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BUS708 Statistics And Data Analysis
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BUS708 Statistics And Data Analysis
0 Download7 Pages / 1,545 Words
Course Code: BUS708
University: Kings Own Institute
MyAssignmentHelp.com is not sponsored or endorsed by this college or university
Country: Australia
Question:
Prepare a report in a document file which includes all relevant tables and figures, using the following structure:
Give a brief introduction about the assignment, including your research question. Include a short summary of a related article with a proper citation.
Give a short description about this dataset. Is this primary or secondary data? What types of variable(s) is involved? Display the first 5 cases of your dataset.
Explain how you collect the data and discuss its limitation (e.g. whether your sample is biased). Is this primary or secondary data? What type of variable(s) is/are involved? You don’t need to display your data in this section.
Answer:
Introduction
In every environment of work, it is only fair for people to be judged by what they can do and achieve. No one should be discriminated based on race, region, gender or any other distinguishing characteristic (McKinsey, 2010). The act of discrimination nowadays plays out so much when it comes to employment of males and females in many organizations. Researches focused on finding the truth about disparities in treatment of females at work places have been carried out in various disciplines such as psychology, business and sociology.
With regards to gender biasness at work places, it has been found that the female gender has been the greatest victims. The print media and social media have been full of reports of females being paid less than their male counterparts in assignments that are similar or in same job rank. One of the researches conducted on this subject by (Roxana, 2013) found that is always an element of male chauvinism that usually plays out when it comes to recruitment of employees. It found that, all factors kept constant, employers prefer male employees for particular jobs. For example the hiring managers according to (Roxana, 2013) feel that males are usually good in technical subjects therefore they perceive that the same is true when it comes to jobs. These employers because of paranoia assign technical tasks to male employees rather than their female counterparts (Major & McFarlin, 2012) . According to Major and McFarlin, these male employees had the chance to advance in their careers more than their female counterparts courtesy of discrimination. It is against this background that this research has been conducted to unravel the many questions that have been asked about gender discrimination in terms of salary and employment. More so, to find out whether there is biasness in treatment of female or male employees in Australia.
The data to answer the research question was taken from Australian taxation office (ATO). The sample consisted of 1000 workers who were drawn from various professions in Australia. There were both categorical and numerical variables. Some of the numerical variables were salaries and deductions. Categorical variables were gender and profession.
The other type of data was collected by the research itself to help answer the research topic. This data was collected through the use of questionnaires. The main undoing of this method of data collection is that there are always chances of respondents misinterpreting the questions thereby giving wrong responses.
Summary statistics
a) Table of occupation according to gender
Occupation
Gender
Male
Female
Grand Total
Clerical & Administrative worker
16
80
96
Community & Personal service workers
27
54
81
Consultants & Apprentices
36
45
81
Laborers
51
24
75
Machinery operators & Drivers
50
2
52
Managers
55
33
88
Not specified
94
83
177
Professionals
74
116
190
Sales workers
16
45
61
Technicians & Trade workers
85
14
99
Grand Total
504
496
1000
Table 1
Graph of occupation according to gender
Figure 1
From the graph and table above, a distribution of occupation with regards to gender can be observed. A keen analysis of both genders across all occupation can reveal that females have got high numbers in generally half of the occupations. In clerical and administrative jobs, the number of females was 80 and that of males was 16. There were 116 females in professional jobs against 74 for males. There were 45 females and 16 males in sales jobs. Community and personal service worker had 54 females and 27 of the male counterparts. However, there was a shift in some occupations where the number of males was higher than the number of females. An example from the figure and table above is technicians and trade workers. In this occupation the number of males was 85 while the number of females was 15. The other occupations where the numbers of females were more than the number of males were in managerial positions, drivers and laborers. There were 51 males against 24 females working as operators while in managerial positions, the number of males was 55 against 33 of females.
b) Salary/wage amount and gender graph.
Figure 2
Table of correlation involving salary/wage and gender
Correlation analysis results table
gender
Salary/wage
gender
1
Salary/wage
0.224259889
1
Table 2
The results from table 2 above supported by figure 2 above show the amount of correlation that exists between gender and salary. The correlation result gives a correlation coefficient of 0.22. This means that there is little but positive relationship between gender and salary. Furthermore, from the linear graph, it is observed that the computed R-square is 0.05. This indicates that independent variable (gender) can only explain 5% variation that occurs in the dependent variable (salary/wage).
