Sun Coast’s Research 7
 
 
Columbia Southern University
MBA 5652, Research Methods
 
May 17, 2022
 
 
Table of Contents
Introduction. 5
Statement of Problems. 5
Particulate Matter (PM) 5
Safety Training Effectiveness. 6
Sound-Level Exposure. 6
New Employee Training. 6
Lead Exposure. 7
Return-On-Investment 7
Literature Review.. 7
Particulate Matter (PM) Article. 7
Safety Training Effectiveness Article. 8
Sound-Level Exposure Article. 9
New Employee Training Article. 9
Lead Exposure Article. 9
Return on Investment Article. 10
Research Objectives, Research Questions, and Hypotheses. 10
Research Methodology, Design, and Methods. 13
Research Methodology. 13
Research Design. 13
Research Methods. 14
Data Collection Methods. 14
Sampling Design. 14
Data Analysis Procedures. 15
   Correlation: Descriptive Statistics and Assumption Testing. 16
Simple Regression: Descriptive Statistics and Assumption Testing. 18
Multiple Regression: Descriptive Statistics and Assumption Testing. 20
Independent Samples t Test: Descriptive Statistics and Assumption Testing. 23
Dependent Samples (Paired-Samples) t Test: Descriptive Statistics and Assumption Testing  26
ANOVA: Descriptive Statistics and Assumption Testing. 30
Data Analysis: Hypothesis Testing. 33
Independent Samples tTest: Hypothesis Testing. 34
Dependent Samples (Paired Samples) tTest: Hypothesis Testing. 35
ANOVA: Hypothesis Testing. 36
Findings. 37
Recommendations. 38
 
 
 
Executive Summary
Sun Coast Remediation Services is plagued with six business problems that can affect employee’s health and the well-being of the company. They have requested research to solve and better understand the base of their problems. Sun Coast collected data about their concerns but never utilized the information. This research paper aims to provide Sun Coast with requested information that will be beneficial to the company as well as their employees. The paper will include correlation, simple regression, multiple regression analysis. Theory and hypotheses will be tested. Simply, this research paper was completed using quantitative research that will provide reliable results. Findings and recommendations are included.
 
 
 

Introduction

Senior leadership at Sun Coast has identified several areas for concern that they believe could be solved using business research methods. The previous director was tasked with conducting research to help provide information to make decisions about these issues. Although data were collected, the project was never completed. Senior leadership is interested in seeing the project through to fruition. The following is the completion of that project, and includes statement of the problems, literature review, research objectives, research questions and hypotheses, research methodology, design, and methods, data analysis, findings, and recommendations.

Statement of Problems

Six business problems were identified:

Particulate Matter (PM)

There is a concern that job-site particle pollution is adversely impacting employee health. Although respirators are required in certain environments, particulate matter (PM) varies in size depending on the project and job site. PM between 10 and 2.5 microns can float in the air for minutes to hours (e.g. asbestos, mold spores, pollen, cement dust, fly ash), while PM less than 2.5 microns can float in the air for hours to weeks (e.g. bacteria, viruses, oil smoke, smog, soot). Due to the smaller size of PM less than 2.5 microns, it is potentially more harmful than PM between 10 and 2.5 since the conditions are more suitable for inhalation. PM less than 2.5 are also able to be inhaled into the deeper regions of the lungs, potentially causing more deleterious health effects. It would be helpful to understand if there is a relationship between PM size and employee health. PM air quality data have been collected from 103 job sites, which is recorded in microns. Data are also available for average annual sick days per employee per job-site.

Safety Training Effectiveness

Health and Safety training is conducted for each new contract that is awarded to Sun Coast. Data for training expenditures and lost-time hours were collected from 223 contracts. It would be valuable to know if training has been successful in reducing lost-time hours and, if so, how to predict lost-time hours from training expenditures.

Sound-Level Exposure

Sun Coast’s contracts generally involve work in noisy environments due to a variety of heavy equipment being used for both remediation and the clients’ ongoing operations on the job sites. Standard earplugs are adequate to protect employee hearing if the decibel levels are less than 120 decibels (dB). For environments with noise-levels exceeding 120 dB, more advanced and expensive hearing protection is required, such as earmuffs. Historical data have been collected from 1,503 contracts for several variables that are believed to contribute to excessive dB levels. It would be important if these data could be used to predict the dB levels of work environments before placing employees on-site for future contracts. This would help the safety department plan for procurement of appropriate ear protection for employees.

