Sample Research Methodology Paper on Sun Coast Remediation Project

Executive Summary

Sun Coast aims to solve several consistent problems using research to help it reach
evidence-based business decisions. The company identified six areas of concern to address:
particulate matter presence in its job sites, exposure of lead to its employees, employee
productivity using upgraded training programs, factors to include in determining return on
investment, sound level exposure of its employees, and the overall employee’s attitude towards
work due to safety training program. This research used quantitative design to collect data across
103 job sites to help Sun Coast understand and address its problems. Statistical analysis
demonstrated the strong impacts of appropriate training program on the attitude of employees
towards work. The results further implied that Sun Coast employees had lower exposure to risk
health due to the presence of lead in the work environment. However, the findings demonstrated
that Sun Coast loses approximately 7 days per employee each year, making it essential for the
company to provide protective measures that prevent employees from inhaling dangerous
particulate matter. The results further demonstrated the need to include both water, soil, and air
safety in the training programs to calculate appropriate return on investment. The findings reveal
that even though Sun Coast currently loses many days per year, it can increase its workforce
productivity by implementing new training program and reducing the particle matter presence in
its job sites.

Sun Coast Remediation Project 5

Sun Coast Remediation Course Project

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 the
statement of the problems, literature review, research objectives, research questions and
hypotheses, research methodology, design, and methods, data analysis, findings, and
recommendations.

Statement of the 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, PM varies in size depending on the
project and job site. PM that is 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 that is 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 that is less than 2.5 microns, it is potentially more harmful than PM
that is between 10 and 2.5 since the conditions are more suitable for inhalation. PM that is less
than 2.5 is 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

Sun Coast Remediation Project 6
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 ear-plugs 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

Sun Coast Remediation Project 7
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 baseline 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 2-year 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.

Sun Coast Remediation Project 8

Literature Review

A large percentage of Sun Coast contracts involve working in areas contaminated with
various substances. Numerous variables place the health and safety of workers at risk. Various
studies have focused on the same issues as those affecting Sun Coast. Research by Boin, Colin,
Grzebyk (2016) aimed at understanding the effect of training on occupational health and safety
on workplace injuries among young workers beginning their careers. The authors hypothesized
that young individuals who receive education on occupation and health training would
experience fewer cases of workplace injuries. The research included learners at the end of their
education and the start of their careers. The study presented all descriptive statistics as either
standard deviation or mean for continuous variables. The incident rate ratio (IRR) was used to
interpret the parameters of the model. Boin, Colin, Grzebyk (2016) concluded that in France
education on occupational health and safety was useful in reducing cases of workplace injuries.
Boin, Colin, Grzebyk (2016) offer an important framework for researchers interested in the issue
of workplace safety. The authors highlight the need for organizations like Sun Coast to provide
education and training to their employees on workplace safety and how to deal with various risks
in the workplace. Training and education will equip employees at Sun Cost with the knowledge
and skills required to manage potential risks and challenges in the workplace.
In another research, Ceballos, Gong, and Page (2015), surveyed a randomly chosen
sample of e-scrap recycling facilities in the United States to define workplace exposures,
processes, and controls. While the researchers had targeted 278 facilities nationwide, only 47
participated in the research with nearly all the facilities reporting the recycling of electronics.
This study identifies workplace health and safety issues affecting employees and measures
initiated by organizations in dealing with these elements. While it is not possible to generalize

Sun Coast Remediation Project 9
the findings to the entire population in the country, the study offers informative guidelines on
safety and health programs in organizations. This information is useful for an organization like
the Sun Coast, which faces similar challenges reported by some of the organizations in the study.
Ceballos, Gong, and Page (2015) highlight the need for organizations like Sun Coast to educate
employees on best practices in occupational health and safety, especially in handling objects that
pose significant risks to their lives. The study provides an effective framework to approach the
issue at Sun Coast. It highlights challenges organizations implementing safety measures
experience and offers insight into how Sun Coast can overcome these challenges.
In a theoretical and empirical investigation, Wachter and Yorio (2014) investigated safety
management practices and strategies for reducing workplace accidents in organizations. The
overall research goal was to empirically and theoretically develop ideas around safety
management practices in organizations and explore how various practices work to create a
positive environment in organizations. Researchers collected data using supervisors, safety
managers and employee surveys aimed at assessing and linking safety management practices to
safety performance outcomes. Using ANOVA analysis and statistical significance of p ≤ 0.05 in
developing a causal link between the variables, Wachter and Yorio (2014) observed a significant
negative link between safety management practices in an organization with accident rates. The
authors concluded that when organizations like Sun Coast invest in safety practices to protect the
employees, it is essential to remain focused on winning the minds and hearts of the workers
through performance-based systems aimed at enhancing worker engagement. This study
enriches the research with vital knowledge on the things Sun Coast should do or programs the
organization needs to initiate when implementing measures aimed at protecting the employees

