Number Analysis
Introduction
Psychological wellbeing is an important trait in human resource management and development. Employees with better psychological health perform better than those with neuroticism or ill-health (Quelch & Knoop, 2018). It is, therefore, important for employers to ensure that employees have good psychological health and job satisfaction. This can be done through employee motivation and maintaining a friendly working environment for employees (Robbins, 2019).
The study is conducted to determine the factors that affect job satisfaction and psychological health. In particular, the study is carried out to answer the following research questions.
- Is there a difference in job satisfaction between people from different geographical locations?
- Which is a strong predictor of psychological ill-health; job satisfaction or neuroticism?
The data set was drawn from the British Household Panel Survey (BHPS). Both descriptive statistics and inferential statistics will be used to analyse the study data. Measures of central tendency and distribution will be used to describe the data (Rosnow & Rosenthal, 1992). ANOVA tests will be used to determine the significance of the difference in job satisfaction among the geographical locations. Both correlation and regression analyses will be used to evaluate the predictive strength of psychological ill-health between job satisfaction and neuroticism.
Table 1 below shows the descriptive statistics for the quantitative variables for the study.
Table 1: Descriptive statistics for the study variables
| Descriptive Statistics | |||||
| N | Minimum | Maximum | Mean | Std. Deviation | |
| job satisfaction | 186 | 1.25 | 7.00 | 4.9341 | 1.16162 |
| neuroticism | 186 | 1.00 | 7.00 | 3.6505 | 1.20324 |
| psychological ill-health (general health questionnaire) | 186 | 2 | 32 | 11.15 | 5.000 |
| Valid N (listwise) | 186 | ||||
The average job satisfaction is 4.934, with a standard deviation of 1.1616. The minimum job satisfaction is 1.25, while the maximum is 7. The average neuroticism is 3.651, with a standard deviation of 1.2032. The minimum neuroticism is one while the maximum is 7. The average measure for psychological ill-health is 11.15, with a standard deviation of 5. The minimum measure for psychological ill-health two while the maximum is 32.
Table 2 below shows the descriptive statistics for the distribution of respondents among th regions.
Table 2: Descriptive statistics for sample distribution by location
| geographical location | |||||
| Frequency | Percent | Valid Percent | Cumulative Percent | ||
| Valid | Central London | 39 | 21.0 | 21.0 | 21.0 |
| Greater London | 102 | 54.8 | 54.8 | 75.8 | |
| Rest of South East | 45 | 24.2 | 24.2 | 100.0 | |
| Total | 186 | 100.0 | 100.0 | ||
21% of the respondents are from Central London, 55% from Greater London and 24% from Rest of South East.
Inferential Analysis
The difference in Job Satisfaction by Geographical Region
There are three regions in the dataset which are comprised of Central London, Greater London and the rest of South East. There are three independent groups in the dataset with job satisfaction measured on the ratio scale. Since the categorical groups are greater than two, and the dependent variable is quantitative and measured on the ratio scale, the ANOVA test is appropriate for determining the significance of the difference between group means.
The following hypotheses will be tested.
Null hypothesis: The average job satisfaction for the three locations is significantly equal.
Alternative hypothesis: At least one of the locations has a significantly different job satisfaction than other locations.
Table 3 below shows the descriptive statistics for the job satisfaction for the three regions.
Table 3: Descriptive statistics for job satisfaction by location
| Descriptives | ||||||||
| job satisfaction | ||||||||
| N | Mean | Std. Deviation | Std. Error | 95% Confidence Interval for Mean | Minimum | Maximum | ||
| Lower Bound | Upper Bound | |||||||
| Central London | 39 | 4.8590 | 1.21368 | .19434 | 4.4655 | 5.2524 | 2.00 | 7.00 |
| Greater London | 102 | 5.1446 | 1.01134 | .10014 | 4.9460 | 5.3433 | 3.00 | 7.00 |
| Rest of South East | 45 | 4.5222 | 1.33125 | .19845 | 4.1223 | 4.9222 | 1.25 | 7.00 |
| Total | 186 | 4.9341 | 1.16162 | .08517 | 4.7661 | 5.1022 | 1.25 | 7.00 |
The mean job satisfaction for Central London is 4.859, with a standard deviation of 1.214. The average job satisfaction for greater London is 5.145, with a standard deviation of 1.011. The average job satisfaction for the Rest of South East is 4.522, with a standard deviation of 1.331.
