Abstract
A number of results for organizations have been observed in varied American Intellectual Union researches. There has been a determination that the productivity of American employees is affected by individualities. The assignment sees the association between intrinsic job satisfaction and gender. Statistical tools may be used so that a better understanding of the relationship is obtained.
Introduction
Gender differences are also one of the major factors which determine employee satisfaction in the workplace. There are internal factors which affect employee satisfaction. The research by the American Intellectual Union states that gender and other varied internal factors affect the level of job satisfaction. The analysis of these factors is done in detail, with the inclusion of qualitative as well as quantitative findings.
Chosen Variables
The factors under study for the paper are intrinsic job satisfaction and gender. Qualitative data is used for the representation of gender whereas quantitative is used for intrinsic job satisfaction. The answers observed in the form of words for gender can be categorized, and insights can be gained for the same.
For intrinsic job satisfaction, there can be ranking of numbers, thus the quantitative data. The quantification of intrinsic job satisfaction is labeled from 1 to 7 so that it can be better understood.
Variable Type Differences:
There is a variation of a qualitative variable with the variation in a quantitative variable. There is a non-numeric result from a qualitative variable. The type of information obtained is subjective meaning they are contained in images, words or recordings. There cannot be a quantification of these data. Qualitative variables have relevance for data that require subjective analysis. Qualitative variables can be used for both descriptives as well as exploratory research (Walker, 2010).
There is a numerical expression for quantitative variables. The nature of qualitative data is objective and expressed to the concrete point. The use is for counting and for the use of number so that models can be constructed for inference. There is also scientific tools’ use such as computers and calculator so that conclusions can be reached.
Descriptive statistics: Qualitative variable
For the given analysis, the qualitative variable taken is gender. The gender differences can also be shown in the given table:
Descriptive statistics Explanation
There were males as well as females involved in the total sample. The differentiation of genders in the form of male and female is represented by 1 and 2 respectively. The number of males and females taken in the survey is 50 each. Thus, there is equal representation of genders. The inference will be varied in case there is a change in the quantity of men and women used in the study.
Descriptive statistics’ Explanation
The quantitative data is presented in a table. Given the details in the table, each individual creates a ranking for their level of satisfaction from 1 to 7. The ones who have their values inclined towards 1 are dissatisfied with their job whereas the ones who have an inclination towards 7 will be greatly satisfied. Even if the ranking is in decimal points, the conclusions are precise.
Qualitative variable Pie Chart
The expression of qualitative data details will be done by weighing besides the employment position. The association between gender and position is represented by a pie chart.
Explanation of the Chart and inference
The analysis from the pie chart depicts that hourly employed men are more than hourly employed women. There is also the preference of salaried employment by women in comparison to hourly employment. There is high vividness of gender disparity and magnification for dual positions. There is a clear explanation of the chart. There is an explanation from the visual data without data reading. There is easier and clear understanding of the results by the readers of the end-use.
Explanation of the line graph
The visual association between men and women for the level of satisfaction is given by the line graph. There is a representation of intrinsic factor that contributes to the level of satisfaction. It can be concluded that there is the influence of internal factors on men and women for the level of satisfaction. The average of the satisfaction is 5 and is relative. The satisfaction in women is higher because of internal factors in comparison to men.
Standard deviation and variance
Standard deviation and variance are used for the purpose of finding out and evaluating any form of discrepancy or difference in collected data. It also reflects the greater characters of the target group (Mathsisfun.com, 2016). In cases where the variance and standard deviation are calculated well, conclusions will also be well defined.
Importance of charts and graphs
The use of charts and graphs facilitate the in-depth analysis of the collected data from a specific research platform. There is variance in a best-suited way for presentation and communication of information. There may be varied forms of charts and graphs which fairly represent a certain type of data. The findings can thus be interpreted correctly (Herkenhoff & Fogli, 2013). Thus, there is unlimited scope and importance of graphs and charts in research and statistic field.
Conclusion
There is a close association between the two fields of research and statistics. There is the use of a similar type of formulae and tools so that a particular result is obtained. The analysis of data can be done by using statistics in research. Graphs and charts can be used for the analysis of qualitative as well as quantitative data. Standard deviation and variance can also be taken out so that understanding becomes easier. Thus, there can be no separation of research tools and statistical tools.
References
Herkenhoff, L., & Fogli, J. (2013). Applied statistics for business and management using Microsoft Excel. New york: Springer.
Mathsisfun.com,. (2016). Standard Deviation and Variance. Retrieved 9 January 2016, from http://www.mathsisfun.com/data/standard-deviation.html
Walker, I. (2010). Research methods and statistics. Houndmills, Basingstoke: Palgrave Macmillan.
