MBA Program: The Perceptions of Online MBA Students. American Journal of Business Education (AJBE),3 (4). doi:10.19030/ajbe.v3i4.411

Walker, I. & Zhu, Y. (2011). Differences by degree: Evidence of the net financial rates of return to undergraduate study for England and Wales. Economics Of Education Review, 30(6), 1177-1186.

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