Table 4 below shows the results of the ANOVA test.
Table 4: Analysis of variance results for job satisfaction by location
| ANOVA | |||||
| job satisfaction | |||||
| Sum of Squares | df | Mean Square | F | Sig. | |
| Between Groups | 12.374 | 2 | 6.187 | 4.772 | .010 |
| Within Groups | 237.257 | 183 | 1.296 | ||
| Total | 249.631 | 185 | |||
There is a significant difference in mean job satisfaction for at least one of locations, F(2, 183) = 4.772, p-value = 0.010. Post hoc analysis is conducted to determine the significantly different pairs of locations.
Table 5: Post hoc test for differences in job satisfaction by location
| Multiple Comparisons | ||||||
| Dependent variable: job satisfaction | ||||||
| Tukey HSD | ||||||
| (I) geographical location | (J) geographical location | Mean Difference (I-J) | Std. Error | Sig. | 95% Confidence Interval | |
| Lower Bound | Upper Bound | |||||
| Central London | Greater London | -.28563 | .21437 | .379 | -.7922 | .2209 |
| Rest of South East | .33675 | .24911 | .369 | -.2519 | .9254 | |
| Greater London | Central London | .28563 | .21437 | .379 | -.2209 | .7922 |
| Rest of South East | .62239* | .20377 | .007 | .1409 | 1.1039 | |
| Rest of South East | Central London | -.33675 | .24911 | .369 | -.9254 | .2519 |
| Greater London | -.62239* | .20377 | .007 | -1.1039 | -.1409 | |
| *. The mean difference is significant at the 0.05 level. | ||||||
The average job satisfaction for Central London is equal to the average job satisfaction for the rest of South East. The average job satisfaction for Greater London is significantly higher than the job satisfaction for the Rest of South East.
Determinants of Psychological Ill-health
The predictors of psychological ill-health will be evaluated using the study data. The aim of the analysis is to determine whether job satisfaction and neuroticism are significant predictors of psychological ill-health. Besides, the strength of the prediction is evaluated to determine the more robust predictor of psychological ill-health between the two variables. First, Pearson’s correlation analysis is conducted to determine the relationship between the two independent variables and the dependent variable. Table 6 below shows the correlation matrix for the relationship between the three variables.
Table 6: Correlation matrix for job satisfaction, neuroticism and psychological ill-health
| Correlations | ||||
| job satisfaction | neuroticism | psychological ill-health (general health questionnaire) | ||
| job satisfaction | Pearson Correlation | 1 | -.066 | -.146* |
| Sig. (2-tailed) | .369 | .047 | ||
| N | 186 | 186 | 186 | |
| neuroticism | Pearson Correlation | -.066 | 1 | .183* |
| Sig. (2-tailed) | .369 | .012 | ||
| N | 186 | 186 | 186 | |
| psychological ill-health (general health questionnaire) | Pearson Correlation | -.146* | .183* | 1 |
| Sig. (2-tailed) | .047 | .012 | ||
| N | 186 | 186 | 186 | |
| *. Correlation is significant at the 0.05 level (2-tailed). | ||||
Psychological ill-health has a significant negative correlation to job satisfaction (r = -0.146, p-value = 0.047). This implies that 14.6% of the variation in psychological ill-health is attributed to changes in job satisfaction. The negative correlation implies that an increase in job satisfaction increases psychological ill-health.
Psychological ill-health has a significant positive correlation to neuroticism (r = 0.183, p-value = 0.012). This implies that 18.3% of the variation in psychological ill-health is attributed to changes in neuroticism. An increase in neuroticism increases psychological ill-health.
Both job satisfaction and neuroticism have weak correlations to psychological ill-health. However, the correlation between psychological ill-health and the two independent variables is stronger with neuroticism.
