Injuries In Work Places - Essay Example

? WEEK 4 APPLICATION Introduction The rate of injuries in work places is very common especially in environments where workers do not observe adequatesafety measures. The rate of injury is even worse where the supervisors do not monitor the behaviors of the workers when performing their tasks. MAs it would be expected, the lack of observance of safe behaviors in work places is expected to increase the rate of injuries (Woods, 1975). As such, the role of the supervisors in shaping the behaviors of workers in the workplace is very important as they can help reduce the injury rate. The roles of supervision are played by both men and women. In fact, women have become more instrumental in supervisory roles, and the question whether they can perform so well as their men counterparts is of interest. Women are gradually shifting from their traditional roles and they are embracing production management jobs, which are somewhat difficult and complicated. As expected, women supervisors may face some difficulties that are not faced by men supervisors. Some of the factors that could lead to this observation is the lack of aggressiveness and lack authorotativeness among others. In view of these (Patton, 1999). Observations, the purpose of this paper is to carry out a statistical investigation to establish whether men supervisors can help reduce injury rate than their women counterparts, and whether observance of safe behaviors in workplaces has some role in the injury rate reduction. The paper will start with a brief descriptive analysis of the variables and then delve into hypothesis testing using t-tests. Table 1: Frequency of percentage of per safe behavior From the frequency table above, it is clear that the percentage of safe behavior is most frequent from 89 per cent to 100 percent. Ideally, less workers are observed with percentage of less than 89 per cent. Table 2: frequency of females From the figure 2 above, the number of female supervisors is slightly more than their male counterparts Figure 3: frequency of injury rate. From figure 3, the injury rate is most frequent at the rate of zero. Other frequent rates include 8.74, 9.62, 16.03, 22.89 and 26.22. Table 1: Descriptives N Minimum Maximum Mean Std. Deviation % of Per Safe behavoir 51 .42 1.00 .8655 .13891 Female 51 0 1 .53 .504 Injury Rate 51 .00 76.92 15.1755 17.47447 Valid N (listwise) 51 Table 1 shows the combined description of the three variables. The most important observation in this table is that the percentages of safe behavior have a minimum of 42% . A maximum of 100% and an average of 86.55%. Therefore, a score of less than 86.55% can be considered poor. The injury rate has an average of 15.18% and has a greater rate of deviation from mean than the % of per safe behavior. T-test The following is the appropriate null and alternate hypothesis of the test. Pair 1 H0: Injury rate does not have any relationship with the % of per safe behavior H1: Injury rate has a relationship with the % of per safe behavior Pair 2 H0: The sex of the supervisor does not determine the injury rate H1: The sex of the supervisor has some role in determining the injury rate Table 2: paired sample statistics Mean N Std. Deviation Std. Error Mean Pair 1 Injury Rate 15.1755 51 17.47447 2.44692 % of Per Safe behavoir .8655 51 .13891 .01945 Pair 2 Injury Rate 15.1755 51 17.47447 2.44692 Female .53 51 .504 .071 Table 3: Paired samples correlation N Correlation Sig. Pair 1 Injury Rate & % of Per Safe behavoir 51 .035 .808 Pair 2 Injury Rate & Female 51 .090 .532 Table 4: Paired Samples test Paired Samples Test Paired Differences t df Sig. (2-tailed) Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pair 1 Injury Rate - % of Per Safe behavoir 14.31000 17.47017 2.44631 9.39644 19.22356 5.850 50 .000 Pair 2 Injury Rate - Female 14.64608 17.43656 2.44161 9.74197 19.55019 5.999 50 .000 The correlation coefficient in table 3 indicates that as the number of females and the % of per safe behavior increases, the rate injury increases rather unpredictably. The statistics in table confirm what had been determined in the descriptive statistics in the earlier section. The two-tailed p-value of less than 0.01 for both pairs. This value is, by far, less than the standard ? of 0.05. This leads us to rejection of the null hypothesis and and acceptance of the alternate hypothesis. Therefore, there is significant evidence to conclude that the injury rate has some relationship with the % of per safe behavior and that the sex of the supervisor has some role in determining the injury rate. Conclusion In view of this study, the t-test has come out very useful in testing the hypothesis and hence led us to the conclusion that injury rate can be influenced by the % of per safe behavior and gender. Specifically, we have found out that women are poorer than their male counterpart, in reducing injury rates and that observance of safe behaviors in workplaces can help reduce injury rates. References Woods, M.M. (1975). What does it take for a woman to make it in management. Personal Journal, 54 NI, pp. 56-89. Patton J.A. (1999). The first line supervisor: industries number one problem. Business management, 5 (8), pp. 18-19 Read More