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1. Explore and apply different levels of measurements, as well as understanding independent/dependent variables in research scenarios.
2. Construct and interpret graphs and tables to demonstrate analytical proficiency.
3. Calculate basic descriptive statistics and interpret their meaning in application.
4. Apply the principles of normal distribution as they relate to a population distribution.
5. Compare and contrast various aspects of descriptive data analysis and inferential data analysis.
6. Explore and apply basic probability theory to calculations and hypothesis testing.
7. Undertake data analysis by applying the principles of causation versus correlation.
8. Calculate and utilize confidence intervals and margin of error in population estimation results.
9. Determine alpha (p-values), and interpret p-levels as it relates to statistical significance.
10. Utilize statistical software to conduct data analysis.
11. Analyze the use and applicability of statistics in personal, professional, and academic applications, and as a tool for research.
 

Variables Collected

The income senior level managers has been one of the controversial area in the recent time, especially with the growing unicorn which do not hesitate to pay higher compensation for the right candidate. Unicorn are those start-ups whose valuation is more than $ 1 billon. With increasing number of unicorns in last few years, and the higher compensation of the managers, it would be interesting to analyze the factors affecting their income(Business Insider, 2016). Also, there has been limited research on this area, which focus on factors affecting the income level of the senior level managers in unicorn. One of the most important factors are the demographic profile of the managers. So, the current research will only focus on the demographic factors and its impact on income level.

The main aim of the current study is examine the impact of demographic profile on the income level of the senior level managers in unicorns.

Null hypothesis: Demographic profile do not have significant impact on the income level  of the senior managers in unicorn.

Alternative hypothesis. Demographic profile have significant impact on the income level of the senior managers in unicorn.

The variables collected for the current research are as follows: 

The income level of the senior managers have been collected in four different categories.

Age – Measured in terms of years and it is a continuous variable

Education- Highest degree held by the managers and it is a categorical variable

IQ level – IQ score of the managers and it is a continuous variable

Marital status- Marital status of the managers and it is categorical variable

Work experience – Total work experience of the managers in the related industry and it is continuous variable 

Primary data was collected from 50 senior managers from 5 different unicorns. However for some managers the data was not complete, so the final sample size for the current study is 45. 

The descriptive statistics for the continuous variable have been shown in the table below. Descriptive statistics help the research to get the overview of the data. This also shows whether the collected data is as per the requirement of the research paper or not(Wooldridge, 2002). 

On the basis of the results it can be concluded that the average age of the managers in the unicorn is 46 years. This is because most of the unicorn hire the managers from the other established companies who have enough knowledge and experience about the industry and who can guide the unicorn in right direction.  The minimum age is 28 years whereas the maximum age is 60 years. Managers who are young might be the founders of the unicorn. 

Statistics

Age

IQ_score

Experience

N

Valid

44

44

44

Missing

0

0

0

Mean

46.1591

105.9545

17.3864

Median

45.5000

104.5000

20.0000

Mode

44.00a

96.00a

22.00

Std. Deviation

10.04164

9.39806

8.24400

Variance

100.835

88.323

67.964

Skewness

-.275

-.049

-.339

Std. Error of Skewness

.357

.357

.357

Kurtosis

-1.104

-1.281

-.942

Std. Error of Kurtosis

.702

.702

.702

Minimum

28.00

90.00

2.00

Maximum

60.00

120.00

31.00

a. Multiple modes exist. The smallest value is shown

Descriptive Statistics

Similarly the descriptive statistics also show that the average IQ score of the managers is 105.95 with standard deviation of 9.3. This shows that the average IQ level of the managers in not significantly higher than the average IQ level of the normal human being. In fact the maximum IQ score in the current data set is 120 only whereas the minimum is 90. Furthermore the descriptive statistics for work experience is the 17 years. This shows that the senior managers in the unicorn are highly experienced. This can be one of the reason for higher compensation in the unicorns as the managers have to leave their stable job and the trade off to leave the stable job with the venerable unicorn is with the higher income. 

For the categorical variable the graphical presentation of the descriptive analysis have been shown,

The graphical results as shown in the figure above shows that most of the senior managers in the unicorn hold the post graduation, which is logical also. Only 11 % of the managers have bachelor degree. The close analysis of the data indicates that the senior managers who have only the bachelor degree have vast work experience. Furthermore only around 9 % of the managers have Phd. 

