Free Analyzing SPSS (Pasw) Software: Part 2 Essay Example
Workplace safety is the welfare, health and security concern of an employee in the course of carrying out their occupational activities. There are a number of factors that contribute to injury in the workplace, some of the factors are environmental which mainly is determined by the kind of work schedule of the employee and the nature of supervision of the organization. The type of work also is a contributing factor depending on the exposure to hazards by the employees. Personal factors that the injury rate include age, gender and the health status of the employee. The individual factors of the employee naturally contribute to the fatigue, drowsiness and stress of the employee (Dembe et al., 2005).
According to (Dembe et al., 2005), exposure of a worker to safety and health hazards and inappropriate supervision are some of the leading factors that cause stress to an employee. Stress is predictably one of the factors that directly impact the injury rate of employees. This is because stress will directly impact how an individual behaves. The injury that an employee is exposed can be assessed in several ways depending on the frequency of the injury and its severity. It is expected that sleepiness of the employee contributes to behavioral decrements in the workplace which may lead to an increase in the injury rate of that particular employee or his/her colleagues (DeArmond & Chen, 2009).
In the article “Occupational Safety: The Role of Workplace Sleepiness”, DeArmond & Chen, 2009 explain that sleepiness is often a factor in the decrement of behavior at work. Sleepiness is one of the consequences of insomnia and is usually caused by unpredictable sleeping patterns. Therefore, stress and lack of enough sleep are some of the factors that can lead to increase in injuries rates at the workplace. Among the possible factors that cause stress are poor supervision of employees and increased workload.
Safety climate is the employee’s perception of the supervisor’s priority in terms of safety and health hazards of the employee at the workplace. The objective of this study is to determine if there is a relationship between the safety climate on all three locations and the injury rate reported by the employees. An extension of this study will be conducted so as to determine the association between the number of hours worked and the injury rate. The outcomes of this study will be used to improve the existing safety programs at the workplace.
Is there a significant association between safety climate and the injury rate on all the three locations?
The null hypothesis HO: Safety climate does not significantly predict the injury rate.
The alternative hypothesis H1: Safety climate significantly predicts injury rate.
Descriptive Statistics for Study Variables (N =51)
*N in the descriptive statistics table represents the number of supervisors who completely took part in the study.
*Hours Worked- This variable represents the actual number of hours that all the employees in the team worked for the period under consideration.
*Safety Climate- This variable quantifies the perception of the employees on the safety and health priorities of the supervisor in the 12 month period. This variable was measured quarterly.
*Injury rate- This variable captures the mean injury rate for every 100 employees in the 12 month period.
The mean safety climate is calculated to be 4.697 and its variance is 1.03492. The range of the variable safety climate is 4.3. The value of the standard deviation 1.0349 indicates that the score of the employees on the safety climate can be considered to normally range from 3.66 up to 5.73.
The average value of the variable hours worked is obtained to be 49960.78 and a variance of 15590.242. The range of the variable hours worked is 83200, and this represents the difference between the team that worked the highest number of hours and the team that worked the least number of hours. The value of the standard deviation 15590.24 implies that teams whose number of working hours are in the range of 34370.4 and 65551.02 during the 12 month period are considered to be within the expected limits.
The average injury rate of employees during the 12 month period was 15.176 and the standard deviation of the variable injury rate was 17.475 and a range of 76.923. The standard deviation value implies that injury rates reported by the employees that are within the range of -2.299 and 32.651 are considered to be within the expected limits. The variance of the variable injury rate is obtained to be 305.36
Linear regression analysis was conducted to determine if there is a significant relationship between the safety climate and the injury rate of employees in the 12-month period. Safety climate is the independent variable and we will be using it to predict injury rate which is the dependent variable. The descriptive of the two variables is included in the table 1. We will be interested in conducting a parametric test to determine if the relationship between safety climate and injury rate is significant. The F-statistic for the linear regression model is obtained from the ANOVA table to be, F (1, 49) = 0.08, p = 0.93 > 0.05. Therefore, the null hypothesis is accepted and the alternative hypothesis is rejected. Based on the results of the analysis it is concluded that the safety climate does not have an effect on the injury rate of employee
The adjusted R-square value = -0.02 is an estimation of how much the variance of a dependent variable in this case the injury rate can be explained by the safety climate that is the independent variable. Therefore, 2% of the entire variability in the variable injury rate can be explained by the safety climate.
A line graph of the average injury rate verses the safety climate is included below.
The p-value 0.930 > 0.05 and it shows that the regression model is statistically insignificant in predicting the outcome variable. The confidence interval of the variable safety climate has intervals ranging from -0.089 to 0.930.
The minimum values observed in the graph are within the limits defined in the explanation of the descriptive of the variable injury rate. This is because all the values are above -2.299. However, there are values which exceed our upper interval which was defined to be 32.651. These values coincide with the safety climate values 3.5, 4.97, 5.2 and 5.8 and the values may be considered to be outliers. The linear graph of the average injury rate verses the safety climate does not indicate a distinct linear relationship between the variables and as such it is impossible to define a standardized regression equation which could be used to predict the injury rate.
Figure 2: Pie chart of the hours worked for the different manufacturing sites.
Employees in the manufacturing site phoenix worked more than any of the other manufacturing sites and the represent 37.55%. They were closely followed by employees in the Seattle manufacturing site and they represent 34.78% of the entire annual hours which is computed to be 886,080 hours. Boston manufacturing location had the least number of hours and it constituted 27.67% which make up 705,120 hours of the entire number of hours worked for the 12 month period.
The test is insignificant and as such the null hypothesis is accepted and the alternative hypothesis is rejected. Based on the results of the analysis it is concluded that the safety climate does not have an effect on the injury rate of employee
DeArmond, S., & Chen, P., (2009). Occupational safety: the role of workplace sleepiness. Accid Anal Prev. 41 (5) Retrieved from: http://www.ncbi.nlm.nih.gov/pubmed/19664435
Dembe, A, E, Erickson, J, B, Delbos, R G & Banks, S, M. (2005). The impact of overtime and long work hours on occupational injuries and illnesses: new evidence from the United States. Occup Environ Med. 62 (9) Retrieved from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1741083/pdf/v062p00588.pdf