Graph of relationship involving salary and gift amount
Figure 3
The scatterplot above is a representation of the relationship between salary amounts and gift amount. It can be observed that there negligible negative relationship between the two variables. This can be confirmed by the coefficient of independent variable gift (-0.0002). The value of R-square is also zero. This means that the independent variable is not responsible for any variation that occurs in the dependent variable.
Inferential statistics
a) Top 4 occupations according to median salary
managers
Mean
83416.7841
Standard Error
5971.92793
female
33
Median
72401.5
total
88
Mode
0
proportion
0.375
Table 3
Technicians & Trade workers
female
14
Mean
69624.40404
total
99
Standard Error
4447.829874
proportion
0.14
Median
64886
Mode
#N/A
Table 4
professional
Mean
69771.03158
Standard Error
3843.825377
female
116
Median
62108
total
190
Mode
308183
proportion
0.61
Table 5
Clerical & Administrative worker
Mean
46762.51
Standard Error
4163.464
female
80
Median
41605
total
96
Mode
#N/A
proportion
0.83
Table 6
The best paying occupation going by the median salary is managerial position. This position pays a median salary of 72,401.5 dollars. The second lucrative job by the median salary was being a technician or trade worker. The median salary for this group was 64.886. The clerks and administrative workers came third by earning 62,108 dollars. The last top 4 occupation was being a professional. This group of people earned a median pay of 41, 605 dollars.
b) Test for sample proportion
Table 7
Test hypothesis
H0: ? = 0.8
Versus
H1: ? > 0.8
From the normal tables, Z-value at 95% is 1.64
Comparing Z-value calculated with the tabulated Z-value, it is found the calculated Z-value (1.69) is greater than the tabulated Z-value (1.645). We are directed to not reject the null hypothesis. Therefore the proportion of male machine operators is 0.8.
c) Test for the difference in salary between genders
Since there are two variables involved (male and females) in this test, a paired sample t-test was used to test the difference.
Hypothesis
H0: There is no difference in the salary amount between males and females.
Versus
H1: There is a significant difference in the salary amount between males and females.
Table of results
Table 8
From the t-test table of results above, we can observe that the computed p-value is 0.00 while the level of significance is 0.05. Since p-value is less than the significance level, we fail to accept the null hypothesis. The conclusion is that there is a significant difference in the salary amount between males and females.
d) The second data which I collected sought to find out whether there were more males at various work places than their female counterparts. This is because there has been a notion that male job seekers have for a long time being favored when it comes to employment and salary amounts than their female counterparts. The table below shows the distribution of the two genders in terms of remuneration.
Current salary level ($)
Gender
Male
Female
Grand Total
25,000 ≥
10
2
12
26,000 – 36,000
2
2
37,000 – 47,000
2
2
48,000 – 58,000
2
2
48,000-58,000
1
1
58,000 ≤
1
1
Grand Total
14
6
20
Table 9
Figure 4
It can be observed from the table and figure above that the percentage of females in the work place is lower than the percentage of males. The percentage of females is 30% while that of males is 70%.
Discussion and conclusion
This research was conducted in order to be able to use its findings to shed more light on the conventional claim that males are favored than females when it comes to employment and even remuneration. The first data showed that among the 10 occupations, the two genders were equally distributed. Females were high in number in 5 occupations while males were also high in number in the other five occupations. A test to establish whether there a significant difference in salaries paid between males and females found that there was no significant difference in salaries paid between the two genders. However, this research team recommends that further research can still be conducted using other tools to cover for the weaknesses of this research due to the methodology used. In the second data, the proportion of females was less than that of males. This confirmed that in deed there is some favoritism practiced by employers towards male employees. However, in dataset 1, the occupations where males were high in number were physically demanding in nature. For example laborers and drivers were high in number than females. This could explain why they were considered for these kinds of jobs rather than females.
References
Major, B., & McFarlin, D. B. (2012). Overworked and underpaid: On the Nature of Gender Differences in Personal Entitlement. Journal of Personality and Social Psychology, 47(6), 44-56.
McKinsey, C. (2010). Women Matter:Gender Diversity; A Corporate Performance Driver.
Roxana , B. (2013). Women in the workplace: A research round up
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