New Employee Training

All new Sun Coast employees participate in general health and safety training. The training program was revamped and implemented six months ago. Upon completion of the training programs the employees are tested on their knowledge. Test data are available for two Groups; Group A employees who participated in the prior training program, and Group B employees who participated in the revised training program. It is necessary to know if the revised training program is more effective than the prior training program.

Lead Exposure

Employees working on job sites to remediate lead must be monitored. Lead levels in blood are measured as micrograms of lead per deciliter of blood (μg/dL). A base-line blood test is taken pre-exposure and post-exposure at the conclusion of the remediation. Data are available for 49 employees who recently concluded a two-year-long lead remediation project. It is necessary to determine if blood lead levels have increased.

Return-On-Investment

Sun Coast offers four lines-of-service to their customers, including air monitoring, soil remediation, water reclamation, and health and safety training. Sun Coast would like to know if each line of service offers the same return-on-investment. Return-on-investment data are available for air monitoring, soil remediation, water reclamation, and health and safety training projects. If return-on-investment is not the same for all lines of service, it would be helpful to know where differences exist.

Literature Review

This literature review was obtained by reviewing and analyzing peer-reviewed scholarly articles related to Sun Coast’s six problems. The six problems are particulate matter, safety training effectiveness, sound-level exposure, new employee training, lead exposure, and return on investment. This literature review will provide evidence on how Sun Coast’s problems may directly impact the company and its employees.

Particulate Matter (PM) Article

Walter Crinnion was a pioneer is environmental medicine.  According to Crinnion(2017), particulate matter contributes to air pollution and is also a factor in major diseases. Particulate matter (PM) contains solid particles such as smoke and dust. Of great importance, PM absorbs toxic chemicals such as volatile organic compounds and polycyclic aromatic hydrocarbons, which all contribute to their harmful effects. People living in urban areas, especially those working on busy roads, have higher chances of risk from neoplastic disease, cardiovascular disease, and respiratory complications. This position is further supported by the United States Environmental Protection Agency (EPA) through its report titled “Health and Environmental Effects of Particulate Matter (PM).”Evidence shows that the size of particles is linked to effects on health (EPA, 2021). It is beneficial to Sun Coast to understand that particles measuring below 10 micrometers in diameter cause the greatest problems because they can get into the lungs and bloodstream. Exposure to small PM cause premature death, heart attacks, and aggravated asthma.

Safety Training Effectiveness Article

Sinelnikov, Emily, and Alaina (2020) researched to determine the effectiveness of workplace safety training interventions. The authors work with the National Safety Council in Itasca, Illinois. The systematic review involved published articles from 2000 to 2019. According to 22 peer-reviewed journals, training interventions help improve workplace safety. There is strong evidence that training workers positively affect their behavior. The study shows that Sun Coast should train its workers on accident reduction. The safety climate in the company will become positive over time upon implementation of safety training. Safety communication and work pressure improve; this shows the positive impact of training workers on safety. Training interventions should be based on the company’s need sand assessed through safety appraisal processes. Intervention should be embedded into the business procedures and processes.
 
 
 
 

Sound-Level Exposure Article

Tang, Burlutsky, and Mitchell (2021) conducted a research study to determine workplace noise exposure and the prevalence of age-related hearing loss. These authors are affiliated with the Centre of Vision Research. Their study involved 1923 participants aged above fifty years with occupational noise exposure. Exposure to loud noise for more than ten years increased the chances of hearing loss by 35.5 %. However, prior occupation noise exposure is not associated with the progression of hearing loss. The noise increases the risk of hearing loss in adults. This finding brings attention to the importance of preventive measures Sun Coast could take to reduce noise exposure in the workplace.

New Employee Training Article

Jorgensen is a lean, health, and safety consultant for Vestergaard Company in Denmark. Dyreborg works for the National Research Centre for the Working Environment in Demark. Jorgensen and Dyreborg(2021) explore the importance of induction safety training for 33 workers in the metal work sector. The analysis shows that training new employees are more than giving information about safety. The results point out that safety learning is critical, and workers should be inducted to engage in correct practices at the workplace. Without training, new workers are exposed to risks and injuries. Sun Coast should not only be concerned with new employee training but also focus on continued training of senior employees.