Multiple regression analysis is conducted to determine whether the two independent variables predict psychological ill-health significantly. Table 7 below shows the results of the regression analysis.
Table 7: Correlation coefficients for job satisfaction and neuroticism
| Coefficientsa | |||||||||||
| Model | Unstandardised Coefficients | Standardised Coefficients | t | Sig. | Correlations | Collinearity Statistics | |||||
| B | Std. Error | Beta | Zero-order | Partial | Part | Tolerance | VIF | ||||
| 1 | (Constant) | 11.354 | 1.974 | 5.751 | .000 | ||||||
| job satisfaction | -.578 | .311 | -.134 | -1.860 | .064 | -.146 | -.136 | -.134 | .996 | 1.004 | |
| neuroticism | .725 | .300 | .174 | 2.419 | .017 | .183 | .176 | .174 | .996 | 1.004 | |
| a. Dependent variable: psychological ill-health (general health questionnaire) | |||||||||||
The following regression equation is obtained from the results above.
Psychological ill-health = 11.354 – 0.578 (Job satisfaction) + 0.725 (Neuroticism)
Job satisfaction is not a significant predictor of psychological ill-health, t = 1.860, p-value = 0.064. Neuroticism is a significant predictor of psychological neuroticism, t = 2.419, p-value = 0.017.
An analysis of variance is conducted to determine the significance of the regression model in predicting the dependent variable. Table 8 below shows the results of the ANOVA analysis.
Table 8: ANOVA test for significance of regression model
| ANOVAa | ||||||
| Model | Sum of Squares | df | Mean Square | F | Sig. | |
| 1 | Regression | 238.541 | 2 | 119.271 | 4.975 | .008b |
| Residual | 4387.244 | 183 | 23.974 | |||
| Total | 4625.785 | 185 | ||||
| a. Dependent variable: psychological ill-health (general health questionnaire) | ||||||
| b. Predictors: (Constant), neuroticism, job satisfaction | ||||||
The ANOVA test shows that the regression model developed significantly predicts the dependent variable, F(2, 183) = 4.975, p-value = 0.008). A multiple correlation analysis is conducted to determine the impact of the independent variables on the dependent variable. Table 9 below shows the results of the multiple correlation analysis.
Table 9: Multiple correlation analysis results
| Model Summaryb | ||||
| Model | R | R Square | Adjusted R Square | Std. Error of the Estimate |
| 1 | .227a | .052 | .041 | 4.896 |
| a. Predictors: (Constant), neuroticism, job satisfaction | ||||
| b. Dependent variable: psychological ill-health (general health questionnaire) | ||||
The value of r-squared for the regression model is 0.052. This implies that 5.2% of the variation in the dependent variable is explained by the regression model.
Conclusion
The study shows that there exist significant differences in job satisfaction based on location. The average job satisfaction for the sample participants is lower in the Rest of South East area than Greater London area. Therefore, workers in Greater London area are more satisfied than those in the Rest of South East area. However, the workers in Central London are equally satisfied to those in both Greater London area and the Rest of South East area.
Both job satisfaction and neuroticism have significant correlations to psychological ill-health. Job satisfaction has a negative correlation to psychological ill-health, while neuroticism has a positive correlation. Multiple regression of both job satisfaction and neuroticism on psychological ill-health shows that neuroticism is a better predictor of the psychological ill-health. In addition, a regression model developed using both independent variables significantly predict psychological ill-health.
The results of the study show that employers and human resource managers can reduce psychological ill-health among employees by ensuring that they are satisfied with their jobs and reducing neuroticism among them. In addition, there is a need to determine the cause of the lower job satisfaction among employees in the Rest of South East region.
References
Quelch, J. A., & Knoop, C.-I. (2018). Compassionate Management of Mental Health in the Modern Workplace. Cham Springer International Publishing.
Robbins, S. (2019). Organisational Behaviour. Melbourne: P. Ed Australia.
Rosnow, R. L., & Rosenthal, R. (1992). Statistical Procedures and the Justification of knowledge in Psychological Science. American Psychologist, 44, 1276-1284.