Discuss about the Quantitative Methods in Business Research Methodology.
A gripping issue in the context of workforce is without doubt the gender gap issue. This hints to the salary gap which is visible between the two genders which is always stacked against females. In theory, there are regulations to ensure that gender based discrimination in employment matters do not exist but despite this the underlying evidence confirms the presence of this gap. It is imperative to close this gap as continuation of this gap can lead to dwindling of female participation levels and hence can trigger potential shortage of labour (Livsey, 2017). In wake of this gripping issue, using the data provided, the aim of the given research is to identify if this problem does persist in Australia. Also, another secondary objective would to identify the potential reasons particular in relation with gender distribution of different occupations.
Dataset one is a secondary dataset which contains key information on randomly collected 1000 taxpayers. It is noteworthy that the given dataset is termed secondary owing to the data retrieved from Australia Taxation Office website instead of being directly collected from the taxpayers. (Hillier, 2016). The key variables contained in this dataset refer to taxpayers’ gender, occupation, deduction claimed on gift along with the salary amount. The gender and occupation of taxpayer would be considered as categorical variables where the representations do not have any magnitude but rather these are codes for particular occupation or gender. However, there are two quantitative variables management which are presented using numerical data. These refer to the income level along with the deduction amount (Flick, 2015).
Dataset 2 is primary data used for this research since data has been collected directly by contacting the respondents. The sample size represented through this data amounts to 30. The number of variables has been limited to only those which are vital for the gender gap related research. It is critical to note that that this dataset has certain limitations which arise primarily on account of use of convenience sampling. Thus, there is a possibility that data collected may be biased and do not represent the underlying population. Also, the small sample size further adds to the possibility of sample data being biased. As a result, while deriving conclusion based on the research results, more focus would be laid on dataset 1 and lesser on dataset 2 (Eriksson and Kovalainen, 2015). The requisite bar chart for highlighting the underlying the gender distribution for various occupations is shown below.
The graphical depiction clearly indicates that the gender distribution across different occupations shows high variability. This is especially apparent from females who as per the sample data have no representation in the occupation with corresponding code 7 but have around 70% representation in the occupation with corresponding code 5. This variability in representation is less extreme for males since no occupation is so female dominated or centric so as to reduce the male proportions to less than 20%. More research needs to be carried out to identify the underlying reasons for such extreme variation especially for female. The requisite column chart for capturing the gender distribution of annual salary is highlighted as shown below.
A clear pattern in relation to female representation is visible from the above graphical summary. Therefore, as the salary levels indicate an increase, the proportion representation of females takes a dip as if there is a perfect inverse relationship between the two. At annual salary level exceeding $ 140,000, the female representation is apparent and only one female is visible more of an exception rather than being a norm. Thereby, the graphical distribution of salaries by gender highlights that gender gap does persist in the given sample. The numerical relationship between gender and salary levels is indicated using a tabular format below.
About 60% of the females in the sample have an annual salary level which does not exceed $ 40,000. This is however not true for males where this level is almost 40%. As the salary level progresses, the situation becomes even more grim for females as they are only seen as anomalies with the basic norm being that these levels are open to males. This clearly raises a key question in relation to the trend witnessed particularly in relation to the underlying reason. A possible reason could be genuine discrimination against females in matters of salary. However, in the presence of so much statutory protection, it is hard to imagine such wide scale discrimination against women. Thus, a plausible explanation would be the differing distribution of two genders across occupations where male tend to have higher representation in jobs that pay a higher salary leading to higher average salary for them. The scatter plot has been chosen as the appropriate graphical technique for exploring any possible relationship between income levels and gift deduction.
The presentation of above scatter plot reveals that the two variable are unrelated owing to distribution of the points being random which no clear pattern. Additional support for this conclusion is provided from the value of R2 which is almost zero. Considering that square root of R2 yields the correlation coefficient, hence the logical conclusion is that correlation coefficient is zero again highllighting no relation between the given variables (Eriksson and Kovalainen, 2015).
This task requires estimating the population proportion distribution of gender across the four occupations that have the highest median salary. Thus, based on the sample data, the first task is to identify these occupations which has been accomplished through the use of pivot table available in excel. These occupational codes are 3,2,1 and 7 in no particular order. For the purpose of estimation of gender in the chosen four occupations, the focus would be estimating female proportion in the above profession with a confidence interval of 95%.
The computation above clearly suggests that one can make a claim that there is 95% probability that proportion of female workers of all workers engaged in occupation 1 would range in the vicinity of (0.3367, 0.5320).
