Abstract
This is a quantitative methods and analysis paper for gender and extrinsic job satisfaction based on the data from a survey conducted by American Intellectual Union. It analyzes extrinsic job satisfaction using measures of central tendency and variation and gender using measures of distribution.
Introduction
There were 9 variables examined in the study conducted by the AIU which include gender, position, age, department, position, intrinsic job satisfaction, extrinsic job satisfaction, overall job satisfaction and benefits. However, this paper only analyses two variables. One is quantitative while the other is qualitative.
Chosen Variables
Gender and extrinsic job satisfaction were chosen for purposes of analysis. Gender was expressed as either male or female. This type of data is referred to as nominal. On the other hand, extrinsic job satisfaction was quantified in terms of numbers through the use of ordinal scale. The least and the highest rating for this variable was 2.2 and 6.9 respectively.
Difference in variable types
Gender is a qualitative variable while extrinsic job satisfaction is a quantitative variable. Quantitative variables are expressed in terms of numbers while qualitative variables are expressed using words and other non numerical indicators.
Descriptive statistics: Qualitative variable
Explanation of descriptive statistics
Gender as a qualitative variable could not be analyzed using measures of central tendency or variability. This is because the data was not expressed in numbers but in words. Nonetheless, frequency was used to analyze this variable. Frequency was used to give information about the distribution of males and females involved in the survey. Equal number of females and males participated in the survey. This implies that the sampling technique used allocated male and female equal representation. Therefore, non probability sampling design was employed in the research. This was inappropriate because the American workforce does not consist of equal number of men and women. For this reason, the paper recommends that a mix of purposive and random sampling techniques be used in the subsequent research.
Descriptive statistics: Quantitative variable
Explanation of descriptive statistics
The mean for extrinsic job satisfaction was found to be 5.1. This was the average score for extrinsic job satisfaction given each respondent. If a 7 point ordinal scale was used for measurement then, it can be concluded that respondents rated extrinsic job satisfaction highly. The median for these scores was 5.5. Meaning, half of scores were below this value while the other half were above the value. In terms of percentiles, the median shows that 50% of the respondents gave extrinsic job satisfaction a rating of 5.5 and above. On the other hand, the mode was found to be 5.6. This implies 5.6 was the most frequent or occurring score for extrinsic job satisfaction. In other words, the score was associated with the highest number of respondents.
The other two statistics, variance and standard deviation were used to measure dispersion of scores from the mean. Variance is the average of square of difference between the mean of observations and individual observations. The square root of variance is the standard deviation for the scores. The standard deviation was found to 1.00. This implies that about 68% of the scores were between 4.1 and 6.1 assuming normal distribution. Standard deviation is a representation of standard error in sample mean.
Chart/Graph for qualitative variable
Description of Chart
The above pie chart above shows the proportion of female and male representation in the survey. It shows that equal number of men and women participated in the survey because size of the two segments of the pie chart is the same.
Chart/Graph for quantitative variable
Description of Chart
The histogram above shows the distribution of scores for extrinsic job satisfaction. It shows that a score of between 5.49 and 5.96 was associated with the highest number of respondents while least number of respondents gave extrinsic job satisfaction a rating between 4.08 and 4.55 and 5.96 and 6.43. From the graph it can be deduced that score of 5.02 and above was associated with the majority of the respondents.
Explanation of standard deviation and variance
Variance is the average of square of difference between the mean of scores and individual scores while standard deviation is the square root of variance. Standard deviation and variance are used to measure dispersion in scores. The two statistics measures the variation of scores from the mean (Crossley, 2008). They also give information about the about heterogeneity or homogeneity of scores (Morrow, 2011). Besides, standard deviation is an estimator of error in sampling (Finkelstein, 2009). Thus, it is referred to as standard error of sample mean.
Importance of charts and graphs
Graphs and charts are used to present data visually. A visually presented data is easier to interpret because it condense and break large chunk of data into a simple diagrammatical format. Thus, the consumer of such information can easily comprehend and compare the data. Moreover, charts and graphs stimulate interest in information. They can also be interpreted by people who have no background in statistics or the finer details of the study.
Conclusion
Descriptive statistics involve the use central tendency, measures of dispersion and measures of distribution to analyze data. The analysis involves manipulation of numbers. For this reason, it is mostly applied in quantitative study. References
Crossley, M. L. (2008). The desk reference of statistical quality methods. Milwaukee, Wis: ASQ Quality Press.
Finkelstein, M. O. (2009). Basic concepts of probability and statistics in the law. New York, NY: Springer Science + Business Media LLC.
Morrow, J. R. (2011). Measurement and evaluation in human performance. Champaign, IL: Human Kinetics.
