Abstract
This is a quantitative paper that analyses the issues related to employee health and safety. The major focus is on the particulate matter, sound-exposure levels and how they relate to the health of employees in terms of how many annual sick days per employee and safety training expenditure and lost time. Particulate matter such as bacteria, dust, soot, viruses, asbestos etc. can cause the work environment not to be conducive health-wise resulting in sickness. Sound-exposure depending on the levels also has effect on health. Sickness of an employee can be injurious both to the affected person and the organization at large. It may result in demotivation among the employees and consequent low productivity. A data set comprising a sample of 103 job sites was considered where the particulate matter, sound-exposure level, safety training expenditure, lost time, and mean annual sick days are taken as the variables. A research problem is presented and research questions and hypotheses stated. They have guided the research design and the analysis of the results. The data analysis employed different techniques including correlation analysis, ANOVA and descriptive statistics.
Statistical Analysis of Data Set
According to Jilcha, K., Kitaw, D., & Beshah, B. (2016) World Health Organization considers the Health and Safety of workers a priority. In construction and manufacturing industries, more health issues and work-related injuries affect employees ( Jilcha, K., & Kitaw, D., 2017) . López-Alonso, M., Ibarrondo-Dávila, M. P., Rubio-Gámez, M.C., & Munoz, T.G. (2013) confirm that health issues and hazard can impact companies significantly. This is due to the fact that the associated illness and accidents cause employees to be absent from work.
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The dataset for this study comes from the occupational health and safety of workers and focuses on the particulate matter, sound-exposure levels (in decibels), average annual sick days per employee, safety training expenditure, and time lost. The analysis of the data is intended to promote occupational health and safety and to create a pleasant working environment for employees. The dataset is obtained from secondary data, which means the data was retrieved mainly from archives.
This is a quantitative study as it will be justified by data and numerical analysis. Through the study, an individual will be able to get an overall view of the data and as well the distribution of the variables. The study will involve conducting a correlation analysis. Through the analysis, the researcher will be able to comprehend the relation amid the dependent and independent variables within the dataset.
There are a number of different problems related to possible occupational injuries resulting from the workplace environment and atmosphere ( Jilcha, & Kitaw, 2017) . Exposure to materials such as particulate matter has registered significant health effects over time hence the desire for the company to avoid the same.
Research Problem
The problem of poor employee health in the workplace often results whenever the work environment is not conducive for the employees and often can lead to low motivation. Besides, the problem of low motivation at work is capable of resulting in higher employee turnover, reduced engagement levels among employees, dysfunctional system of communication, and reduced productivity. Such results of this particular problem have a higher likelihood of proliferating and turning the situation at the workplace into a toxic environment.
Research Objective
To establish the relationship between the size of particulate matter and the health of employees.
Research Hypothesis
The research problem identified above demand the formulation of research questions, hypothesis, and alternative hypothesis to conduct relevant research. Past studies have identified a permanent link between the workplace environment and overall employee health as well as the risks of exposure of the same ( Wenter, (2015) . The employees who work in different job sites face numerous risks when visiting worksites and hence the need to determine the possible effects of the factors that affect the safety and health of while identifying ways to minimize lawsuits based on health.
Research Questions:
Is there a relationship between the health-impacting factors such as particulate matter and sound levels and the exposed health of employees or safety training expenditure?
Null hypothesis: There is a relationship between health-impacting factors such as particulate matter and sound levels and the exposed health of employees or safety training expenditure.
Alternative hypothesis: There is no positive relationship between health-impacting factors such as particulate matter and sound levels and the exposed health of employees or safety training expenditure.
Research Method
The research method used is a quantitative approach with critical attention paid to the sampling of data, data analysis procedures, and interpretation. The study finds that using quantitative methods is appropriate based on the fact that each process involved reduces the risk of bias through standardization. As compared to qualitative or mixed methods of research, quantitative techniques within the study will be less time consuming and provide comprehensive insight with regards to the statistical data involved ( Pini, & Vantini, 2016) . The research will specifically adopt a correlation analysis as the most convenient and appropriate research method. This is because it is only through the correlation analysis will we understand the relationship between the dependent variable and the independent variables within the model (Cornell, 2018). Descriptive statistics will be employed to summarize the data from the sample using indexes like measures of central tendency, Anova, t-Test, correlation analysis, and regression analysis. The aim of using descriptive statistics is to test the relationship in the data set.
The Sample Study
The data focuses on factors that affect the work environment thus affecting the health of employees. Although some job sites require using protective gears such as respirators or ear muffs, there are various effects that occur depending on the job site or project that is being undertaken. For instance, there are PM that varies from 2.5 microns to 10 microns and can suspend in the air from minutes to hours, for example, pollen, asbestos, fly ash cement etc. Other PM having sizes that do not exceed 2.5 microns suspend in the air from hours to weeks for example viruses, bacteria, smog etc. PM that is below 2.5 microns tends to be more harmful than those that are above 10 microns because of their smaller sizes and the conditions are suitable to be easily inhaled. In addition, they can be inhaled and get into the deeper regions of the lungs thus possibly resulting in harmful health impacts. Therefore, analysis of the data dealing with factors affecting the safety and health of employees will be significant in understanding whether a relationship exists between the given variable and the health of employees. The data set for the study is gathered from 103 job sites. The unit of measurement used for the particulate matter is microns, the health of employees is given as the average annual sick days per year, sound-level exposure in decibels, lost time in hours.
