CPP450 Employee Stress SPSS Exercise
Assessment 4c: Employee stress SPSS exercise
Null hypothesis: There no is significant difference between three groups of employees
Alternative hypothesis: There is significant difference between three groups of employees
Table 1 Descriptive statistics
95% Confidence Interval for Mean
The result indicates a total observation in three groups N = 33, each group having N= 11 observations. Group 1 has a mean of Mean of 3.8182(2.183), different from group 2 with Mean of 4.00(1.732), different from group 3 with Mean 7.0(1.673).
Table 2: ANOVA
Sum of Squares
The ANOVA results above indicates a significant analysis variance between the three groups with p-value< 0.001 at 95% confidence interval. Therefore, the null hypothesis is rejected over the alternative hypothesis ‘H1: There is significant difference between three groups of employees.’ This means that stress level always varies among the employees based on the time frame, workload, employee-employer relationships, and the environment, Chen, Z.X. and Francesco, A.M. (2003, P. 490-510).
Assessment 5b Correlation coefficient test
Type of correlation
Sex: female or male (nominal)
There is strong positive relationship between the two variables
Table 4: Correlations results
The correlation result indicates that Sex has mean of 1.3636(0.50452), while employment 7.00(1,673). Sex has strong positive relationship with employees (r = 0.237, p =0.107). Male gender in employment sector, are more preferred as compared to female sector as result of the level of output male employee can render in the industry, Crant, J.M. (2000, P. 435-462).
Correlation, in its broadest sense, is an indicator of the strength of a relationship between variables. A change in the magnitude of one variable is associated with a change in the magnitude of another variable, or in the same (strong correlation) or inverse (negative correlation) path in correlated data. The word correlation is most widely used for the identification of a linear linear relationship between two variables, which is demonstrated as Pearson product-moment correlation, (Wackerly, et al,. 2008). For jointly normality of data, the Pearson correlation analysis is widely used also expressed as data that follow a bivariate normal distribution). A Spearman rank causal relationship can be used as an indicator of monotonic association for nonnormally distributed continuous data, ordinal data, or data with pertinent outliers. The two correlation coefficients are scaled from –1 to 1, where 0 indicates that there is no linear or monotonic association, and as the coefficient approaches an absolute value of 1, the relationship becomes stronger and eventually approaches a straight line (Pearson correlation) or a continuously increasing or decreasing curve. However, the covariance of the variables describes the degree to which a change in one random function is related with a modification in another continuous variable. Covariance is closely related to variance in that it explains the differences of a single variable, so even though variance defines the differences of two variables together, (Rodgers, et al,.1998). Hence, covariance is dependent on the variable measurement scale, and its ultimate magnitude cannot be easily interpreted or compared throughout studies. A Correlation analysis is commonly used to aid interpretation.
Chen, Z.X. and Francesco, A.M. (2003), “The relationship between components of
commitment andemployee performance in China”,Journal of Vocational Behavior,
Vol. 62 No. 3, pp. 490-510.
Crant, J.M. (2000), “Proactive behaviour in organizations”,Journal of Management, Vol. 26
No. 3,pp. 435-462.
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