Results from the income level distribution shows that around 48 % of the managers have income level between $ 100000 - $ 150000 followed by 30 % of the managers having income level of $50000 and $10000. The proportion of managers having very low level of income and very level of income is comparatively low for the collected sample. Since the data was collected only for the 44 managers, the results cannot be easily generalized. However on the basis of the results it can be said the senior managers are earning higher income as compare to their counterparts in other companies. 

Results from the marital status show that more than 90 % of the managers are married and only 9 % of the managers are unmarried or are divorced. High proportion of the married managers may be because of the higher age group data being collected, as the descriptive statistics for age shows that average age is 46 years. 

Results from the cross tabulation has been discussed in this secion. Cross tabulation has been performed to examine whether there is significant difference in the income level on the basis of different demographic factors.

Cross Tabulation Results

The first cross tabulation results are to examine if there is significant difference in the income level of the managers for different education level. In other words, cross tabulation along with the chi square test help to examine whether the senior manager with post-graduation degree have significantly different income level from that of the senior managers holding bachelor degree of the Phd degree. 

Income level * Educational level Crosstabulation

% of Total  

Educational level

Total

Bachelor degree/Diploma

Post Graduation

Phd

Income level

0 - $50000

4.5%

4.5%

$50001 -$ 100000

25.0%

4.5%

29.5%

$100001 - $150000

6.8%

38.6%

2.3%

47.7%

above $150000

4.5%

11.4%

2.3%

18.2%

Total

11.4%

79.5%

9.1%

100.0%

Symmetric Measures

Value

Approx. Sig.

Nominal by Nominal

Contingency Coefficient

.317

.557

N of Valid Cases

44

Results from the Pearson chi-square test show that the chi square value of 4.89 with 6 degree of freedom is not statistically significant at 5 % significance level. This is because the p value is more than the 0.05. On the basis of these results it can be concluded that there is no significant difference in income level on the basis of the education level of the senior managers. 

Similarly the cross tabulation has been performed to examine whether there is statistically significant difference in the income level of the senior managers for different marital status. Results from the cross tabulation and chi square test are shown in the table below. 

Income level * Marital Status Crosstabulation

% of Total  

Marital Status

Total

Married

Unmarried/Divorsed

Income level

0 - $50000

2.3%

2.3%

4.5%

$50001 -$ 100000

27.3%

2.3%

29.5%

$100001 - $150000

43.2%

4.5%

47.7%

above $150000

18.2%

18.2%

Total

90.9%

9.1%

100.0%

Chi-Square Tests

Value

df

Asymp. Sig. (2-sided)

Pearson Chi-Square

4.886a

3

.180

Likelihood Ratio

3.776

3

.287

Linear-by-Linear Association

2.073

1

.150

N of Valid Cases

44

a. 5 cells (62.5%) have expected count less than 5. The minimum expected count is .18.

Symmetric Measures

Value

Approx. Sig.

Nominal by Nominal

Contingency Coefficient

.316

.180

N of Valid Cases

44

In this case also the pearson chi square value is not statistically significant at 5 %. So, there is no statistically significant difference in the income level for managers with different marital status. In other words married managers are also earning similar income as the unmarried/divorced managers. 

The third cross tabulation is to check the statistical difference on the basis of the age of the senior managers. Results from the chi square are shown in the table below and the results shows that the chi square value of 85.21 with 75 degrees of freedom is not statistically significant. On the basis of the results it can be said that there is no difference in the income level for managers in different age group. 

Chi-Square Tests

Value

df

Asymp. Sig. (2-sided)

Pearson Chi-Square

85.216a

75

.197

Likelihood Ratio

72.588

75

.557

Linear-by-Linear Association

17.422

1

.000

N of Valid Cases

44

a. 104 cells (100.0%) have expected count less than 5. The minimum expected count is .05.

Symmetric Measures

Value

Approx. Sig.

Nominal by Nominal

Contingency Coefficient

.812

.197

N of Valid Cases

44

Results from the contingency coefficient is also insignificant indicating the relationship is not strong. 

Similarly the chi square test for income level and the IQ score has also been conducted and in this case also the chi square value of 72.877 is not statistically significant as the  p value is higher than 0.05. So, there is no statistically significant difference in the income level for managers with different IQ score. 

Chi-Square Tests

Value

df

Asymp. Sig. (2-sided)

Pearson Chi-Square

72.877a

63

.185

Likelihood Ratio

58.725

63

.629

Linear-by-Linear Association

.191

1

.662

N of Valid Cases

44

a. 88 cells (100.0%) have expected count less than 5. The minimum expected count is .05.

Symmetric Measures

Value

Approx. Sig.

