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Compensation for Sales Professionals Case Study


Executive Summary

This paper presents comprehensive results from a survey conducted within a local company in Abu Dhabi to test the difference in mean salary and experience for 184 inside and outside salespersons. The variables of the study were salary, position, and year of experience. Through descriptive statistics, the researcher used tools such as ANOVA, regression, and t-test to establish the existing relationship between the variables. The findings revealed that there is a significant difference in the mean salary due to position. Besides, the years of experience creates a difference in the mean salary of salespersons. However, there is no significant difference between the proportions of inside salespersons with low and high experience.

Statistical Analysis

Introduction

This research paper examines the existing relationship in the variables of salary, position, and years of experience for 184 salespersons in a local company in Abu Dhabi. Through descriptive statistics, the paper intends to establish existing significance in the variables of the study.

Main text: Statistical analysis

Coding of data

The data that is available for the analysis contains both qualitative and quantitative attributes (Yin, 2009). Therefore, it will be important to convert the qualitative attributes into numerical for ease of analysis (Zikmund et al., 2012). Therefore, the data for position and experience will be coded as follows;

Position: Inside = 1, outside = 2

Experience: Low = 1, Medium = 2, High = 3

The coded data is shown below.

Position Experience Position Experience Position Experience
1 1 2 1 1 2
1 1 2 1 1 2
1 1 2 1 1 2
1 1 2 1 1 2
1 1 2 1 1 2
1 1 2 1 1 2
1 1 2 1 1 3
1 1 2 1 1 3
1 1 1 2 1 3
1 1 1 2 1 3
1 1 1 2 1 3
1 1 1 2 1 3
1 1 1 2 1 3
1 1 1 2 1 3
1 1 1 2 1 3
1 1 1 2 1 3
1 1 1 2 2 3
1 1 1 2 2 3
1 1 2 2 2 3
1 1 2 2 2 3
2 1 2 2 2 3
2 1 2 2 2 3
2 1 2 2 2 3
2 1 2 2 2 3
2 1 2 2 2 3
2 1 2 2 2 3
2 1 2 2 2 3
2 1 2 2 2 3
2 1 2 2 2 3
2 1 2 2 2 3
2 1 2 2 2 3
2 1 2 2 2 3
2 1 2 2 2 3
2 1 2 2 2 3
2 1 2 2 2 3
2 1 2 2 2 3
2 1 2 2 2 3
2 1 2 2 2 3
1 1 2 2 2 3
1 1 2 2 2 3
1 1 2 2 2 3
1 1 2 2 2 3
1 1 2 2 1 3
2 1 2 2 1 3
2 1 1 2 1 3
2 1 1 2 1 3
2 1 1 2 1 3
2 1 1 2 1 3
2 1 1 2 1 3
2 1 2 2 1 3
2 1 2 2 1 3
1 1 2 2 1 3
1 1 2 2 1 3
1 1 2 2 1 3
1 1 2 2 1 3
1 1 2 2 2 3
1 1 2 2 2 3
1 1 2 2 2 3
2 1 1 2 2 3
2 3 2 3 2 3
2 3 2 3
2 3 2 3

Descriptive statistics

The table presented below shows descriptive statistics for the data.

Salary (AED) Position Experience
Mean 199839.6576 1.581521739 1.961956522
Standard Error 1780.583413 0.036466483 0.06134958
Median 198770 2 2
Mode 160392 2 1
Standard Deviation 24153.00854 0.494655446 0.832186191
Sample Variance 583367821.4 0.24468401 0.692533856
Kurtosis -0.0896271 -1.90983 -1.554147
Skewness 0.126666587 -0.3332322 0.071648075
Range 131380 1 2
Minimum 145863 1 1
Maximum 277243 2 3
Sum 36770497 291 361
Count 184 184 184

The results above show that the average salary for the salespersons is AED199, 839.65. Further, a significant proportion of the sample size is employed outside sales positions. Also, a large number of employees have been employed in the organization between 11 to 20 years. The minimum salary that employees are paid is AED145, 863 while the maximum salary is AED277, 243. The deviation of salary from the mean is 24,153.

The confidence interval for mean salary

The 95% confidence interval for the annual mean salary is shown below.

AED196, 326.55 ≤ µs ≤ AED203, 352.77

The upper limit for salary is AED203, 352.77 while the lower limit is AED196, 326.55.

The confidence interval for mean salary for inside salespersons

The 95% confidence interval for the annual mean salary for inside salespersons is shown below.

AED 184234.63 ≤ µs ≤ AED195751.32

The upper limit for salary is AED195751.32 while the lower limit is AED 184234.63.