Sun Coast Remediation Project 10
against workplace risks. The study identifies some of the measures the organization can
implement to protect workers from workplace hazards.
One of the major challenges facing researchers is how to garner evidence from studies. In
a meta-analysis conducted by Combs, rook, and Rauch (2019), the authors observed that meta-
analysis has emerged as a widely useful technique for researchers to develop a deep
understanding of an issue. While focusing on various approaches to meta-analysis, including the
meta-analytic structural equation modeling (MASEM) the authors develop effective framework
researchers can use to develop insight about a phenomenon under investigation. The study by
Combs et al. (2019) has a huge impact on future studies. The authors note that one of the
common methodological changes facing meta-analysis is the failure of many researchers to
report the causal link or relationships between variables. Combs et al. (2019) provide vital
guidelines required in collecting the most useful material and research in developing a critical
analysis of the situation at Sun Coast and fulfilling the project. Besides, Creswell and Creswell
(2017) offers an effective framework for approaching quantitative research. The authors note
that utilizing various approaches can help researchers develop a wider scope of the phenomenon
under investigation. The studies by Creswell and Creswell (2017) and Combs et al. (2019) offer
an insightful framework for approaching the problem facing the Sun Coast. The authors identify
research approaches and data analysis tools researchers can adopt when conducting quantitative
research.
Research Objectives

Introduction

Organizations aim to protect their staff from workplace injuries and by taking several
approaches that ensure the firm operates at maximum potential. Employees are the backbone of

Sun Coast Remediation Project 11
efficient service production and customer handling (Ceballos, Gong, & Page, 2015). When
employees are healthy and assured of their safety within an organization, they obtain the
psychological peace that motivates them to remain productive at their level best (Bayram, 2019).
A substantial proportion of Sun Coast employees work in areas contaminated with various
substances, exposing their health at risk. The high rate of injuries that forces Sun Coast
employees to remain worried about their health problems is a significant problem. Boini, Colin,
and Grzebyk (2016) outline the positive impacts of formal training on health and safety of
employees. Wachter and Yorio (2014) also elucidate a strong relationship between safety
management practices in an organization and accident rates, outlining that institutions with poor
safety management have higher accident rates. Therefore, organizations like Sun Coast should
invest in safety practices to protect the employees.
Research Problems

A major problem relies on the most appropriate ways to protect employees and win their
confidence in the organization. Consequently, the following are the major research problems that
Sun Coast must address.
RO1: Determine if health and safety training reduce workers’ injury rate.
RO2: Determine the impacts of particulate matter on employees’ health and productivity.
RO3: Determine the impacts of sound level exposure on the injury of employees.
RO4: Determine the impact of work environments on lead exposure of employees.
RO5: Determine the relationship between appropriate workplace safety management and
employee’s attitude towards work.
RO6: Determine the impact of the return on investment of health and safety management through
the inclusion of different factors.

Sun Coast Remediation Project 12

Research Questions and Hypotheses

This section provides the major questions that the study aims to answer, and the
hypothesis used to answer these questions. The research questions are as follows:
RQ1: Does a health and safety training program reduce employees’ injury rate at work?
H01: Health and safety training program do not reduce employees’ injuries at work.
HA1: Health and safety training program reduces employees’ injuries at work
RQ2: Determine the level of particulate matter affects employees’ health and productivity?
H02: Particulate matter levels do not affect the health of employees at work.
HA2: Particulate matter levels affect the health of employees at work.
RQ3: What is the impacts of sound level exposure on the injury rate of employees?
H03: Sound level exposure do not affect injury rates of employees.
HA3: Sound level exposure affect injury rates of employees.
RQ4: What is the impact of work environments on lead exposure of employees?
H04: Work environment does not affect lead exposure of employees.
HA4: Work environment does not affect lead exposure of employees.
RQ5: What is the overall impact of a firm’s workplace safety management on employees’
attitude towards work?
H05: A firm’s workplace safety management approach does not influence an employee’s attitude
towards work.
HA5: A firm’s workplace safety management approach influences the employees’ attitude
towards work.
RQ6: Does an organization’s approach to health and safety management impacts its return on
investment?