Nominal by Nominal

Contingency Coefficient

.790

.185

N of Valid Cases

44

Lastly the cross tabulation for the income level and the work experience has been conducted. As shown in the table below the chi square value is statistically insignificant indicating there is no statistically significant difference in the income level for managers with different work experience 

Chi-Square Tests

Value

df

Asymp. Sig. (2-sided)

Pearson Chi-Square

79.345a

69

.185

Likelihood Ratio

63.398

69

.668

Linear-by-Linear Association

12.596

1

.000

N of Valid Cases

44

a. 96 cells (100.0%) have expected count less than 5. The minimum expected count is .05.

Conclusion

Symmetric Measures

Value

Approx. Sig.

Nominal by Nominal

Contingency Coefficient

.802

.185

N of Valid Cases

44

In addition the contingency coefficient is also insignificant which indicates weak relationship between the two variables. 

In this section results from the inferential analysis has been presented.  For the inferential analysis the One way ANOVA, correlation and the multiple regression analysis has been performed and the results are discussed below.

The first inferential analysis is the one Way ANOVA table. Results from the ANOVA shows that there is statistically significant difference in the group mean in the age for different income level. This is because the F statistic of age, 9.288 is statistically significant at 5 %. 

ANOVA

Sum of Squares

df

Mean Square

F

Sig.

Age

Between Groups

1780.203

3

593.401

9.288

.000

Within Groups

2555.683

40

63.892

Total

4335.886

43

IQ_score

Between Groups

131.532

3

43.844

.478

.699

Within Groups

3666.377

40

91.659

Total

3797.909

43

Experience

Between Groups

914.930

3

304.977

6.077

.002

Within Groups

2007.502

40

50.188

Total

2922.432

43

Similarly the results for experience is also significant as the p value is 0.002 which is less than 5 %. However the significance of IQ score is statistically insignificant. 

The correlation analysis is conducted to examine the relationship between the two variable and also the direction of the relationship. In this case also the correlation was performed and the results are shown in the table below. 

Correlations

Income level

Age

Educational level

IQ_score

Marital Status

Experience

Income level

Pearson Correlation

1

.637**

-.205

-.067

-.220

.541**

Sig. (2-tailed)

.000

.181

.667

.152

.000

N

44

44

44

44

44

44

Age

Pearson Correlation

.637**

1

-.116

-.053

-.443**

.893**

Sig. (2-tailed)

.000

.454

.732

.003

.000

N

44

44

44

44

44

44

Educational level

Pearson Correlation

-.205

-.116

1

-.282

.016

-.109

Sig. (2-tailed)

.181

.454

.064

.918

.482

N

44

44

44

44

44

44

IQ_score

Pearson Correlation

-.067

-.053

-.282

1

-.203

.023

Sig. (2-tailed)

.667

.732

.064

.187

.880

N

44

44

44

44

44

44

Marital Status

Pearson Correlation

-.220

-.443**

.016

-.203

1

-.481**

Sig. (2-tailed)

.152

.003

.918

.187

.001

N

44

44

44

44

44

44

Experience

Pearson Correlation

.541**

.893**

-.109

.023

-.481**

1

Sig. (2-tailed)

.000

.000

.482

.880

.001

N

44

44

44

44

44

44

**. Correlation is significant at the 0.01 level (2-tailed).

Results from the correlation analysis show that the dependent variable income level is positively and significantly correlated with age and experience, whereas there exist negative relationship between income level with level of education, IQ score, and marital status. Correlation between other variables can be interpreted from the above table. 

Results from the regression analysis have been discussed in this section. Regression analysis allows that researcher to examine the impact of the independent variables on the dependent variable. The regression coefficient tells whether the impact is positive or negative and the significance value or the p value shows whether the impact is statistically significant or not. In this case the income level is the dependent variable whereas the demographic factors age, education level, marital status, IQ score and work experience are the independent variables. 

Model Summaryb

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

Durbin-Watson

1

.657a

.432

.358

.63699

2.356

a. Predictors: (Constant), Experience, IQ_score, Educational level, Marital Status, Age

b. Dependent Variable: Income level

Model summary the results for R squared and the Durbin Watson test. The R squared value in this case is 0.35 which means that 35 % of the variance is explained by the demographic factors whereas other variation is due to some other factors. Furthermore the value of Durbin Watson is 2.36 which is close to 2, so there is no problem of autocorrelation in the data set. 