The independent or predictor variable in the data set is the particulate matter, sound-exposure levels and safety training expenditure. PM is varied in terms of size in microns across the 103 job sites considered, the sound-exposure levels varied in terms of decibels and training expenditure in US dollars. The independent variable is the mean annual sick days per employee, which determines on average the number of days an employee is absent in a given job per year because of sickness and lost time due to time taken to train employees on safety procedures. The variables are varied across the 103 selected job sites with respect to how the independent variables are affected by the predictor variables.
Statistical Analyses
Descriptive statistics will be employed to analyze the data from the sample study using indexes like measures of central tendency and correlation analysis. The aim of using descriptive statistics is to test the relationship in the data set. Carrying out a correlation analysis is more effective since it yields conclusions that are convincing. This is because it will give the relationship between the dependent and independent variables (Pini & Vantini, 2016). It also designates the strength of the relationships, that is, whether weak or strong. In addition, it provides room to give policy which might help the organization in the future as well it is easy to undertake.
Correlation Analysis
Particulate Matter and Mean Annual Sick Days
Frequency distribution table.
| Class | Frequency- Microns | Frequency - Mean annual sick days |
| 2 | 15 | 1 |
| 4 | 17 | 6 |
| 6 | 22 | 31 |
| 8 | 33 | 42 |
| 10 | 16 | 19 |
| 12 | 0 | 4 |
| More | 0 | 0 |
Histogram.
Descriptive statistics table
| microns | mean annual sick days per employee | |
| Mean | 5.66 | 7.13 |
| Standard Error | 0.26 | 0.19 |
| Median | 6 | 7 |
| Mode | 8 | 7 |
| Standard Deviation | 2.59 | 1.89 |
| Sample Variance | 6.73 | 3.58 |
| Kurtosis | (0.85) | 0.12 |
| Skewness | (0.37) | 0.14 |
| Range | 9.8 | 10 |
| Minimum | 0.2 | 2 |
| Maximum | 10 | 12 |
| Sum | 582.7 | 734 |
| Count | 103 | 103 |
Safety training expenditure and lost time
|
Bin range |
Frequency |
|
40 |
5 |
|
80 |
15 |
|
120 |
27 |
|
160 |
32 |
|
200 |
51 |
|
240 |
44 |
|
280 |
28 |
|
320 |
15 |
|
360 |
6 |
| More |
0 |
Histogram.
Descriptive statistics table
safety training expenditure |
lost time hours |
|
| Mean | 595.98 | 188.00 |
| Standard Error | 31.48 | 4.80 |
| Median |
507.772 |
190 |
| Mode |
234 |
190 |
| Standard Deviation | 470.05 | 71.73 |
| Sample Variance | 220,948.85 | 5,144.54 |
| Kurtosis | 0.44 | (0.50) |
| Skewness | 0.95 | (0.08) |
| Range |
2251.404 |
350 |
| Minimum |
20.456 |
10 |
| Maximum |
2271.86 |
360 |
| Sum |
132904.517 |
41925 |
| Count |
223 |
223 |
Sound-Exposure Level
Descriptive statistics table
|
Decibel |
|
| Mean |
124.8359 |
| Standard Error |
0.177945 |
| Median |
125.721 |
| Mode |
127.315 |
| Standard Deviation |
6.898657 |
| Sample Variance |
47.59146 |
| Kurtosis |
-0.31419 |
| Skewness |
-0.41895 |
| Range |
37.607 |
| Minimum |
103.38 |
| Maximum |
140.987 |
| Sum |
187628.4 |
| Count |
1503 |
Hypothesis Testing
Hypothesis testing is an inference tool of a given hypothesis conducted on a given sample taken from a larger population. In this test, we get to know if or not the main hypothesis holds. Statistical analysts test a given hypothesis by measuring and examining a random sample that is under analysis.
Correlation: Hypothesis Testing
Excel output
|
SUMMARY OUTPUT
|
||||||||||
| Regression Statistics | ||||||||||
| Multiple R | 0.715984175 | |||||||||
| R Square | 0.512633345 | |||||||||
| Adjusted R Square | 0.507807931 | |||||||||
| Standard Error | 1.327783445 | |||||||||
| Observations | 103 | |||||||||
| ANOVA | ||||||
| df | SS | MS | F | Significance F | ||
| Regression | 1 | 187.2953229 | 187.2943 | 106.2361 | 1.89049E-17 | |
| Residual | 101 | 178.0638984 | 1.763019 | |||
| Total | 102 | 365.3592232 | ||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
| Intercept | 10.08144482 | 0.315156971 | 31.98864 | 0.000 | 9.456258185 | 10.70662 |
| Microns | -0.522376553 | 0.050681267 | -10.3072 | 1.88E-17 | -0.622914553 | -0.42183 |
It can be noted that there is a strong positive correlation, as indicated by the Pearson correlation coefficient (fr = -0.715984185) amid microns and annual sick days. As a result, an r 2 of 0.51261 occurs which explains the 51.26% of the difference amid the variables. Using a 0.05 significance level, the outcome shown is a p-value of 0.00<0.05. Therefore, there is enough evidence to reject the null hypothesis and fail to reject the alternative hypothesis.
