Understanding Job Satisfaction Among Employees – A Statistical Analysis

Problem definition

In order to set this research work straight, it is only in order to understand the meaning of job satisfaction. According to this research paper, job satisfaction is that feeling of fulfillment that employee gets as a result of doing their job (Barton , 2001). Job satisfaction is a key factor in any organization as it determines the productivity of the organization (Verson , 2016)and ( Pierce , 2004). This is because a satisfied employee will have a high morale of doing work hence improving productivity. However, a dissatisfied employee will have a low morale towards work hence lowering productivity at work (Lin , 2010). So in order to boost the morale and hence their job satisfaction, employers are encouraged to create a good working environment, have fair promotion systems and cultivate good leadership behavior.  XYZ Company has had a difficult year. There have been low sales due to low production (Sousa-Poza , 2000) and (Zhao , 2009). It is perceived that this situation has been occasioned by low morale due to dissatisfaction of the employees. It is against this background that XYZ Company has decided to carry out a research on their employees focusing on their satisfaction.

XYZ Company has had a difficult year. There have been low sales due to low production. It is perceived that this situation has been occasioned by low morale due to dissatisfaction of the employees. It is against this background that XYZ Company has decided to carry out a research on their employees focusing on their satisfaction. The research has employed statistical analysis in order to turn the data into information that can be acted upon. Descriptive statistics was used to establish the mean satisfaction level.

Data variable and types

This research involved both numerical and non-numerical variables. The numerical variables were age, years of experience and salary. The dichotomous categorical variables were gender and marital status. Other categorical variables were job satisfaction before and after training, life happiness score and promotion.

Descriptive statistics

Statistics
	age in years	Years of experience	salary amount in 1000s
N	Valid	300	300	300
Missing	0	0	0
Mean	41.8800	20.7067	47.3600
Median	42.0000	23.0000	47.0000
Mode	54.00	25.00a	45.00
Std. Deviation	10.17895	9.53785	6.67775
Variance	103.611	90.971	44.592
Minimum	20.00	1.00	26.00
Maximum	60.00	36.00	65.00
Interquartile range	1st quartile	32.0000	13.0000	44.0000
2nd quartile	42.0000	23.0000	47.0000
3rd quartile	50.0000	28.0000	52.0000
a. Multiple modes exist. The smallest value is shown

Table 1

The above table shows the descriptive statistics for age of employees, years of experience and salary amount. It can be observed that the mean age of the employees was 41.9 years. The median age was 42 years. The youngest employee was 20 years old while the oldest employee was 60 years old. The ages deviated from the mean by 10.17 years.  It can also be observed that the mean years of experience of employees were 20.7 years. The median years of experience of employees were 23 years. The years of experience of employees deviated from the mean by 9.5 years.  When it comes to salary, it can be seen that the mean salary of the employees at XYZ was 47,360. The median salary was 47,000. The least earning employee got 26,000 while the highest earning employee was 65,000. The salary of experience of employees deviated from the mean by 6.677.

METHODS OF DATA SUMMARIZING

Summary of gender distribution

male or female
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Male	114	38.0	38.0	38.0
Female	186	62.0	62.0	100.0
Total	300	100.0	100.0

Table 2

Table 2 and figure 1 above illustrates the distribution of XYZ Company employees by gender. As can be observed, the males were 114 while the females were 186.

Data variable and types

 Marital status distribution

married or not married
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	Married	252	84.0	84.0	84.0
Single	48	16.0	16.0	100.0
Total	300	100.0	100.0

Table 3

Table 3 and figure 2 above illustrates the distribution of XYZ Company employees by their marital status. As can be observed, the married employees were 252 while the single employees were 48.

Distribution of employees by place of origin

Region of origin
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	East	71	23.7	23.7	23.7
North	47	15.7	15.7	39.3
South	59	19.7	19.7	59.0
West	123	41.0	41.0	100.0
Total	300	100.0	100.0

Table 4 and figure 3 above illustrates the distribution of XYZ Company employees by their places of origin. As can be observed, the employees from east were 71, the employees from north were 47, the employees from south 59 and the employees from west were 123.

working department
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	IT	101	33.7	33.7	33.7
Marketing	109	36.3	36.3	70.0
Sales	32	10.7	10.7	80.7
Human resource	30	10.0	10.0	90.7
Finance	15	5.0	5.0	95.7
Innovation	13	4.3	4.3	100.0
Total	300	100.0	100.0

Table 5

Table 5 and figure 3 above illustrates the distribution of XYZ Company employees by the departments they work in. As can be observed, the employees who worked in the IT department were 101. Those who worked in the marketing department were 109 while those who worked in the sales department were 32. In the human resource department, there were 30 employees. The finance department had 15 employees while the innovation department had 13 employees.

Distribution of employees according to whether they have had promotion

have been promoted
	Frequency	Percent	Valid Percent	Cumulative Percent
Valid	No	110	36.7	36.7	36.7
Yes	190	63.3	63.3	100.0
Total	300	100.0	100.0

Table 6

Table 6 and figure 5 above illustrate the distribution of XYZ Company employees according to whether they have had promotion or not. As can be observed, the employees who have had promotion were 190 while those who have had no promotion were 110.

