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8 Dec 2022

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MBA Starting Salaries: How Much Do MBAs Make?

Format: APA

Academic level: Master’s

Paper type: Case Study

Words: 921

Pages: 4

Downloads: 0

Correlations 

The scatter plots and correlation coefficient for the variables GMAT Score and Overall GPA are shown below.

From running a regression analysis on Spss, the multiple R or rather the Pearson correlation coefficient was found to be  with a p value of 0.044. Using an alpha of  , we can tell that the correlation is significant as  is less than  .Therefore, GMAT scores have a weak linear correlation with GPA which is significant. This can further be illustrated by the scatter plot as there seems to be a slight increase in GPA as GMAT scores increase.

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The scatter plots and correlation coefficient of the variables Starting salary and GMAT score.

From the regression analysis on Spss, the Pearson correlation coefficient (R) between the two variables is  and the p value is 0.181. Therefore, the GMAT score and starting salary have a very weak negative linear correlation that is insignificant using an alpha of  . This can further be illustrated by the scatter plot, though not clearly but there seems to general slight decrease salaries as the GMAT scores increases.

The scatter plots and correlation coefficient of the variables Starting salaries and Overall GPA are shown below.

From Spss, the Pearson correlation coefficient (R) between the overall GPA and the starting salary is  with a p-value of  . Therefore, there is a very weak and barely visible negative linear correlation between the two variables and since the p-value is more than the alpha level, the correlation is not significant.

The scatter plots and correlation coefficient of the variables starting salaries and years of experience are shown below.

From Spss, the Pearson correlation coefficient between the two variables, starting salaries and years of experience is  with a p-value of  . The p-value is significantly lower than the alpha of  . Therefore, indicating the correlation is significant. Moreover, the two variables have a relatively strong positive linear correlation. The scatter plot illustrates this by showing a slightly visible increase in salary as years of experience increase.

Creating a Salary Model 

After running a multiple regression analysis, with Starting salaries as the dependent variable and GMAT score, Overall GPA and work experience as the independent variables, work experience showed the strongest correlation as well as was the only significant independent variable that determined the Starting salaries. This is because it had a p-value of  which is less than alpha of  . The GMAT score and Overall GPA do not have a significant relationship with the starting salary.

The inclusion of Gender_DV changes the overall Pearson correlation coefficient from  to  . It therefore increases the positive linear correlation between the Starting salaries and the other three independent variables, GMAT score, Overall GPA and work experience.

Running the multiple regression analysis using the independent variables Gender_GMAT, Gender_GPA and Gender_WorkYear, there is a general improvement in the Pearson correlation coefficients of the Gender_GMAT scores and Gender_GPA. The Pearson correlation coefficients are  and  . The two variables also become significant in determining the Starting salaries as they have p-values of  and  respectively which are both lower than the alpha value  . However, the Pearson correlation coefficient of the Gender-work year decreases to  indicating a weaker positive linear correlation between the it and the Starting salaries. It however, remains a significant variable.

From the Spss analysis and using stepwise regression the final model of the regression is

The coefficient of Gender_workyear has a standard error of  and the constant has a standard error of  .

Limitations of the analysis 

From the analysis, what we are not sure about is whether the graduates come out of school and go into employment in their own or family businesses or apply for jobs directly. Moreover, the graduates could go into different firms that are in different fields or are government institutions. Different firms have different income levels which means the salaries they get paid even if they are in the same position could be different.

Appendix

Correlations 

 

Salary 

GMAT_TOT 

GPA 

Work_Yrs 

Pearson Correlation  Salary 

1.000 

-.091 

-.018 

.455 

GMAT_TOT 

-.091 

1.000 

.169 

-.123 

GPA 

-.018 

.169 

1.000 

-.060 

Work_Yrs 

.455 

-.123 

-.060 

1.000 

Sig. (1-tailed)  Salary 

. 

.181 

.428 

.000 

GMAT_TOT 

.181 

. 

.044 

.108 

GPA 

.428 

.044 

. 

.272 

Work_Yrs 

.000 

.108 

.272 

. 

N  Salary 

103 

103 

103 

103 

GMAT_TOT 

103 

103 

103 

103 

GPA 

103 

103 

103 

103 

Work_Yrs 

103 

103 

103 

103 

Table 1 Table showing regression analysis of salary as dependent variable and GMAT scores, Overall GPA and years of experience as the independent variables. 

Model Summary b 

Model 

R 

R Square 

Adjusted R Square 

Std. Error of the Estimate 

Durbin-Watson 

1 

.456 a 

.208 

.184 

16139.492 

1.188 

a. Predictors: (Constant), Work_Yrs, GPA, GMAT_TOT 
b. Dependent Variable: Salary 

Table 2 Model summary of the table 1 regression analysis 

Coefficients a 

Model 

Unstandardized Coefficients 

Standardized Coefficients 

t 

Sig. 

95.0% Confidence Interval for B 

Correlations 

Collinearity Statistics 

B 

Std. Error 

Beta 

Lower Bound 

Upper Bound 

Zero-order 

Partial 

Part 

Tolerance 

VIF 

1  (Constant) 

99102.674 

22536.518 

 

4.397 

.000 

54385.333 

143820.016 

         
GMAT_TOT 

-13.359 

32.196 

-.038 

-.415 

.679 

-77.243 

50.525 

-.091 

-.042 

-.037 

.959 

1.043 

GPA 

746.134 

4392.102 

.015 

.170 

.865 

-7968.750 

9461.017 

-.018 

.017 

.015 

.970 

1.031 

Work_Yrs 

2676.588 

535.316 

.451 

5.000 

.000 

1614.405 

3738.770 

.455 

.449 

.447 

.983 

1.017 

a. Dependent Variable: Salary 

Table 3 Coefficients of table 1 

Correlations 

 

Salary 

GMAT_TOT 

GPA 

Work_Yrs 

Gender_DV 

Pearson Correlation  Salary 

1.000 

-.091 

-.018 

.455 

.166 

GMAT_TOT 

-.091 

1.000 

.169 

-.123 

.020 

GPA 

-.018 

.169 

1.000 

-.060 

-.152 

Work_Yrs 

.455 

-.123 

-.060 

1.000 

.092 

Gender_DV 

.166 

.020 

-.152 

.092 

1.000 

Sig. (1-tailed)  Salary 

. 

