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Unit 1 - Fundamentals of Statistics
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American InterContinental University
Abstract
Throughout the research of American Intellectual Union, different results has taken place in the organizations. Satisfactory determine individualities affect the productivity of employees in America. The following paper shows the relationship between gender and intrinsic job satisfaction. By use of various statistical tools, the relationship is better understood (Roxy Peck, 2011).
Introduction
In the work place, gender difference plays a vital role in providing satisfaction. Internal factors affect the level of satisfaction of employees. In a research done by American Intellectual Union, job satisfaction is affected by the gender and other internal factors. External factors also contribute to level of satisfaction by employees. These factors have been analyzed in details. They entail both quantitative and qualitative findings (Creswell, 2011).
Chosen Variables
In the paper, we discuss gender and intrinsic job satisfaction. Gender represents the qualitative data while intrinsic job satisfaction falls within quantitative data. Gender form the qualitative data because it can only be analyzed as words and observations made from the research. The observations answer and provide insight on the questions that are posed at the beginning of the research.
Intrinsic job satisfaction can be ranked in numbers. This proves the reason it is rankled as quantitative data. The level of job satisfaction is quantified between 1 and 7 to allow better understanding. Therefore, in the following paper, job satisfaction will be taken as quantitative results.
Difference in variable types
Qualitative variables vary greatly with quantitative variable. Qualitative variables provide non numeric results. These types of variables provide subjective information in words, images or verbal recording. The data collected cannot be quantified in numeric values. For data that need subjective analysis, qualitative variables are relevant (James L. Burrow, 2011). Exploration and descriptive research use qualitative variables.
Quantitative variables are expressed in numerical values. Qualitative research is objective and to the point. They are used in counting and using numbers to construct models to infer from them. It makes use of scientific tools like calculators and computers in arriving at conclusions.
Descriptive statistics: Qualitative variable
In this analysis, gender has been taken as the qualitative variable. The table below shows the differentiation between the genders.
GENDER
|
REPRESENTATION
|
Male
|
1
|
Female
|
2
|
Explanation of descriptive statistics
From the sample of 42 participants taken in the study, some were male while others while female. To differentiate the genders, the research used 1 and 2 to represent male and female respectively. The research used 5o male and 50 female. The genders were equally represented in the study. As recommendation, changing the number of men and women would bring out a different inference (Roxy Peck, 2011).
Descriptive statistics: Quantitative variable
Quantitative variable is represented by intrinsic job satisfaction. The ranking of job satisfaction is ranked from 1 to 7. This is shown in the following table which show gender and job satisfaction arising from intrinsic factors.
5.5
5.2
5.3
4.7
5.2
4.7
4.7
5.4
3.7
5.2
5.5
5.2
5.3
4.7
5.5
5.2
4.7
5.4
5.5
4.7
5.2
5.3
5.3
5.5
5.2
5.3
4.7
|
Explanation of descriptive statistics
The table above presents the intrinsic quantitative data. From the table, different individual rank their job satisfaction between 1 and 7. Those with values closer to 1 are not satisfied while those closer to 7 are highly satisfied. The ranking in even decimal points gives more precise and specific conclusions.
Pie Chart for qualitative variable
To express the details of qualitative data, they will be weighed against the position one hold in employment. The pie chart represents the relationship between position and gender.
Chart explanation and inference
The pie chart above shows that the number of men in the hourly employment is more than women. Women mostly prefer the salaried employment to hourly employment. Gender disparity is very vivid and magnified in both positions. The chart gives a clear explanation. The visual diagram explains further without reading on the data. Results are more clearly and easily understood by the target readers (David Blakesley, 2011).
Line Graph for quantitative variable
Explanation of the line graph
The line graph above gives the visual relationship between men and women in the level of satisfaction of their job. It represents the intrinsic factor contributing to satisfaction. From the graph, both men and women are influenced by the internal factors of an organization to either get very satisfied or less satisfied while working. The level of satisfaction is relative and averages at 5. The decline does not go to zero while the rise does not reach 7. Also, women have a higher satisfaction due to internal factors than men. Therefore, the graph gives a fast and clear understanding of the results.
Standard deviation and variance
Standard deviation and variance are tools used in statistics to understand quantitative data. They are closely related as they explain on the deviation of values from the mean. Variance shows the deviation of each recorded data from the mean of the whole results. Standard deviation is calculated from the variance. It is the square root of variance.
They are used in statistics to find and evaluate discrepancies or variance in data collected. The variance brings out great characteristics of the target population (Roxy Peck, 2011). They are needed to steer clear of alterations and expected differences between samples. With well calculated variance and standard deviation, well defined conclusions are made from the research.
Importance of charts and graphs
Charts and graphs are visual presentation of data. They can either be in the form of graphs, histograms, pie charts or line graphs. They convey information in a simple and easy to understand format. From the charts and graphs, one can interpret data and give a viable conclusion.
Charts and graphs provide a platform for in-depth analysis of information collected from a particular research. The best way to present and communicate information varies greatly. Different type of data require different types of charts and graphs to relay and interpret the findings correctly (Roxy Peck, 2011). Therefore, the importance of charts and graphs are unlimited in statistics and research.
Conclusion
Statistics and research are two closely related subjects. They use similar tools and formulas in reaching particular finding. Statistics is used in research in analyzing data. Both qualitative and quantitative data is analyzed by use of charts and graphs. Other methods like variance and standard deviation are used in understanding data from a particular sample. Therefore, research and statistical tools cannot be separated.
Reference
Creswell, J. W. (2011). Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research. New York: Pearson.
David Blakesley, J. L. (2011). Writing: A Manual for the Digital Age. Chicago: Cengage Learning.
James L. Burrow, J. B. (2011). Marketing. London: Cengage Learning.
Roxy Peck, C. O. (2011). Introduction to Statistics and Data Analysis. Chicago: Cengage Learning.