Sun Coast Remediation Project 13
H06: An organization’s health and safety management approach does not impact return on
investment.
HA6: An organization’s health and safety management affects return on investment.

Research Methodology, Design, and Methods

Investigating the effectiveness of the health and safety training in reducing workers’
injury rate is of utmost urgency at Sun Coat Company. Therefore, this paper describes a research
design and methodology employed in performing the investigation.
Research Methodology
The current research will employ a mixed-method research methodology. This
methodology combines quantitative and qualitative research designs, enabling the collection of
both numeric and textual data (Basias & Pollalis, 2018; Morse, 2016). The primary variables in
this research are workers’ injury rates, health and safety training, employees’ attitudes towards
work, and accident investigation process. The injury rate is a quantifiable element, requiring
quantitative methodologies to collect and analyze its data. Data on the remaining variables will
be mainly textual, requiring qualitative methods. Therefore, since both numeric and non-numeric
data will be necessary for the successful completion of this research, a mixed-method
methodology is the most appropriate.
Research Design
A descriptive research design is the most appropriate for this study. This design explains
the impact of independent variables on the dependent ones by answering the ‘what’ questions,
thereby enabling users of the established findings to make data-backed decisions (Gray, 2019).
On the other hand, the exploratory design is most appropriate in studies where researchers lack
in-depth understanding and definition of the central issues. In contrast, a causal model is relevant

Sun Coast Remediation Project 14
in studies where all the primary variables change (Ghauri et al., 2020). Since the primary goal of
the current research is to explain the impact of OHS training on the safety consciousness of the
employees, a descriptive design fits the requirements.
Research Methods
The research methods employed in a study must align with the research design. To respond
effectively to the research questions and hypotheses formulated in this study, a blend of
descriptive statistics and correlation methods will be appropriate. These two methods will apply
to all the research questions, establishing correlations among the variables.
Data Collection Methods
Research questions and study designs guide the data collection techniques. Since a mixed
study methodology is the most relevant for this study, both quantitative and qualitative methods
will be critical for gathering data. To collect the necessary data, combining records analysis,
surveys, observations, and questionnaire techniques will be vital.
Sampling Design
The research will employ random sampling to select the respondents. This type of
sampling eliminates bias in data collection processes, thereby guaranteeing the validity and
credibility of the obtained statistics (Aono & Nguyen, 2017). Nonetheless, the selected
respondents must possess a minimum of five-month working experience in the organization. The
employees with a shorter working spell at the company had to go through the current OHS
training, while the cohort with three or more years was trained using the previous procedures. To
compare the effectiveness of the two instructions, workers that experienced each of them must
participate in the study. Therefore, there will be no cap for the upper limit of the working
experience.

Sun Coast Remediation Project 15
Data Analysis Procedures
The MAXQDA program is the preferred tool to test RQ3, RQ4, RQ5, and RQ6 because
these questions focus on analyzing different non-numeric variables. Conversely, the t-test is the
favorite analysis for RQ1 and RQ2. For these two questions, the researcher will collect numeric
data about the effectiveness of training on injury rates. To compare the two types of OHS
trainings implemented by the organization, the t-test will be the most appropriate.
Data Analysis: Descriptive Statistics and Assumption Testing

The purpose of research projects is to understand the behavior of variables and make
inferences based on the various attributes of the variables. While inferential analysis is the
ultimate intention of the project, it is important to study the characteristics of variables regarding
measures of central tendency, dispersion, variability, and symmetry regarding the normal
distribution. This is because classical statistics require the normality assumption to be fulfilled
before proceeding with the inferential analysis.

Correlation: Descriptive Statistics and Assumption Testing

Histogram

Below are the histograms of the microns and the average number of annual employee sick days.