Lead Exposure Article

Lead exposure leads to toxicity, an environmental disease that has devastating effects on the human body. All body functions are affected by lead toxicity. Saleem is affiliated with the Department of Medicine in Pakistan and Qayyum affiliated with Islamic International University, Department of Pathology(2019), the pair conducted a study to determine the effects of lead exposure on children of workers living in lead-contaminated regions. The quantitative study involved 246 children aged between 1 and 6 years living in Pakistan industrial area. The clinic and demographic data of the participants were collected. Blood analysis was conducted, and biochemical tests of the renal profile were analyzed. The children exposed to lead poisoning had high BLLs compared to those living in a clean and lead-free environment. Renal functions were impaired in lead-exposed participants. This means that children living in lead-exposed areas have a high frequency of poisoning.

Return on Investment Article

Workplace injuries and fatalities cost companies millions of dollars. In 2021, according to the Workplace Safety Index, Liberty Mutual approximated that companies pay more than $1 billion per month for workers’ compensation. Moreover, “National Safety Council” estimated that work-related injuries and deaths cost companies and the nation above $170 billion in 2019(OSHA, 2021). Public health worker Rebecca Masters (2017), conducted a search to identify studies that calculated a ROI for public health interventions in high income countries.  Results show that the media for public health interventions was 14.3 to 1 which entails that public health interventions are highly cost-saving.
 

Research Objectives, Research Questions, and Hypotheses

Within Sun Coast Company, there are some areas of great concern that need to be addressed or resolved to improve the company performance. The problems identified are exposure to particulate matter, safety training effectiveness, exposure to noise, new employee training, lead exposure and return on investment. This study aims at giving the right protection to workers at Sun Coast according to OSHA standards.
RO1: Determine if there is a relationship between PM size and employee health.
RQ1: Is there a relationship between particulate matter size and employee sick days?
H01: There is no statistically significant relationship between particulate matter size and employee sick days.
HA1: There is a statistically significant relationship between particulate matter size and employee sick days.
 
RO2: Predict lost-time hours from training expenditures
RQ2. Is there a predictive relationship between safety training expenditure and lost-time hours?
H02.There is no statistically significant relationship between safety training expenditure and lost-time hours.
HA2. There is a statistically significant relationship between safety training expenditure and lost-time hours.
 
RO3: Predict the dB relationship between frequencies, angle in degrees, chord length, velocity, and displacement and the decibel level.
RQ3: Is there a predictive relationship between frequency, angle in degrees, chord length, velocity, and displacement and decibel level?
H03: There is no statistically significant relationship between frequency, angle in degrees, chord length, velocity, and displacement and decibel level.
HA3: There is a statistically significant relationship between frequency, angle in degrees, chord length, velocity, and displacement and decibel level.
 
RO4: Determine if revised program more effective than the prior program.
RQ4: Is there a difference in the effectiveness of the revised program versus prior program?
H04: There is no statistically significant evidence of the effectiveness of revised program versus prior program.
HA4: There is statistically significant evidence of the effectiveness of revised program versus prior program.
 
RO5: Determine if employee blood lead levels have increased.
RQ5: Have employee blood lead levels increased from their pre-exposure baseline measurements?
H05: There is no statistically significant difference in employee blood lead levels between pre-exposure and post-exposure.
HA5: There is a statistically significant difference in employee blood lead levels between pre-exposure and post-exposure.
 
RO6: Determine if there is a difference in return on investment between each line of service.
RQ6: Is there a difference in return on investment between each line of service?
H06: There is no statistically significant connection with return on investment between each line of service.
HA6: There is statistically significant connection with return on investment between each line of service.
 

Research Methodology, Design, and Methods

 
Research methodology basically focus on the how you will do research. There are three categories of research methodology: qualitative, quantitative and mixed methods. Research design is like a map that will help you find the answers to your research questions. Research methods specifies what procedures will be used based on methodology and design (Creswell, 2017). Below you will find more information on how the research methodology, design and methods will be used to help Sun Coast.

