Analysis Of Healthcare Industry Job Satisfaction

Discussions

Describe about the Healthcare Quality and Information Management.

The job satisfaction is a very important issue in case of healthcare industry. The employees in the healthcare industry are concerned with looking after the patients and provide proper treatments to them (Hülsheger et al., 2013). The job satisfaction is required for the peoples in order to provide proper kind of service (Card et al., 2012). The job satisfaction is measured with the help of different measures like the amount of salary, the time required to work, the behavior of the managerial staffs. The job satisfaction also varies from person to person (Purpora & Blegen, 2015).

Job satisfaction is related to psychological condition of the people. Job satisfaction is an impact of different factors (Fortney et al., 2013). The condition of the workplace environment plays an important role in job satisfaction (Millán et al., 2013). The work life balance condition also plays an important role in this aspect (Pineau Stam et al., 2015).

In this report the data on overall job satisfaction is being analyzed with the help of different statistical tests like t-tests, scatter plot, Pearsonian correlation coefficient. The results of the analysis and also the possible interpretation of the results are given in this report.

The job satisfactions of the people who are employed in the concerned industry are measured on a 5 point scale. The average ratings of the two consecutive years 2013 and 2014 are considered. The data were collected with the help of questionnaire. The questionnaires were given to 30 people who are working in the relevant industry. The people were asked about 20 questions and the relevant information was collected on the basis of the answers given by the employees.

The average rate of job satisfaction is measured by the average rating of each of the employee on the 20 given questions. The t-test is done to compare the mean values of the two years. The hypothesis of the test is given by:

H₀: d= 0 against H₁: d

t= (d- µ_d )/s_d.

The “d” represents the difference of each of the two means. µ_d is the mean value of the actual population. The mean value is equal to zero in the case of null hypothesis. So our objective will be two tests whether the mean value obtained from the difference of the average value of the two groups is equal to zero or not. The “test statistic” follows a t distribution with 29 degrees of freedom.

Conclusion

The summary of the measures obtained from the t-test performed in excel is given below:

t-Test: Paired Two Sample for Means
	Variable 1	Variable 2
Mean	3.326667	3.755
Variance	1.018057	0.749543
Observations	30	30
Pearson Correlation	0.807887
Hypothesized Mean Difference	0
Df	29
t Stat	-3.9312
P(T<=t) one-tail	0.000241
t Critical one-tail	1.699127
P(T<=t) two-tail	0.000482
t Critical two-tail	2.04523

The p-value of the test statistic is 0.000241 for one tailed test. The p-value of the test statistic is 0.000482 for the two tailed test. The p-value is the probability value given in the direction of the alternative hypothesis. The alternative hypothesis suggests that the test is one sided. Therefore, the p-value for one sided test is to be considered. The given level of significance is 0.05.The null hypothesis of the test gets rejected if the p-value is less than the given significance level. The test statistic is rejected in this case. Therefore, it can be concluded that the average job satisfaction has been improved than the previous years.

The job satisfaction is measured with the help of various questions like the payment status, the ability to work alone, the workplace environment. The data reveals that there has been an improvement in the job satisfaction level over the years.

The job satisfaction can also be measured with the help of Pearsonian correlation coefficient. The Pearsonian correlation coefficient values are calculated by the following formula:

r =

The Pearsonian correlation coefficient values ranges between -1 to 1. The Pearsonian correlation coefficient value when equals to +1 this implies that there is a perfect positive correlation while if it equals -1 then this indicates there is a negative correlation between the variables. The Pearsonian correlation coefficient values for the two periods are calculated. The values for 2014 are considered as the x variables and the values for 2013 are taken as y variables. The correlation coefficient value for the given dataset is calculated with the help of “EXCEL”. The value of the measure is 0.934873. The correlation matrix obtained from the above observations is:

	Column 1	Column 2
Column 1	1
Column 2	0.934873	1

The value of the cells (1,1) and (2,2) gives the correlation between the same variables. The correlation between the two variables that is the job satisfaction of the people in 2013 and 2014 is 0.934873. This implies that the correlation is quite high. The high value of correlation implies that there is a linear relationship between two variables. The two variables are correlated.

The scatter plot diagram of the levels of the two years is also constructed. The scatter plot diagram is drawn by taking the value for 2013 as x variables. The values of 2014 are taken as y variables. The scatter plot diagram is given below:      

                            

Figure: Scatter plot of the Job satisfaction level for two years.

(Source: Created by author)

The scatter plot diagram shows that there is a positive association between the two values.         

     

Figure: Scatter plot diagram with a linear trend line

(Source: Created by author)

The scatter plot diagram clearly shows the linear association between the two variables. The linear trend line shows that the two variables have a positive association. The linear trend line has a positive slope. The equation of the straight line is y = 1.0502x -0.2842. The value of the R squared statistics is 0.874. The regression equation has two parts. One part is the explained part and the other is unexplained. The “R-squared” measures how much good is the regression equation. It is the ratio of the explained part to the total. Therefore, high value of “R-squared” indicates a good fitted model.

Conclusion:

The data on job satisfaction for the two years 2013 and 2014 are analyzed in this report. The data is analyzed with the help of “paired t-test” .The test has been rejected. The test aims to study whether the mean value of job satisfaction has been increased for the year 2014. The test supports the null hypothesis. This means that the job satisfaction level has been increased in the second year of the study. The correlation coefficient also shows a positive value. The correlation coefficient is almost equals to 1. This means that there is a positive relationship between the two variables. This implies that the value as the value of one variable increases the value of the other also increases. The scatter plot diagram also reveals a straight line with the positive slope. This level of association is because the data has been collected from the same Institute.

The study has been conducted by taking a very small sample. The entire study could had been better if the observations are a bit more. The job satisfaction level has been improved as suggested by the data analysis tools. The job satisfaction level can further be improved by improving the factors that affects the satisfaction level. The improvement in the job satisfaction will lead to the betterment of the services provide to the patients. The data shows three distinct groups. The first group shows a higher level of job satisfaction while the other two groups have given a comparatively low rating. The problems faced by these two groups need special attention.

References:

Hülsheger, U. R., Alberts, H. J., Feinholdt, A., & Lang, J. W. (2013). Benefits of mindfulness at work: the role of mindfulness in emotion regulation, emotional exhaustion, and job satisfaction. Journal of Applied Psychology,98(2), 310.

Card, D., Mas, A., Moretti, E., & Saez, E. (2012). Inequality at work: The effect of peer salaries on job satisfaction. The American Economic Review,102(6), 2981-3003.

Purpora, C., & Blegen, M. A. (2015). Job satisfaction and horizontal violence in hospital staff registered nurses: the mediating role of peer relationships.Journal of clinical nursing, 24(15-16), 2286-2294.

Fortney, L., Luchterhand, C., Zakletskaia, L., Zgierska, A., & Rakel, D. (2013). Abbreviated mindfulness intervention for job satisfaction, quality of life, and compassion in primary care clinicians: a pilot study. The Annals of Family Medicine, 11(5), 412-420.

Millán, J. M., Hessels, J., Thurik, R., & Aguado, R. (2013). Determinants of job satisfaction: a European comparison of self-employed and paid employees. Small business economics, 40(3), 651-670.

Pineau Stam, L. M., Spence Laschinger, H. K., Regan, S., & Wong, C. A. (2015). The influence of personal and workplace resources on new graduate nurses’ job satisfaction. Journal of nursing management, 23(2), 190-199.