The computation above clearly suggests that one can make a claim that there is 95% probability that proportion of female workers of all workers engaged in occupation 2 would range in the vicinity of (0.4389, 0.5914).
The computation above clearly suggests that one can make a claim that there is 95% probability that proportion of female workers of all workers engaged in occupation 3 would range in the vicinity of (0.0297, 0.1461).
The computation above clearly suggests that one can make a claim that there is 95% probability that proportion of female workers of all workers engaged in occupation 7 would be 0% i.e. comprise of 100% male employees.
On the basis of the confidence intervals computed above, it becomes evident that the gross under-representation of females of two of the four highest paying occupations is a matter of grave concern but may offer insight into the lower average salary for females when compared to the male counterparts. It is imperative that more research in this regards has to be carried out so that the key issues responsible for this dismal representation can be brought out. For performing the requisite hypothesis, null and alternative hypothesis are required as mentioned below.
For the given scenario, a z based test would be found suitable as the given distribution can be assumed as normal. The focus would be on finding the p value since p based approach has been selected for testing the hypothesis. The computation in this regards is as illustrated below.
The excel based result clearly highlights that the p value is 0.0001.For the purposes of testing the given hypothesis, a significance level of 0.05 has been taken. The p value obtained from the above output clearly does not exceed the level of significance. As a result, it would be appropriate to reject the null hypothesis. Consequently the alternative hypothesis is accepted (Hair et. al, 2015). Thus, it would be correct to conclude that the given sample supports that male representation in occupation code 7 is higher than 80%. For performing the requisite hypothesis, null and alternative hypothesis are required as mentioned below.
For the given scenario, a t based test would be found suitable as the given variables, standard deviation is not known, The focus would be on finding the p value since p based approach has been selected for testing the hypothesis. The computation in this regards is as illustrated below.
The excel based result clearly highlights that the p value is 0.000.For the purposes of testing the given hypothesis, a significance level of 0.05 has been taken. The p value obtained from the above output clearly does not exceed the level of significance. As a result, it would be appropriate to reject the null hypothesis. Consequently the alternative hypothesis is accepted (Hillier, 2016). Thus, it would be correct to conclude that Dataset 1 provides evidence indicating that gender gap indeed exists in Australian workplace. For performing the requisite hypothesis, null and alternative hypothesis are required as mentioned below.
For the given scenario, a t based test would be found suitable as the given variables, standard deviation is not known, The focus would be on finding the p value since p based approach has been selected for testing the hypothesis. The computation in this regards is as illustrated below.
The excel based result clearly highlights that the p value is 0.000.For the purposes of testing the given hypothesis, a significance level of 0.05 has been taken. The p value obtained from the above output clearly does exceed the level of significance. As a result, it would not be appropriate to reject the null hypothesis. Consequently the alternative hypothesis is not accepted (Hillier, 2016). Thus, it would be correct to conclude that Dataset 2 does not provide evidence indicating that gender gap indeed exists in Australian workplace.
Conclusion
Gender gap presence is supported by Dataset 1 but not by Dataset 2. But this is hardly surprising considering the biased nature of Dataset 2. However, with regards to reasons for this, the given research provides incomplete insight. This is because the given research does indicate that males have high representation in the high paying occupations which would be to an extent responsible for the high average salaries of males when compared with females. But the reason for low representation of females and the comparative salary drawn by the two genders in the same occupation and position has not been compared.
The given research results clearly highlight the gaps in the present research. In this regards, it is imperative that more research needs to be conducted on exploring the reasons behind females having so low representation in selected occupations. Also, to put all speculations to rest, the average salaries for each occupation for the two genders need to be compared after ensuring that differences in level, experience and education are accounted for.
References
Eriksson, P. and Kovalainen, A. (2015) Quantitative methods in business research 3rd ed. London: Sage Publications.
Flick, U. (2015) Introducing research methodology: A beginner’s guide to doing a research project. 4th ed. New York: Sage Publications.
Hair, J. F., Wolfinbarger, M., Money, A. H., Samouel, P., and Page, M. J. (2015) Essentials of business research methods. 2nd ed. New York: Routledge.
Hillier, F. (2016) Introduction to Operations Research 6th ed. New York: McGraw Hill Publications.
Livsey, A (2017) Australia’s gender pay gap: why do women still earn less than men? [online] Available at https://www.theguardian.com/australia-news/datablog/2017/oct/18/australia-gender-pay-gap-why-do-women-still-earn-less-than-men (Assessed on May 22, 2018)
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