Research Methodology

Sun Coast collected a lot of data and wants it tested. My choices of research methodologies are qualitative which uses words, quantitative uses numbers or mixed methods which are a combination of qualitative and quantitative research. Quantitative research will be used to complete this research. Quantitative research was chosen because there are numbers involved. It can be either experimental or non-experimental.  Most importantly quantitative research should be used when a theory or hypothesis needs testing.

Research Design

The research design is descriptive; it aims at establishing a pattern regarding the topic of study. Research design help to establish the relationship between workplace safety and operating cost. The factors to consider include impact of lead exposure, induction training, noise exposure, safety training, and exposure to particulate matter on operating cost. The descriptive nature of the research will help address the research questions and hypothesis. The pattern obtained by the descriptive design will show how the research topic is relevant to workers’ productivity.
 

Research Methods

The suitable research method for this topic is correlation and descriptive statistics. Correction method is important explaining the topic. The methods allow two variables to be compared and to develop a conclusion. Descriptive statistics explain how health and safety of the workers is impacted by the six variables. The descriptive statistics will explain how workplace safety is important in influencing productivity.

Data Collection Methods

Data is collected by observation. This involves the researcher making observations on the variables. Observation research technique is flexible (Jensen, 2017). Before undertaking structured research the researcher conducted observations to form research questions. This is the basis of the descriptive research. The data regarding safety and health of workers is used in this research. The data observed are based on workers’ productivity and the level of safety put in place by Sun Coast Company. Observing the data gives a hint of how the company is operating. The researcher chose observation because of its strong validity. However, there are problems with generalization and reliability.

Sampling Design

Sampling is done using convenience sampling procedure. This method is effective because of accessibility of data needed in the research. There are accessible data sets available and the same is used for sampling. Convenience sampling enables the research topic to be well researched and will help in understanding health and safety in Sun Coast.
 

Data Analysis Procedures

Sun Coast’s research hypotheses will be tested using a variety of data analysis procedures, including correlation, simple regression, multiple regression, independent samples t-test, paired-samples t-test, and one-way ANOVA.
Correlation will be used to test H01 since the research question is whether relationship exists between the variables of particulate matter size and employee sick days.
Simple-regression analysis will be used to test H02 since the research question is whether there is a predictive relationship between the variables safety training expenditure and lost-time hours. Simple regression is used to predict the dependent variable from a single independent variable.
Multiple-regression analysis will be used to test H03 since the research question is whether a predictive relationship exists between the variables’ frequency, angle in degrees, chord length, velocity, displacement, and decibel level. Multiple regression is used to predict the dependent variable from two or more independent variables.
Independent samples t-test will be used to test H04 since the research question is whether the revised new employee training program more effective than the prior training program. Independent samples t-test is used to test for statistical differences between two unrelated (independent) means.
Paired samples t-test will be used to test H05 since the research question is whether employee blood lead levels have increased from their pre-exposure baseline measurements. Paired samples t-test is used to test for statistical differences between two related (dependent) means.
One-way ANOVA will be used to test H06 since the research question is whether there are differences in return on investment between air monitoring, soil remediation, water reclamation, and health and safety training? ANOVA is used to test for statistical differences between more than two means.

Data Analysis: Descriptive Statistics and Assumption Testing

Correlation: Descriptive Statistics and Assumption Testing
Frequency Distribution Table
 

Sick days Frequency
2 1
3 1
4 5
5 13
6 18
7 24
8 18
9 12
10 7
11 2
More 2

 
Histogram
 
 
 
Descriptive Statistics Table
 

Mean Annual Sick days per employee  
   
Mean 7.126213592
Standard Error 0.186483898
Median 7
Mode 7
Standard Deviation 1.892604864
Sample Variance 3.58195317
Kurtosis 0.124922603
Skewness 0.142249784
Range 10
Minimum 2
Maximum 12
Sum 734
Count 103

 
Measurement Scale
The measurement scale used in the above data is ratio scale because the exact values between the data can be determined.
Measure of Central Tendency
The mean, median and mode of the data are identical at 7 implying that the data is perfectly identical.
Skewness and Kurtosis
The skewness of mean annual sick days per employee is 0.142 and is within the range of 0.5 and 1 implying that the data is moderately skewed. On the other hand the kurtosis of the data is 0.1249 and falls within the acceptable region because the curve is not highly picked.
Evaluation
The histogram of the above data is normally distributed and the frequency does not show outliers.  The mean, median and more are closely aligned implying normalcy. The skewness and the kurtosis are within the acceptable range of -2 and + 2. The results of the descriptive statistics show that the assumption of the parametric statistics has been met.
 

