Abstract
This paper contains the results of regression analysis for benefits against three forms of job satisfaction based on the AIU data. The analysis established that there was a positive correlation between benefits and extrinsic or overall job satisfaction. On this e other hand, the correlation between benefits and intrinsic job satisfaction was found to be negative.
Introduction
Regresses ion is a statistical technique used to establish the relationship among variables (Freund & Wilson, 1998). The aim of regression analysis is to determine if the is there is a causal effect association between variable. The relationship is quantified at a specific confidence level which is the degree of confidence that the relationship between the variables is close to true relationship. Regression analysis has been used in economics to determine the relationship between economic variables such as prices of commodities, gross domestic product, interest rates and inflation rates. The technique has been accepted in some legal jurisdictions as tools for evaluating racial bias. There are two types of liner regression; simple and multiple. Simple regression is performed when the aim of the analysis is to establish the relationship between two variables for instance scores in a test and intelligence. The simple linear regression takes equation of a line with gradient as the coefficient of correlation (Asthana & Bhushan, 2007). Multiple regressions is performed when the aim of the analysis is to investigate the relation between one independent variable and more than two dependent variables for instance the influence of quality of teaching, available learning resources, intelligence, discipline and intelligence on academic performance. Relationship between variable can be either positive or negative. A positive correlation means an increase in one variable leads to an increase in another variable. On the other hand, a negative correlation means an increase in one variable makes the other variable to decrease. This is indicated by the direction of the correlation coefficient. The values of correlation coefficients range from -1 to +1. Where -1 and + 1 indicate very strong negative and positive correlation respectively (Guerrero, 2010). The value of correlation squared is called the coefficient of determination which is the proportion of variability that can be explained is attributed to the relationship between the variables (David, 2011).
Benefits and Intrinsic Job Satisfaction
Regression output from Excel
Graph
Benefits and Extrinsic Job Satisfaction
Regression output from Excel
Graph
Benefits and Overall Job Satisfaction
Regression output from Excel
Graph
Components of Regression
Similarities and Differences
The regression analysis for the three cases has shown that there is a weak relationship between benefits and the three forms of job satisfaction. The correlations between benefits and extrinsic job satisfaction or overall job satisfaction are all positive. This implies that increases in benefits leads to increase in extrinsic and overall job satisfaction. However, the correlation between benefits and intrinsic satisfaction is negative. Meaning increase in benefits causes intrinsic satisfaction to reduce.
Correlation coefficients
The above regression analysis shows that there is weak correlation between benefits and the three forms of job satisfaction. This is because the correlation coefficients of all these cases are below 0.2. However, the strongest correlation exists between benefits and extrinsic satisfaction. The coefficient of correlation for this case is 0.150496667. It is strongest because it has the highest absolute value for the correlation coefficient.
The correlation coefficient of benefits versus extrinsic job satisfaction is positive. This implies that that extrinsic satisfaction increases with increase in benefits. In other words, there is positive relationship between benefits and extrinsic job satisfaction. This analysis is important to the manager because it informs the person that extrinsic job satisfaction is greatly associated with extrinsic job satisfaction. Consequently, he or she can use benefits to boost the extrinsic job satisfaction of employees in an organization.
Conclusion
This analysis has shown that benefits can be used to boost extrinsic and overall job satisfaction. However, this strategy has negative influences on intrinsic job satisfaction. This calls for the use of optimized benefits as opposed to high benefits.
References
Asthana, H. S., & Bhushan, B. (2007). Statistics for social sciences: (with SPSS applications). Delhi: Prentice-Hall of India.
Freund, R. J., & Wilson, W. J. (1998). Regression analysis: Statistical modeling of a response variable. San Diego: Academic Press.
Guerrero, H. (2010). Excel data analysis: Modeling and simulation. Berlin [u.a.: Springer.
Ravid, R. (2011). Practical statistics for educators. Lanham, Md: Rowman & Littlefield.