Sun Coast Remediation Project 16

Descriptive statistics table

microns mean annual sick days per employee
Mean 5.6573 7.1262
Standard Error 0.2556 0.1865
Median 6 7
Mode 8 7
Standard Deviation 2.5941 1.8926
Sample Variance 6.7291 3.5820
Kurtosis -0.8522 0.1249
Skewness -0.3733 0.1422
Range 9.8 10
Minimum 0.2 2
Maximum 10 12
Sum 582.70 734
Count 103 103

Measurement scale

Sun Coast Remediation Project 17
The two variables are measured on the continuous ratio scale since the various statistical
measures can be obtained directly without manipulation of the initial form of the data.

The measure of central tendency

The measures of central tendency include the mode, mean, and median. The microns
have a mean of 5.67 while the mode was eight and the median was 6. The mean and the median
are close, but the median is about two units from the mean and the mode. However, the mean for
the average number of annual sick leave days was 7.1 while the mode was seven and the median
was also 7.

Evaluation

The microns are negatively skewed since the mean is less than both the mean and the
median. This is also evident from the skewness value of -0.37. However, the distribution of the
microns can be approximated using the normal distribution since the skewness lies between -1
and +1. Similarly, the average number of sick leave days can be approximated to the standard
normal distribution. This is because the measures of central tendency are packed closely
together. More so, the skewness value is small at 0.14, implying the positive skewness is
negligible.

Simple Regression: Descriptive Statistics and Assumption Testing

Sun Coast Remediation Project 18
Histogram

Descriptive statistics table.

safety training expenditure lost time hours
Mean 595.984 188.004
Standard Error 31.477 4.803
Median 507.772 190
Mode 234 190

Sun Coast Remediation Project 19
Standard Deviation 470.052 71.725
Sample Variance 220948.846 5144.536
Kurtosis 0.444 -0.501
Skewness 0.951 -0.082
Range 2251.404 350
Minimum 20.456 10
Maximum 2271.86 360
Sum 132904.517 41925
Count 223 223

Measurement scale

The two variables are measured on the continuous ratio scale since the various statistical
measures can be obtained directly without manipulation of the initial form of the data.

The measure of central tendency

The mean of the safety training expenditure is $595.9, while the mode was $234, and the
median was $507.77. This implies that most of the employees used about $234 on training, while
a few others had values greater than that, leading to the large expenditure as shown by the mean
and the median. This was also a possible pointer to outliers, which affect the validity of the data
and analysis thereof. On the other hand, the mean of lost time was 188 hours, with a mode of 190
and a median of 190. These measures of central tendency are close together with the mean being
slightly lower. Therefore, most of the employees or departments lose between 188 and 190 hours
on average.

Evaluation

Sun Coast Remediation Project 20
The skewness for the expenditure was significantly positive at 0.9, implying that the data
is not normally distributed. However, being the independent variable in the simple regression
model, its distribution is not a concern. The distribution of the dependent variable, the lost hours,
was approximate to the standard normal distribution since the measures of central tendency are
packed close together, and the skewness is negligible at –0.08. It, therefore, meets the parametric
assumption for use as a dependent variable in the regression model without transformation.
Multiple Regression: Descriptive Statistics and Assumption Testing

Sun Coast Remediation Project 21

Sun Coast Remediation Project 22

Descriptive statistics table

Frequenc
y (Hz)

Angle in
Degrees

Chord
Length

Velocity (Meters
per Second)

Displace
ment
Decibel

Mean 2886.381 6.782 0.116 50.861 0.011 124.83
6

Standard
Error

81.318 0.153 0.001 0.402 0.000 0.178

Sun Coast Remediation Project 23
Median 1600 5.4 0.1176 39.6 0.005 125.72
1
Mode 2000 0 0.0917 39.6 0.005 127.31
5

Standard
Deviation

3152.573 5.918 0.049 15.573 0.013 6.899

Sample
Variance

9938717.3
84
35.024 0.002 242.512 0.000 47.591

Kurtosis 5.709 -0.413 -1.178 -1.564 2.219 -0.314
Skewness 2.137 0.689 -0.028 0.236 1.702 -0.419
Range 19800 22.2 0.1697 39.6 0.058 37.607
Minimum 200 0 0.03 31.7 0.000 103.38
0
Maximum 20000 22.2 0.1997 71.3 0.058 140.98
7
Sum 4338230 10193.8 174.5585 76443.7 16.743 187628
.422
Count 1503 1503 1503 1503 1503 1503

Measurement scale

The six variables are measured on the continuous ratio scale since the various statistical
measures can be obtained directly without manipulation of the initial form of the data.