Simple Regression: Descriptive Statistics and Assumption Testing

Frequency Distribution Table
.

Lost Time in Hours Frequency
10 1
35 1
60 9
85 9
110 17
135 18
160 24
185 27
210 37
235 24
260 21
285 15
310 12
335 4
More 4

 
 
Histogram
 
Descriptive Statistics Table
 

Lost Time in Hours
   
Mean 188.0044843
Standard Error 4.803089447
Median 190
Mode 190
Standard Deviation 71.72542099
Sample Variance 5144.536016
Kurtosis -0.501223533
Skewness -0.081984874
Range 350
Minimum 10
Maximum 360
Sum 41925
Count 223

 
Measurement Scale
The lost time per member of contract is ratio data because the data can be classified and ordered.
Measure of Central Tendency
The mean, median and the mode of the data is 188.00, 190 and 190 respectively. These values are closely aligned implying the data is normally distributed.
 
Skewness and Kurtosis
The skewness and kurtosis of the data is within the acceptable region because the values lies between -2 and + 2.
Evaluation
From the descriptive statistics table the three measures of central tendency are close to each other indicating the normalcy of the data. On the other hand the skewness and the kurtosis of the data is within the acceptable range of -2 and + 2 which shows that the data is normally distributed. These results confirm that the assumption of the parametric statistics has been met.
 

Multiple Regression: Descriptive Statistics and Assumption Testing

Frequency Distribution Table

Decibel Frequency
103.38 1
104.37 2
105.36 1
106.35 3
107.34 6
108.33 6
109.32 9
110.31 12
111.30 18
112.29 17
113.28 26
114.27 22
115.26 27
116.25 47
117.24 36
118.22 44
119.21 47
120.20 53
121.19 61
122.18 60
123.17 62
124.16 74
125.15 70
126.14 81
127.13 93
128.12 73
129.11 105
130.10 80
131.09 88
132.08 67
133.07 50
134.06 56
135.05 35
136.04 30
137.03 19
138.02 7
139.01 8
140.00 5
More 2

 
Histogram
 
Descriptive Statistics Table

Decibel
   
Mean 124.83594
Standard Error 0.1779447
Median 125.721
Mode 127.315
Standard Deviation 6.8986566
Sample Variance 47.591463
Kurtosis -0.3141873
Skewness -0.4189522
Range 37.607
Minimum 103.38
Maximum 140.987
Sum 187628.42
Count 1503

 
 
Measurement Scale
The measurement scale of decibel is ratio data because the data has meaningful distance between the data values.
Measure of Central Tendency
The mean, median and mode of the data are closely aligned implying normalcy. The results are shown in the table of descriptive statistics above.
Skewness and Kurtosis
The skewness and the kurtosis of the data is within the acceptable region of -2 and + 2. This indicates that the data is normally distributed.
Evaluation
The skewness and the kurtosis of the data show normalcy since the values lies between -2 and +2. Additionally the mean, median and the mode are closely aligned which is a sign of normal distribution. The normalcy of the data indicates that the assumption of parametric statistics has been met.

Independent Samples t Test: Descriptive Statistics and Assumption Testing

Frequency Distribution Table
 

Group A Prior to Training Frequency
50 4
55.86 5
61.71 7
67.57 8
73.43 14
79.29 10
85.14 8
More 6

 

Group B prior to Training Frequency
75 2
78.14 5
81.29 10
84.43 12
87.57 14
90.71 11
93.86 5
More 3

 
Histogram
 
 
 
 
Descriptive Statistics Table
 

Group A prior to Training  
   
Mean 69.790323
Standard Error 1.4027881
Median 70
Mode 80
Standard Deviation 11.045564
Sample Variance 122.00449
Kurtosis -0.776676
Skewness -0.0867981
Range 41
Minimum 50
Maximum 91
Sum 4327
Count 62

 

