Survey Results

Survey results will be used by the company in number of ways, this survey will help the company to evaluate strategies aimed at improving productivity, and results will be used in implementation of strategies that will improve job satisfaction. The job satisfaction level will provide a benchmark which will be used by the company to compare its mean value with other companies in the industry, and finally the results will be used in determining the most effective way to improve job satisfaction.

Correlation research:

Correlation research refers to research design aimed at determining the association of variables. Association is measured by the correlation value of two variables or a chi square association test can be undertaken to determine relationship of variables. A correlation value of one indicates strong positive relationship between variables while a correlation value of negative one indicates strong negative relationship between variables. Finally a correlation value of zero indicates that there exists no relationship between variables.

From the case study high correlation values are indicated in the relationship between overall and intrinsic variables with a correlation value of 0.463458. This value is positive and this means that as one variable increases then the other variable is also increasing. Negative correlation is evident between benefit and extrinsic variables whereby the value is -0.38097776 and this value mean that when one variable is increasing then the other variable is decreasing.

Correlation and causation:

Survey Results

Correlation and causation have two different meanings, for example correlation may be strong between the number of cars sold in a year and the average students exam score, the correlation value for example may be 0.9999 but this does not mean that the number of cars sold in a year will causes the average student exam score to increase or decrease. Therefore high correlation does not mean that one variable causes the other, this is because correlation may be strong between variables but one variable change does not cause the other variable to change.


John Freund (1997).Modern elementary statistics, New Jersey: Prentice Hall.