The measure of central tendency

Sun Coast Remediation Project 24
The average of the variables was 2886.38 for the frequency, 6.7 for the degrees, 0.116 for
the chord length, 50.86 for the velocity, 0.011 for the displacement, and 124.83 for the decibels.
The median values were 1600. 5.4, 0.1176,39.6,0.005, and 125.721, respectively. Finally, the
mode was 2000, 0, 0.0917, 39.6, 0.005, and 127.315, respectively. The independent variables did
not have uniformity of the measures of central tendency with many variations. In contrast, the
decibels had slight discrepancies with the mean being less than the mode and the media.
Therefore, there was a slight negative skewness.
Evaluation

Multiple regression involves the use of more than one independent variable to explain the
variability of a single dependent variable. As with the simple linear regression, this parametric
procedure requires that the dependent variable be normally distributed, but the distribution of the
independent variables is not a concern. As such, the decibels meet the normality assumption and
can, therefore, be used in the regression model.

Independent Samples t Test: Descriptive Statistics and Assumption Testing

Histogram

Sun Coast Remediation Project 25

Descriptive statistics table

Group A Prior Training Scores Group B Revised Training Scores
Mean 69.790 84.774
Standard Error 1.403 0.659
Median 70 85
Mode 80 85
Standard Deviation 11.046 5.193

Sun Coast Remediation Project 26
Sample Variance 122.004 26.965
Kurtosis -0.777 -0.353
Skewness -0.087 0.144
Range 41 22
Minimum 50 75
Maximum 91 97
Sum 4327 5256
Count 62 62

Measurement scale

Both variables are measured on the continuous ratio scale since the various statistical
measures can be obtained directly without manipulation of the initial form of the data.

The measure of central tendency

On average, the employees scored 69.79 before the training for group A and scored 84.77
when the scores were revised for group B. In group A, the most common score was 80 and a
median of 70, while in group B, the most common score was 85, with a median of 85. This
implies that group B scores had better estimation than Group A scores.

Evaluation

The basic assumption for the independent sample t-test is that both variables should be
distributed according to the normal distribution. Group B scores were close to the standard
normal curve, while Group A scores violated this assumption due to the large disparity between
the measures of central tendency. Therefore, Group A scores should be transformed to ensure
conformance with the normality assumption.

Sun Coast Remediation Project 27
Dependent Samples (Paired-Samples) t Test: Descriptive Statistics and Assumption Testing

Histogram

Descriptive statistics table

Pre-Exposure μg/dL Post-Exposure μg/dL
Mean 32.857 33.286
Standard Error 1.752 1.781
Median 35 36

Sun Coast Remediation Project 28
Mode 36 38
Standard Deviation 12.266 12.470
Sample Variance 150.458 155.500
Kurtosis -0.576 -0.654
Skewness -0.425 -0.484
Range 50 50
Minimum 6 6
Maximum 56 56
Sum 1610 1631
Count 49 49

Measurement scale

Both variables are measured on the continuous ratio scale since the various statistical
measures can be obtained directly without manipulation of the initial form of the data.

The measure of central tendency

The level of blood lead levels was 32.87 before exposure, with the most common level
being 36 and a median of 35. On the other hand, the lead levels after exposure were 33.28 on
average, with the most common being 38 and a median of 36. In both instances, the mean value
is less than the mode and the median.

Evaluation

The discrepancies in the measures of central tendency point to negative skewness. This
implies that none of the variables meets the normality assumption in a strict sense. However, the

Sun Coast Remediation Project 29
skewness values are moderately low; hence, the dependent samples t-test can still be conducted,
but the results may not be reliable.

ANOVA: Descriptive Statistics and Assumption Testing

Histogram

Sun Coast Remediation Project 30

Descriptive statistics table

A = Air B = Soil C = Water D = Training
Mean 8.9 9.1 7 5.4
Standard Error 0.684 0.390 0.576 0.266
Median 9 9 6 5
Mode 11 8 6 5
Standard Deviation 3.059 1.744 2.575 1.188

Sun Coast Remediation Project 31
Sample Variance 9.358 3.042 6.632 1.411
Kurtosis -0.628 0.119 -0.238 0.254
Skewness -0.361 0.492 0.760 0.159
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 six variables are measured on the continuous ratio scale since the various statistical
measures can be obtained directly without manipulation of the initial form of the data.