The difference between the mean salary due to position

Hypothesis

  • H0: There is no significant difference in the mean salary due to position.
  • H1: There is a significant difference in the mean salary due to position.

Statistical technique

In this case, a paired sample t-test will be used to test the hypothesis.

Justification

A paired sample t-test is the most suitable for testing the hypothesis that compared the mean of two related variables.

Results of the test

T-Test: Two-Sample Assuming Unequal Variances.

t-Test: Two-Sample Assuming Unequal Variances
inside Outside
Mean 189992.974 206925.5888
Variance 643650435.1 424534533.6
Observations 77 107
Hypothesized Mean Difference 0
Df 142
t Stat -4.82281336
P(T<=t) one-tail 1.80151E-06
t Critical one-tail 1.655655173
P(T<=t) two-tail 3.60301E-06
t Critical two-tail 1.976810963

In the results, the mean and variance of overall satisfaction for outside salespeople are greater than that of inside employees. Further, t-calculated is greater than t-critical. Also, the p-value is less than alpha (5%). Therefore, the null hypothesis will be rejected at the 95% confidence level. This implies that there is a significant difference in the mean salary due to position.

The confidence interval for the mean salary difference

The calculation for the confidence interval is summarized in the table below.

t-Test: Two-Sample Assuming Unequal Variances
inside outside
Mean 189992.974 206925.5888
Variance 643650435.1 424534533.6
Observations 77 107
Hypothesized Mean Difference 0
Df 142
t Stat -4.82281336
P(T<=t) one-tail 1.80151E-06
t Critical one-tail 1.655655173
P(T<=t) two-tail 3.60301E-06
t Critical two-tail 1.976810963
Lower Limit = M1– M2–(tCL)(sm1-m2 )
upper Limit = M1– M2+(tCL)(sm1-m2)
m1-m2 16,932.61
t Critical two-tail 1.976810963
sm1 8359096.559
sm2 3967612.464
12326709.02
(sm1-m2) 3510.94133
upper limit 23,873.08
lower limit 9,992.15

AED9, 992.15 ≤ µsi – µso ≤ AED23, 873.08

The differences in mean salary between inside and outside salespersons are expected to range between AED9, 992.15, and AED23, 873.08. The analysis in part 4 above shows that there is a significant difference in the mean salary due to position. This can be established in part 5 above by working with the upper and lower limits backward. This can be achieved by working with a margin of error that is highlighted in the table above (Frankfort­ & Nachmias, 2008).

The difference in mean salary due to experience

Hypothesis

  • H0: There is no significant difference in the mean salary due to years of experience.
  • H1: There is a significant difference in the mean salary due to years of experience.

Statistical technique

Analysis of variance (ANOVA) will be used to test the hypothesis.

Justification

ANOVA is the most suitable technique for testing the hypothesis that entails comparing mean for more than one group. One way ANOVA will be used because there is only one independent variable.

Results of the test

ANOVA: Single Factor
SUMMARY
Groups Count Sum Average Variance
LOW 67 1230343 183633.4 375405468.9
MEDIUM 57 1121847 196815.3 301088337.1
HIGH 60 1324858 220809.8 349412072.1
ANOVA
Source of Variation SS Df MS F P-value F crit
Between Groups 44503291 2 2.23E+10 64.69642 6.34E-22 3.0458
Within Groups 62253020 181 3.44E+08
Total 1.06756E+11 183

Interpretation

In the results above, the value of F-calculated is greater than the F-critical. Besides, the p-value is less than alpha (5%). Therefore, the null hypothesis will be rejected at the 95% confidence level. This implies that there is a significant difference in the mean salary due to years of experience (Howell, 2016).

Point estimates

The sample estimates are point estimates for the population parameters. The table presented below shows the estimates for the sample.

Inside Outside Total
Number 77 107 184
Proportion 0.418478 0.581522 1

The point estimate for inside is 41.85% while outside is 58.15%. However, it is important to estimate the margin of error to estimate the range for the population parameter (Baltagi, 2011).

Difference between proportions

Hypothesis

  • H0: There is no difference between the proportions of inside salesperson between Low and High experience.
  • H1: There a difference between the proportions of inside salesperson between Low and High experience.

The calculations are summarized below.

Low high medium
Number 32 23 22 77
proportion (p) 0.415584 0.298701 0.714286
1/n1 0.03125
1/n2 0.043478
0.074728
S.E 0.009276
0.096314
Z 1.21356
read p values from the normal distribution table 0.11246 0.22492

Interpretation of results

The calculation in the table above shows that the p-value is 0.22492 and it is greater than the alpha (0.05). Therefore, the null hypothesis will not be rejected at a 5% significance level. This implies that there is no difference between the proportions of inside salespeople between Low and High experience (Kenney & Keeping, 2011).