ANOVAa

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

11.741

5

2.348

5.787

.000b

Residual

15.419

38

.406

Total

27.159

43

a. Dependent Variable: Income level

b. Predictors: (Constant), Experience, IQ_score, Educational level, Marital Status, Age

Similarly the results from the ANOVA test show that the F statics  of 5.787 which indicates the cumulative effect of the independent variable on the dependent variable  is statistically significant as the p value is less than 0.05. 

Coefficientsa

Model

Unstandardized Coefficients

Standardized Coefficients

t

Sig.

Collinearity Statistics

B

Std. Error

Beta

Tolerance

VIF

1

(Constant)

1.225

1.868

.656

.516

Age

.057

.022

.726

2.610

.013

.193

5.175

Educational level

-.260

.225

-.150

-1.154

.256

.890

1.124

IQ_score

-.005

.011

-.059

-.440

.662

.833

1.200

Marital Status

.120

.394

.044

.305

.762

.718

1.392

Experience

-.010

.027

-.101

-.360

.721

.190

5.251

a. Dependent Variable: Income level

Finally the results for the regression coefficient are presented in the table above. As table indicates that age, and marital status have positive coefficient indicating that these variable have positive impact on the income level. The coefficient of age is statistically significant also. The coefficient can be interpreted as, with one year increase in the age, the income level increases by 0.057 units holding all other factors constant.

However on the other hand the coefficient of educational level, IQ score and experience shows negative coefficient. This indicates that those variables have negative impact on the income level. However the coefficient of experience, educational level and IQ score were expected to be positive as shown by the previous results(Gavett, 2015; Gensowski, 2018; Heckman, Humphries, & Veramendi, 2016; Ours & Stoeldraijer, 2010; Wannakrairoj, 2013). In this case the results are opposite. This may be because of the small sample size and the biasness in the data, as data was only collected from the senior managers in the unicorns. Furthermore the results for the multicollinearity shows that there is no problem of mulitcollinearity as the VIF are between 1 and 10. 

Hypothesis testing

Since there are 5 different demographic variables, 5 sub hypothesis has been tested. 

Null hypothesis: There is no significant relationship between the age and the income level of the senior managers in unicorn.

Alternative hypothesis: There is significant relationship between the age and the income level of the senior managers in unicorn.

Since the regression coefficient of age is statistically significant the null hypothesis can be rejected and alternative hypothesis can be accepted.

Hypothesis 1b.

Null hypothesis: There is no significant relationship between educational level and the income level of the senior managers in unicorn.

Alternative hypothesis: There is significant relationship between education level and the income level of the senior managers in unicorn.

Since the regression coefficient is not statistically significant we fail to reject the null hypothesis.

Hypothesis 1c.

Null hypothesis: There is no significant relationship between marital status and the income level of the senior managers in unicorn.

Alternative hypothesis: There is significant relationship between marital status and the income level of the senior managers in unicorn.

Since the regression coefficient is not statistically significant we fail to reject the null hypothesis. 

Hypothesis 1d.

Null hypothesis: There is no significant relationship between IQ score and the income level of the senior managers in unicorn.

Alternative hypothesis: There is significant relationship between IQ score and the income level of the senior managers in unicorn.

Since the regression coefficient is not statistically significant we fail to reject the null hypothesis. 


Hypothesis 1e.

Null hypothesis: There is no significant relationship between work experience and the income level of the senior managers in unicorn.

Alternative hypothesis: There is significant relationship between work experience and the income level of the senior managers in unicorn.

Since the regression coefficient is not statistically significant we fail to reject the null hypothesis.  

References

Business Insider. (2016, May 24). The 10 highest paid CEOs of 2016. Business Insider. Retrieved from https://www.businessinsider.in/The-10-highest-paid-CEOs-of-2016/articleshow/58827319.cms

Gavett, G. (2015). The Factors That Lead to High CEO Pay. Harvard Business Review. Washington D. C.: Harvard Business School Press.

Gensowski, M. (2018). Personality, IQ, and Lifetime Earnings. Copenhagen.

Heckman, J. J., Humphries, J. E., & Veramendi, G. (2016). Returns to Education: The Causal Effects of Education on Earnings, Health and Smoking (No. 9957). Germany.

Ours, J. C. van, & Stoeldraijer, L. (2010). Age, Wage and Productivity. Germany.

Wannakrairoj, W. (2013). he Effect of Education and Experience on Wages: The Case Study of Thailand in 2012. Southeast Asian Journal of Economics, 1(1), 27–48.

Wooldridge, J. M. (2002). Econometric Analysis of Cross Section and Panel Data. booksgooglecom (Vol. 58). MIT Press. https://doi.org/10.1515/humr.2003.021

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