Group B prior to Training  
   
Mean 84.7741935
Standard Error 0.65947889
Median 85
Mode 85
Standard Deviation 5.19274195
Sample Variance 26.964569
Kurtosis -0.3525379
Skewness 0.14408453
Range 22
Minimum 75
Maximum 97
Sum 5256
Count 62

 
 
Measurement Scale
The measurement scale used is ratio because the data can be classified and ordered.
Measure of Central Tendency
The mean, median and mode of group A before training are 69.79, 70 and 80. Since the range of the measure of central tendency is small it indicates that the data is normally distributed. On the other hand, mean median and mode of group B before training is 84.79, 85 and 85. This results shows that there is normality.
Skewness and Kurtosis
Skewness and kurtosis are important statistical tools of measurement that are used determine the distribution of data or the shape of the distribution curve. The skewness and kurtosis of the data for group A is -0.776676 and -0.0867981 respectively. According to George and Mallery, (2018), a normally distributed data will have skewness and kurtosis of -2 and +2. As such the data set is normally distributed because if falls within the acceptable range.
Evaluation
From the descriptive statistics table, the central measure of tendency are closely aligned indicate the normality of the data set. Additionally, the skewness and kurtosis are within the range of -2 and + 2 showing that the data is normally distributed. These results confirm the assumptions of the parametric test were met.

Dependent Samples (Paired-Samples) t Test: Descriptive Statistics and Assumption Testing

Frequency Distribution Table
 

Pre-Exposureµg/DL Frequency
6 1
13.14 3
20.29 5
27.43 6
34.57 8
41.71 13
48.86 9
More 4

 

Post-Exposureµg/DL Frequency
6 1
13.14 3
20.29 5
27.43 6
34.57 8
41.71 11
48.86 11
More 4

 
Histogram
 
Descriptive Statistics Table

Pre-Exposure µg/dL  
   
Mean 32.8571429
Standard Error 1.75230655
Median 35
Mode 36
Standard Deviation 12.2661458
Sample Variance 150.458333
Kurtosis -0.5760371
Skewness -0.4251097
Range 50
Minimum 6
Maximum 56
Sum 1610
Count 49

 
 

Post-Exposure µg/DL  
   
Mean 33.28571429
Standard Error 1.781423416
Median 36
Mode 38
Standard Deviation 12.46996391
Sample Variance 155.5
Kurtosis -0.654212507
Skewness -0.483629097
Range 50
Minimum 6
Maximum 56
Sum 1631
Count 49

 
 
Measurement Scale
The ratio data has been used because the Return on investment per number of contracts can be classified and ordered.
Measure of Central Tendency
The mean, median and mode of the data are 33.29,36 and 38. The three values are close meaning that the data is normally distributed and there is no skewness.
Skewness and Kurtosis
The skewness and kurtosis of both pre and post exposure data are within the acceptable region because the values fall with the range of -2 and +2 for the skewness and -7 to +7 for the kurtosis.
Evaluation
The data presented in the descriptive statistics indicates normally of the data because, skewness and kurtosis fall within the acceptable range of -2 and +2. Additionally, the measure of central tendency are closely aligned which is a sign of normal data. Therefore the assumptions of the parametric statistics have been met.

ANOVA: Descriptive Statistics and Assumption Testing

Frequency Distribution Table

A=Air Frequency
3 1
5.75 3
8.5 4
11.25 8
More 4

 
 

B=Soil Frequency
6 1
7.75 2
9.5 10
11.25 5
More 2

 

C=Water Frequency
3 1
5.25 5
7.5 8
9.75 2
More 4

 

D=Training Frequency
3 1
4.25 3
5.5 7
6.75 6
More 3

 
 
 
 
Histogram
 
 
 
Descriptive Statistics Table

  A=Air B=Soil C=Water D=Training
         
Mean 8.9 9.1 7 5.4
Standard Error 0.6840 0.3900 0.5758 0.2656
Median 9 9 6 5
Mode 11 8 6 5
Standard Deviation 3.0591 1.7442 2.5752 1.1877
Sample Variance 9.3579 3.0421 6.6316 1.4105
Kurtosis -0.6283 0.1192 -0.2375 0.2537
Skewness -0.3608 0.4920 0.7602 0.1592
Range 11 7 9 5
Minimum 3 6 3 3
Maximum 14 13 12 8
Sum 178 182 140 108
Count 20 20 20 20

 
Measurement Scale
The measurement scale used in the above case if ratio because the data can be classified and ordered.
 