The measure of central tendency

The percentage return on investment for air remediation, soil remediation, water
remediation, and training remediation was 8.9, 9.1, 7, and 5.4, respectively. The most common
values for the four remediations were 11, 8, 6, and 5, respectively. The median for the four
remediations was 9, 9, 6, and 5, respectively. The soil, water, and training average percentage
returns on investment were more than the mode and the median as opposed to air.

Evaluation

Based on the measures of central tendency above, the four variables have skewness and
cannot be approximated to the standard normal distribution. However, since the skewness values
are between -1 and +1, the variables can be used in the analysis of variance procedure, although
the results may not be reliable.

Sun Coast Remediation Project 32

Data Analysis: Hypothesis Testing

The inferential analysis is important in approving or disapproving the research
hypotheses. Among the most common approaches to inferential analysis are linear models that
help in creating a cause-and-effect relationship between variables (Herkenhoff & Fogli, 2013).
The parametric linear models include correlation analysis, regression analysis, and the analysis
of variance.

Correlation: Hypothesis Testing

H01: Health and safety training programs do not reduce employees’ injuries at work.
HA1: Health and safety training program reduces employees’ injuries at work
microns mean annual sick days per
employee

microns 1
mean annual sick days per employee -0.716 1

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.716
R Square 0.513
Adjusted R Square 0.508
Standard Error 1.328
Observations 103

ANOVA

df SS MS F Significance F
Regression 1 187.295 187.295 106.236 0.000
Residual 101 178.064 1.763
Total 102 365.3592233

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 10.081 0.315 31.989 0.000 9.456 10.707
microns -0.522 0.051 -10.307 0.000 -0.623 -0.422

Sun Coast Remediation Project 33
Pearson’s linear correlation analysis was conducted to determine whether there was a
linear association between the microns and the number of sick leave days in the organization.
The results indicate that there was a strong negative relationship between the two variables (r = –
0.716, p < 0.05). Therefore, the increase in microns has a corresponding decrease in the number
of sick leave days. Therefore, the null hypothesis was rejected. However, correlation does not
necessarily imply causality (Creswell & Creswell, 2018).

Simple Regression: Hypothesis Testing

H02: Task-employee matching models do not reduce the rates of injuries employees get at work.
HA2: Task-employee matching models reduce the rates of injuries employees get at work.
SUMMARY OUTPUT

Regression Statistics
Multiple R 0.940
R Square 0.883
Adjusted R Square 0.882
Standard Error 24.613
Observations 223

ANOVA

df SS MS F Significance F
Regression 1 1008202.105 1008202.105 1664.211 0.000
Residual 221 133884.890 605.814
Total 222 1142086.996

Sun Coast Remediation Project 34

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 273.449 2.665 102.598 0.000 268.197 278.702
safety training expenditure -0.143 0.004 -40.795 0.000 -0.150 -0.136

Simple regression analysis was conducted to model the relationship between the safety
training expenditure and the number of lost hours. The analysis above shows that the expenditure
is an important predictor of the lost time hours (t = -40.795, p < 0.05). Additionally, the
coefficient of determination of the model represented by the r-square indicates that the training
expenditure explained about 88.3 percent of the variability observed in the lost time (Sweeney,
Williams, & Anderson, 2011). The regression model developed is as follows:

The model is adequate in explaining the relationship between the variables (F = 1664.211, p <
0.05). An increase in training expenditure by $1 is likely to reduce the lost time by 0.143 hours
(Jani, 2014). Therefore, the null hypothesis was rejected.

Multiple Regression: Hypothesis Testing

H03: Enough communication and information sharing do not improve the safety of employees at
work.
HA3: Enough communication and information sharing improve the safety of employees at work.

