According to the initial requirements, two sections of data have been chosen: Gender (Male / Female) (Qualitative Data) and Intrinsic Job Satisfaction (Satisfaction with the actual performance of the job) (Quantitative Data). Gender is widely considered during the recruitment procedure by potential employers and, sometimes, immensely influences the final decision on the acceptation of the candidate. Intrinsic Job Satisfaction is undoubtedly paramount for the motivation of the employee, which is a cornerstone of a productive labor.
As per the statistical research conducted, the following results for Intrinsic Job Satisfaction have been obtained: set of data consisted of 112 observations. 50% of people, who took part in the research, have the rate of the variable lower than 5.2 (median calculations). Consequently, another part (50%) possess this variable at a ratio higher than 5.2 . The most frequently observed rate is 4.7 (mode calculations). Generally speaking, an average amount of Intrinsic Job Satisfaction within the data provided appears to be equal to 5.125 (mean calculations). While examining the data given standard deviation has been determined. Standard deviation is the amount of variation or dispersion from the average , which in our case is equal to 0.759. Hence variation is ratio of the standard deviation to the mean, we have gained the rate of 14.81%, which means the range reviewed is consistent and mean rate is typical, as variation appears to be lower than 33% .
The same procedure has been applied to the Gender variable. It is highly important to emphasize that Gender appears to be a Qualitative variable, which leads us to a limited amount of possible calculations. Indubitably, we can easily measure the mode for the variable, which is going to show us the repetitiveness of a sex of the people. In our case, the iteration of both men and women are both equal to 56, which means both of them can be considered as a mode. This equality is of highest importance to make sure the results of the survey and research are correct and not influenced by gender specifics. It is just to say, that any other possible calculations couldn’t be applied to the Gender variable. As stated by Yates, Moore and Starnes, a categorical variable is a variable that can take on one of a limited, and usually fixed, number of possible values, thus assigning each individual to a particular group or "category" . Essentially, indicators 1 and 2 are only given to link a particular person to any gender, which results in absence of any possibility of being utilized during the quantitative analysis of some measures of central tendency (particularly, median and mean) and, as a result, measures of variability (standard deviation and variation).
With this particular research we have accomplished 2 significant goals. First of them is the fact that now we can easily apply a basic procedure of statistical analysis to any statistical observations and generate common conclusions regarding the informational massive. Secondly, we have investigated the realistic data about the Intrinsic Job Satisfaction, which allows us to derive the up-to-date understanding of the appeasement of the employees with their current Job Positions and Job Performance. With the analysis being held, we now know that an average level of Intrinsic Job Satisfaction is around 5.7 , when the maximum possible scale is 7. As a result, we can be sure that most part of the employees, who were asked to give their opinion, are surely more satisfied with their position and performance, than not satisfied.
It is just to say, that charts and graphs are indubitably important while conveying any information to a visual format, as they clearly represent a frequency of any qualitative or quantitative factor happening within the data massive in a form of a sector square or height of a histogram. This makes any data absolutely comparable, even if the measurements may differ. Due to these immense advantages, graphical methods have been used to visualize the data we received. That’s why have included them into a research of the poll conducted as well.
Standard deviation is crucial as it is used to calculate confidence intervals for a mean of the row. This grants us a possibility to review the fluctuations of this average (from a certain minimum to a certain maximum point). A great advantage of the standard deviation is what it is measured the same, as mean. However, standard deviation is not enough to learn the characteristics of distribution of the data, and sometimes cannot help do this at all (e.g. when you have to compare observations, that were measured with different units – in a situation of a research of human’s height and weight). Consequently, variance, as a relative measure, is used.
References
Bland, J.M.; Altman, D.G. (1996). "Statistics notes: measurement error.". Bmj, 312 (7047), 1654. Retrieved from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2351401/pdf/bmj00548-0038.pdf
Yates, Daniel S.; Moore, David S; Starnes, Daren S. (2003). The Practice of Statistics (2nd ed.). New York: Freeman. ISBN 978-0-7167-4773-4.