.181 

.428 

.000 

.047 

GMAT_TOT 

.181 

. 

.044 

.108 

.422 

GPA 

.428 

.044 

. 

.272 

.063 

Work_Yrs 

.000 

.108 

.272 

. 

.177 

Gender_DV 

.047 

.422 

.063 

.177 

. 

N  Salary 

103 

103 

103 

103 

103 

GMAT_TOT 

103 

103 

103 

103 

103 

GPA 

103 

103 

103 

103 

103 

Work_Yrs 

103 

103 

103 

103 

103 

Gender_DV 

103 

103 

103 

103 

103 

Table 4 Table showing the regression analysis with Gender_DV inclusion 

Model Summary b 

Model 

R 

R Square 

Adjusted R Square 

Std. Error of the Estimate 

Durbin-Watson 

1 

.474 a 

.225 

.193 

16047.860 

1.163 

a. Predictors: (Constant), Gender_DV, GMAT_TOT, Work_Yrs, GPA 
b. Dependent Variable: Salary 

Table 5 Model summary of Table 4 

Coefficients a 

Model 

Unstandardized Coefficients 

Standardized Coefficients 

t 

Sig. 

95.0% Confidence Interval for B 

Correlations 

Collinearity Statistics 

B 

Std. Error 

Beta 

Lower Bound 

Upper Bound 

Zero-order 

Partial 

Part 

Tolerance 

VIF 

1  (Constant) 

94333.329 

22645.176 

 

4.166 

.000 

49394.713 

139271.945 

         
GMAT_TOT 

-16.006 

32.065 

-.045 

-.499 

.619 

-79.637 

47.625 

-.091 

-.050 

-.044 

.956 

1.046 

GPA 

1741.509 

4420.007 

.036 

.394 

.694 

-7029.850 

10512.869 

-.018 

.040 

.035 

.947 

1.056 

Work_Yrs 

2606.092 

534.460 

.439 

4.876 

.000 

1545.474 

3666.710 

.455 

.442 

.434 

.975 

1.025 

Gender_DV 

5121.062 

3505.769 

.132 

1.461 

.147 

-1836.023 

12078.146 

.166 

.146 

.130 

.967 

1.034 

a. Dependent Variable: Salary 

Table 6 Coefficients of table 4 

Correlations 

 

Salary 

Gender_GMAT 

Gender_GPA 

Gender_WorkYear 

Pearson Correlation  Salary 

1.000 

.173 

.192 

.206 

Gender_GMAT 

.173 

1.000 

.967 

.549 

Gender_GPA 

.192 

.967 

1.000 

.544 

Gender_WorkYear 

.206 

.549 

.544 

1.000 

Sig. (1-tailed)  Salary 

. 

.041 

.026 

.018 

Gender_GMAT 

.041 

. 

.000 

.000 

Gender_GPA 

.026 

.000 

. 

.000 

Gender_WorkYear 

.018 

.000 

.000 

. 

N  Salary 

103 

103 

103 

103 

Gender_GMAT 

103 

103 

103 

103 

Gender_GPA 

103 

103 

103 

103 

Gender_WorkYear 

103 

103 

103 

103 

Table 7 Regression analysis using independent variables influenced by gender in determining the dependent variable Starting salaries 

Variables Entered/Removed a 

Model 

Variables Entered 

Variables Removed 

Method 

1  Gender_WorkYear 

. 

Stepwise (Criteria: Probability-of-F-to-enter <= .050, Probability-of-F-to-remove >= .100). 
a. Dependent Variable: Salary 

Table 8 table indicating variables removed after doing stepwise regression 

Model Summary b 

Model 

R 

R Square 

Adjusted R Square 

Std. Error of the Estimate 

Durbin-Watson 

1 

.206 a 

.043 

.033 

17571.151 

.855 

a. Predictors: (Constant), Gender_WorkYear 
b. Dependent Variable: Salary 

Table 9 Model summary after doing stepwise regression of table 7 

Coefficients a 

Model 

Unstandardized Coefficients 

Standardized Coefficients 

t 

Sig. 

95.0% Confidence Interval for B 

Correlations 

Collinearity Statistics 

B 

Std. Error 

Beta 

Lower Bound 

Upper Bound 

Zero-order 

Partial 

Part 

Tolerance 

VIF 

1  (Constant) 

99873.058 

2284.889 

 

43.710 

.000 

95340.452 

104405.663 

         
Gender_WorkYear 

1169.932 

552.434 

.206 

2.118 

.037 

74.050 

2265.813 

.206 

.206 

.206 

1.000 

1.000 

a. Dependent Variable: Salary 

Table 10 Coefficients of table 7 after doing stepwise regression of table 7 

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Reference

StudyBounty. (2023, September 14). MBA Starting Salaries: How Much Do MBAs Make?.
https://studybounty.com/mba-starting-salaries-how-much-do-mbas-make-case-study

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