The confidence interval for the difference in proportions

The calculations for a 95% confidence interval in the proportion of inside salespersons between low and high experience is shown below.

Low High medium
Number 32 23 22 77
proportion (p) 0.415584 0.298701 0.714286
difference in proportion 0.116883
95% confidence interval with continuity
lower limit -0.034
upper limit 0.2606

-0.034 ≤ PslPsh ≤ 0.2606

The results show that the range of difference in proportion between the two groups will fall between -0.034 and 0.2606.

Dependence between salesperson position and experience

Correlation coefficient

The table below shows the result of the correlation between position and experience.

Position Experience
Position 1
Experience 0.080586253 1

The result indicates that the correlation coefficient between the two variables is 8.06%. This shows a weak positive relationship between the two variables.

Regression

Hypothesis

  • H0: There is no relationship between salesperson position and experience.
  • H1: There is a relationship between salesperson position and experience.

Statistical technique

In this case, a simple regression analysis will be used.

Justification

Regression analysis is used to model the relationship between the dependent variable and other explanatory variables.

Results of the test

The regression results shown below will also give information on the relationship between the two variables.

Regression Statistics
Multiple R 0.08058625
R Square 0.00649414
Adjusted R Square 0.00103532
Standard Error 0.83175528
Observations 184
ANOVA
df SS MS F Significance F
Regression 1 0.823027 0.82302 1.1896 0.27684
Residual 182 125.9107 0.69181
Total 183 126.7337
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 1.74754 0.2059 8.4864 7.29E-15 1.34124 2.15384
Position 0.13557 0.1242 1.0907 0.2768 -0.10968 0.38082

Interpretation

The F-test will be used to test the overall significance of the regression model. The p-value for the F – test is greater than alpha (0.05) (Mugenda & Mugenda, 2013). Therefore, the null hypothesis will not be rejected. This implies that there is no relationship between the two variables. Also, the p-value for the explanatory variable (0.27684) is greater than the alpha (0.05). This implies that the sale person position is not a significant determinant of years of experience (Kothari, 2009). Also, the value of R-square is 0.64%. This implies that position explains only 0.64% of variations inexperience. This indicates an extremely weak relationship (Verbeek, 2008). It can be concluded that the two variables do not depend on each other.

Conclusions

The discussion above shows that there is a significant difference in the mean salary due to position. Also, years of experience create a difference in the mean salary of salespersons. Further, there is no significant difference between the proportions of inside salespeople falling within the low and high experience.

Recommendation

From the above findings, it is apparent that higher experience attracts a better salary, especially for the inside salespersons. Therefore, there is a need to consider experience and position when deciding on the salaries of the employees.

References

Baltagi, G. (2011). Econometrics. New York, NY: Springer Publisher.

Frankfort, C., & Nachmias, D. (2008). Research methods in the social sciences. New York, NY: Worth.

Howell, D. (2016). Fundamental statistics for the behavioral sciences. New York, NY: Cengage Learning.

Kenney, J. F., & Keeping, E. S. (2011). Linear regression and correlation. Princeton, NJ: Van Nostrand.

Kothari, J. (2009). Research methodology: methods and techniques. New Delhi, India: New Age International (P) Limited Publishers.

Mugenda, C., & Mugenda, O. (2013). Applied statistics in research. Capetown, SA: CapeHouse Publishers.

Verbeek, M. (2008). A guide to modern econometrics. London, UK: John Wiley & Sons.

Yin, R. (2009). Case study research: Design and methods. Beverly Hills, CA: Sage.

Zikmund, W., Babin, B., Carr, J., & Griffin, M. (2012). Business research methods. Alabama, Al: Cengage Learning.

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IvyPanda. (2020, October 13). Compensation for Sales Professionals. Retrieved from https://ivypanda.com/essays/compensation-for-sales-professionals/

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"Compensation for Sales Professionals." IvyPanda, 13 Oct. 2020, ivypanda.com/essays/compensation-for-sales-professionals/.

1. IvyPanda. "Compensation for Sales Professionals." October 13, 2020. https://ivypanda.com/essays/compensation-for-sales-professionals/.


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IvyPanda. "Compensation for Sales Professionals." October 13, 2020. https://ivypanda.com/essays/compensation-for-sales-professionals/.

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IvyPanda. 2020. "Compensation for Sales Professionals." October 13, 2020. https://ivypanda.com/essays/compensation-for-sales-professionals/.

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IvyPanda. (2020) 'Compensation for Sales Professionals'. 13 October.

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