 
 
Measure of Central Tendency
The mean, median and mode of Air are 8.9, 9 and 11. Soil has a mean, median and mode of 9.1, 9 and 8 while water has 7, 6 and 6. On the other hand, training is 5.4, 5 and 5. These values are close to each other implying that the data is normally distributed.
Skewness and Kurtosis
The skewness and kurtosis for all the data set, for Air, soil, water and training is illustrated in the above descriptive statistic and is between -2 and +2, implying that the data is moderately distributed and the distribution curve is not peaked. This confirms that the data falls within the acceptable region.
Evaluation
The mean, median and mode of the data are closely aligned showing the normalcy of the data. Similarly the skewness and kurtosis are within the acceptable range of -2 and +2 indicating that the data is normally distributed.

Data Analysis: Hypothesis Testing

Hypothesis testing is the examination of the relationship between two or more variables. It tests whether two variables affects one another or not (Keysers&Wagenmakers, 2020) It also tests the strength of relationship that exists between the variables. One variable is assumed to be dependent while the other is assumed to be independent (Creswell, 2018). The dependent variable relies on the independent variable. On the other hand, independent variable does not rely on dependent variable and would happen either way with or without dependent variable (Creswell, 2018). Here we will explore two parametric statistical procedures used to test hypothesis.  They are the t test and ANOVA. The two tests are similar yet different. The t test is used to compare two means and ANOVA is used to compare more than two means (Creswell, 2018).

Independent Samples tTest: Hypothesis Testing

 
Ho4: There are no statistically significant differences in the effectiveness of the revised training program versus the prior training program.
Ha4: There are statistically significant differences in the effectiveness of the revised program versus the prior training program.

t-Test: Two-Sample Assuming Unequal Variances
  Group A Prior Training Scores Group B Revised Training Scores
Mean 69.79032258 84.77419
Variance 122.004495 26.96457
Observations 62 62
Hypothesized Mean Difference 0
df 87
t Stat -9.666557191
P(T<=t) one-tail 9.69914E-16
t Critical one-tail 1.66255735
P(T<=t) two-tail 1.93983E-15
t Critical two-tail 1.987608241  
 
 
 
 

Interpretation:
The mean value is lower for Group A (Prior training) than Group B (Revised training). We used the alpha of 0.05; the results of the independent samples t test show a p-value (two-tailed) of 1.94E-15, which is lower than the alpha of 0.05. Therefore we reject the null hypothesis and accept the alternative hypothesis. There are statistically significant differences in mean values of the DV between the prior training program and revised training program. Respectfully, Sun Coast should replace the prior training program with the revised training program.

Dependent Samples (Paired Samples) tTest: Hypothesis Testing

Ho5: There is no statistically significant difference in employee blood lead levels between pre-exposure and post-exposure.
Ha5: There is a statistically significant difference in employee blood lead levels between pre-exposure and post-exposure.

t-Test: Paired Two Sample for Means
  Pre-Exposure μg/dL Post-Exposure μg/dL
Mean 32.85714286 33.28571
Variance 150.4583333 155.5
Observations 49 49
Pearson Correlation 0.992236043
Hypothesized Mean Difference 0
df 48
t Stat -1.92980256
P(T<=t) one-tail 0.029776356
t Critical one-tail 1.677224197
P(T<=t) two-tail 0.059552711
t Critical two-tail 2.010634722  

Interpretation:
We were provided with an alpha of 0.05 and the results show a p-value of 0.06 which is greater than the given alpha of 0.05. We must reject the alternative hypothesis and accept the null hypothesis which states that there are no statistically significant differences in the lead levels in the blood pre exposure and post exposure of employees working where lead remediation is being conducted.

ANOVA: Hypothesis Testing

Ho6: There are no statistically significant differences with return on investment and each line of service.
Ha6: There are statistically significant differences with return on investment and each line of service.

Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
A = Air 20 178 8.9 9.357895
B = Soil 20 182 9.1 3.042105
C = Water 20 140 7 6.631579
D = Training 20 108 5.4 1.410526
ANOVA
Source of Variation SS df MS F P-value F crit
Between Groups 182.8 3 60.93333333 11.9231 1.75888E-06 2.72494395
Within Groups 388.4 76 5.110526316
Total 571.2 79        

Interpretation:
The ANOVA p-value is 1.76E-06 which is less than our alpha of 0.05. Therefore, we reject the null hypothesis and accept the alternative hypothesis. There are statistically significant differences between return on investment and the four lines of service offered at Sun Coast.
 

Findings

According to the six research objectives results show that there is indeed a relationship between the variables being questioned. Below are the results of the six research objectives:
RO1: Determine if there is a relationship between particulate matter size and employee’s health.
The results show that there is a statistically significant relationship between particulate matter size and employee’s health. The mean is 7.13, mode is 7, and median is 7.
RO2:  Predict lost-time hours from training expenditures.
Results show that lost time hours can be reduced with proper safety training.
RO3: Predict the dB relationship between frequencies, angle in degrees, chord length, velocity, and displacement and the decibel level.
Results show that is a relationship between frequencies, angles in degrees, chord length, velocity and displacement.
RO4: Determine if revised program more effective than the prior program.
Results indicate that the revised training program is more effective than the prior training program. Prior group (A) mode was 69.8 and Revised group (B) mode was 84.7.
RO5: Determine if employee blood lead levels have increased.
The results shows that employee’s blood level increased after being exposed to lead. Pre-exposure blood level was 32.86 and post exposure blood level was 33.29.
RO6: Determine if there is a difference in return on investment between each line of service.
The results indicate that there is a significant difference in return on investment between each line of service. Air return on investment was 8.9. Soil return on investment was 9.1. Water brought in a return of 7, while training had a return of 5.4.
 

Recommendations

This research was completed using quantitative research methodology. Quantitative research has been deemed reliable, therefore I trust my findings. The recommendations below are not professional but they are personal and backed with extensive and reliable research.
Particulate Matter Recommendation
After doing extensive research, it is evident that particular matter size is harmful to human health. Employers should be concerned with the well-being of their employees. Particular matter below 10 micrometers in diameter causes the biggest health problem.
Safety Training Effectiveness Recommendation
Training should be based on company’s needs and assessed through safety appraisal processes. After testing Sun Coast’s data, they should replace the prior training program with the revised training program. Employees should be trained properly to avoid workplace injuries.
Sound-Level Exposure Recommendation
There are various noise levels in the workplace therefore; it is recommended that Sun Coast take preventive measure to reduce noise exposure. Workers should be provided with proper PPE equipment such as earplug, earmuffs and etc.
New Employee Training Recommendation
More than likely, new employees are not familiar with business’s daily operations and safety protocols. New employees should be trained properly and effectively. Without proper training, employees are vulnerable to workplace injuries. Training new employees help to mitigate risks.
Lead Exposure Recommendation
Lead exposure is very harmful. I recommend Sun Coast continue testing the blood levels of all exposed employees. They should also provide workers with protective gear as well as provide them with information on how to protect themselves and their family from being exposed.
Return on Investment Recommendation
All companies want to see a return on their investments. I recommend Sun Coast re-evaluate their expectations and balance their risks. Re-evaluating helps to see what beneficial changes need to made to meet those expectations.
 
 
 
 
 
References
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EPA. (2021). Health and Environmental Effects on Particular Matter (PM). Retrieved from: https://www.epa.gov/pm-pollution/health-and-environmental-effects-particulate-matter-pm
George, D., & Mallery, P. (2018). Descriptive statistics. In IBM SPSS Statistics 25 Step by Step (pp. 126-134). Routledge.
https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.4324/9781351033909-14&type=chapterpdf
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Keysers, C., Gazzola, V., &Wagenmakers, E. J. (2020).Using Bayes factor hypothesis testing in neuroscience to establish evidence of absence. Nature neuroscience23(7), 788-799.
Masters, R., et al. (2017). Return on Investment of public health interventions: a systematic review.
OSHA. (2021). Business Case for Safety and Health. Retrieved from: https://www.osha.gov/businesscase
Saleem, S., & Qayyum, S. (2019). Lead exposure and its adverse health effects among occupational worker’s children. Toxicology and Industrial Health, 26 (8), 497-504.
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Executive Summary?
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