Inferential statistics

Hypothesis 1

H₀: There is no difference between the job satisfaction score before and after training

Versus

H₁: There is a significant difference between the job satisfaction score before and after training

A paired sample t-test was employed to establish whether there was a significant difference in job satisfaction before training and after training.

Table of results

Paired Samples Test
	Paired Differences	t	df	Sig. (2-tailed)
Mean	Std. Deviation	Std. Error Mean	95% Confidence Interval of the Difference
Lower	Upper
Pair 1	Job satisfaction score before training – Job satisfaction score after training	-1.44333	1.48576	.08578	-1.61214	-1.27452	-16.826	299	.000

Table 7

From the t-test results above, it can be seen that the p-value computed is 0.00. This value is less than the value of level of significance which is 0.05. The decision rule is to reject the null hypothesis and accept the alternative. It is concluded that there is a significant difference between the job satisfaction score before and after training.

Hypothesis 2

H₀: There is no difference in job satisfaction score before training between males and females.

Versus

H₁: There is a significant difference in job satisfaction score before training between males and females.

A paired sample t-test was employed to establish whether there was a significant difference in job satisfaction score before training between males and females.

Table of results

Independent Samples Test
	Levene’s Test for Equality of Variances	t-test for Equality of Means
F	Sig.	t	df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
Lower	Upper
Job satisfaction score before training	Equal variances assumed	1.055	.305	1.321	298	.187	.14997	.11352	-.07342	.37337
Equal variances not assumed			1.300	226.603	.195	.14997	.11539	-.07740	.37735

Table 8

From the t-test results above, it can be seen that the p-value computed is 0.18. This value is greater than the value of level of significance which is 0.05. The decision rule is to accept the null hypothesis and reject the alternative. It is concluded that there is a significant difference in job satisfaction score before training between males and females.

Hypothesis 3

H₀: There is no difference in job satisfaction score after training between males and females.

Versus

H₁: There is a significant difference in job satisfaction score after training between males and females.

A paired sample t-test was employed to establish whether there was a significant difference in job satisfaction score before training between males and females.

Table of results

Independent Samples Test
	Levene’s Test for Equality of Variances	t-test for Equality of Means
F	Sig.	t	df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
Lower	Upper
Job satisfaction score after training	Equal variances assumed	.024	.876	-.762	298	.447	-.09819	.12886	-.35178	.15541
Equal variances not assumed			-.753	230.429	.452	-.09819	.13033	-.35498	.15860

Table 9

From the t-test results above, it can be seen that the p-value computed is 0.88. This value is greater than the value of level of significance which is 0.05. The decision rule is to accept the null hypothesis and reject the alternative. It is concluded that there is a significant difference in job satisfaction score after training between males and females.

Hypothesis 4

H₀: There is no difference in age between males and females.

Versus   

H₁: There is a significant difference in age between males and females.

A paired sample t-test was employed to establish whether there was a significant difference in job satisfaction score before training between males and females.

Independent Samples Test
	Levene’s Test for Equality of Variances	t-test for Equality of Means
F	Sig.	t	df	Sig. (2-tailed)	Mean Difference	Std. Error Difference	95% Confidence Interval of the Difference
Lower	Upper
age in years	Equal variances assumed	2.464	.118	4.224	298	.000	4.97566	1.17803	2.65735	7.29398
Equal variances not assumed			4.091	215.029	.000	4.97566	1.21629	2.57829	7.37304

Table 10

From the t-test results above, it can be seen that the p-value computed is 0.00. This value is less than the value of level of significance which is 0.05. The decision rule is to reject the null hypothesis and accept the alternative. It is concluded that there is a significant difference in there is a significant difference in age between males and females.

Discussion of the results

From the analyses above, several recommendations and conclusions could be made. Majority of the employees in XYZ were married. 252 out of 300 were married while the rest were single. To add on, majority of the employees were females. Out of 300 employees, they were 186. The department which had the largest number of employees was the marketing department which had 109 employees out of 300. The innovation department had the least number of employees. Inferential statistics has found that there was significant difference in the mean satisfaction level between the males and the females. This means that one group was more satisfied than the other when it came to job satisfaction. This research therefore recommended further research to find out the reason for the difference in satisfaction levels. It was also found that there is a significant difference between the job satisfaction score before and after training. The research supposes that there was a great improvement in satisfaction after training.

References

Pierce , L. (2004). The effects of pay level on organization based self-esteem and performance.

Barton , M. (2001). The impact of job satisfaction on turnover intent: a test of structural measurement model using a national sample of workers.

Lin , C. (2010). Employee empowerment in a technology advanced work environment.

Sousa-Poza , A. (2000). Well-being at work. A cross-national analysis of the levels and determinants of job satisfaction.

Verson , R. (2016). Employee acceptance of organizational change. The role of organizational commitment.

Zhao , J. (2009). The determinants of job satisfaction among United States Air Force’s security police