| SUMMARY OUTPUT | ||||||
|
Regression Statistics |
||||||
| Multiple R |
0.939549 |
|||||
| R Square |
0.882762 |
|||||
| Adjusted R Square |
0.882251 |
|||||
| Standard Error |
24.61319 |
|||||
| Observations |
103 |
|||||
| ANOVA | ||||||
|
df |
SS |
MS |
F |
Significance F |
||
| Regression |
1 |
1008201 |
1008201 |
1664.211 |
7.71E-105 |
|
| Residual |
221 |
133884.8 |
605.813 |
|||
| Total |
222 |
1142088 |
||||
|
Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
Upper 95% |
|
| Intercept |
273.4493 |
2.665261 |
102.5975 |
2.1E-187 |
268.1967 |
278.701 |
| safety training expenditure |
-0.14327 |
0.003515 |
-40.7946 |
7.7E-104 |
-0.15019 |
-0.13643 |
|
Sound-Exposure μg/dL |
|
| Mean |
32.85713 |
| Variance |
150.4582 |
| Observations |
103 |
| Pearson Correlation |
0.992235 |
| Hypothesized Mean Difference |
0 |
| Df |
48 |
| t Stat |
-1.9297 |
| P(T<=t) one-tail |
0.029775 |
| t Critical one-tail |
1.677223 |
| P(T<=t) two-tail |
0.059552 |
| t Critical two-tail |
2.010636 |
Results
The measures of central tendencies provide significant data. The mean indicates the center of the data by revealing the most typical value in a group of data. From the results, 5.66 was the average micron whereas 7.13 was the mean annual sick days per employee. 6 was the median micron and 7 the median mean annual sick days per employee. 8 represented the mode micron and 7 the modal mean annual sick days per employee.
The standard deviation shows the dispersion of data from the mean. For the mean annual sick days per employee and microns, the standard deviation was 1.89 and 2.59 respectively. The variance for the microns and mean annual sick days per employee was 6.73 and 3.58 respectively. The distribution of data is indicated by the coefficient of skewness with respect to the normal, in other words, the coefficient of skewness reveals the shape of the distribution of the data relative to the normal curve. From the results, the microns and mean annual sick days recorded a skewness of -0.37 and 0.14 respectively. This shows that for the microns the data points are skewed negatively while for the mean annual sick days per employee they are skewed positively.
For the two variables, the histograms tend to nearly presume a normal distribution. In addition, the frequency table shows that there are no outliers and the variables presume ration scale of measurement.
Conclusions
The statistical analysis results revealed that there is an association amid particulate matter and the working environment health condition. This implies that there is a relatively strong relationship between the work environment and the health of employees due to particulate matter in the air. This could result in respiratory-related health issues and other illness to some degree. It is, therefore, relative to see employees suffering from respiratory-related health problems at a job site due to these particles. It is therefore suggested that employees take extra caution at the site and should cover their noses at all times to avoid these ailments. The company should provide materials that employees can use to cover their noses while at work.
It is recommended that workplace health and safety of employees should come at the forefront of the agenda of a company. This is because it improves the work rate of employees because they can work efficiently without the fear of being sick or injured. Statistically, it is proven that particulate matter has a direct relation to respiratory problems at work. It is recommended that the company takes measures of reducing the effect of PM on employees. It is also cost-effective to prevent workers from being injured in the workplace than treatment. It is also suitable for the business if every employee is healthy because then everyone would be working. It ensures that the organization utilizes its human resources fully, and this will give the business return on investment.
References
Cornell, B. (2018). What is the Alternative Hypothesis to Market Efficiency? SSRN Electronic
Jilcha, K., & Kitaw, D. (2017). Industrial occupational safety and health innovation for sustainable development. Engineering Science and Technology, an International Journal , 20 (1), 372-380. doi:10.1016/j.jestch.2016.10.011
Jilcha, K., Kitaw, D., & Beshah, B. (2016). Workplace innovation influence on occupational safety and health. African Journal of Science, Technology, Innovation and Development , 8 (1), 33-42. doi:10.1080/20421338.2015.1128044
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Linguistic Theory , 3 (7), 295. doi: 10.3765/salt.v0i0.2776
López-Alonso, M., Ibarrondo-Dávila, M. P., Rubio-Gámez, M.C., & Munoz, T.G. (2013). The impact of health and safety investment on construction company costs. Safety Science , 60 , 151-159. doi:10.1016/j.ssci.2013.06.013
Pini, A., & Vantini, S. (2016). The interval testing procedure: a general framework for inference in functional data analysis. Biometrics , 72 (3), 835-845.
Wenter, Y. (2015). What Does the Strongest Meaning Hypothesis Mean?. Semantics And Linguistic Theory , 3 (7), 295. doi: 10.3765/salt.v0i0.2776