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.602
R Square 0.362
Adjusted R Square 0.360
Standard Error 5.519
Observations 1503

ANOVA

df SS MS F Significance F
Regression 5 25891.888 5178.378 170.036 0.000
Residual 1497 45590.490 30.455
Total 1502 71482.378

Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 126.822 0.624 203.300 0.000 125.599 128.046
Frequency (Hz) -0.001 0.000 -23.488 0.000 -0.001 -0.001
Angle in Degrees 0.047 0.037 1.269 0.205 -0.026 0.121
Chord Length -5.495 2.928 -1.877 0.061 -11.239 0.248
Velocity (Meters per Second) 0.083 0.009 8.950 0.000 0.065 0.101
Displacement -240.506 16.519 -14.559 0.000 -272.909 -208.103

Sun Coast Remediation Project 35
Multiple regression analysis was conducted to determine the model between the noise
levels in decibels and the other factors related to noise, such as the frequency of sound, angle,
chord length, the velocity of sound, and the displacement. The model developed is as follows:

While this model was adequate (F = 170.036, p < 0.05) to explain about 36 percent of the
variability in noise level, the angle of inclination (t = 1.269, p = 0.205) and the chord length (t = –
1.877, p = 0.061) were found to be insignificant predictors. The displacement, frequency, and
velocity were important predictors. Therefore, the null hypothesis was rejected.
Independent Samples t-Test: Hypothesis Testing

The aim of the independent t-test is to determine if there exists a significant difference
between Group A: Prior Training Scores and Group B: Revised Training Scores
(bolt.mph.ufl.edu, 2018).
The research hypothesis is groups are:
Ho: There is no significant difference in training scores between prior-training and
revised training groups.
Ha: There is a significant difference in training scores between prior-training and revised
training groups.

Sun Coast Remediation Project 36

According to the analysis of the data, 2-tailed t-test was carried to determine the
difference between the two groups of data. According to the t-test results, the t-test statistic is –
9.666557191 at 87 degrees of freedom with a p-value of 9.69914E-16. The analysis is conducted
at a 95% confidence interval which means that the p-value is compared to 0.05 alpha level
(Landers, 2019). The p-value (9.69914E-16) is less than 0.05. There is enough evidence from the
study to reject the null hypothesis. A conclusion is therefore made that there is a statistically
significant difference in mean values for the training scores between Group A and Group B
Dependent Samples (Paired Samples) t-Test: Hypothesis Testing

In this part of the section, the same elements of the study are exposed to different conditions and
the t-test determines if there exists significant difference before and after the exposure. The
employees, in this case, are the study elements.
H0: There is no statistically significant difference in employee health before and after
exposure to μg/dL.
Ha: There is a statistically significant difference in employee health before and after
exposure to μg/dL

Sun Coast Remediation Project 37

According to the analysis, the t-statistic value from the excel output is -1.9298. In the
hypothesis, the analysis investigated the difference in means from the two groups of data.
According to the study, a two-tailed t-test was carried and showed a p-value of 0.05955. The
analysis was conducted at a 0.05 level of alpha (Hogg, McKean, & Craig, 2019). When the p-
value was compared to the alpha level it was slightly greater (0.05955>0.05). As a result, there
was no significant evidence to reject the null hypothesis. It was therefore concluded that there
existed no significant difference in employee health before and after exposure to μg/dL. The null
hypothesis was retained in the test.

ANOVA: Hypothesis Testing

The return on investment as a percentage is tested using four data variables. These are
soil, air, water and training. The aim of the ANOVA tests is to determine if there exists a
significant difference between the four groups of data (Carlson & Winquist, 2018). The analysis
if done on the following hypotheses.
Ho: There is no significant difference in means among the four groups; air, soil, water
and training.

Sun Coast Remediation Project 38
Ha: There is a significant difference in means among the four groups; air, soil, water and
training

According to the analysis, the average values appear different from the summary
statistics. Using the ANOVA analysis, the value of F was 11.9231, with a critical value of
2.7249. The analysis was done at 0.05 level of alpha which means is the base where the ANOVA
p-value is compared (Rossi, 2018). According to the result, the p-value was less than the alpha
level. Therefore, there was enough evidence to reject the null hypothesis and conclude that the
mean from the four groups was different. The alternative hypothesis is retained. A conclusion is
reached that air, soil, training and water have significant differences in Consulting Project Return
on Investment (%).

Sun Coast Remediation Project 39
Findings

This section interprets the findings from the statistical analysis of the data collected to
answer the research questions concerning the current Sun Coast situation.
RO1: Determine if Health and Safety Training Reduce Workers’ Injury Rates
The descriptive analysis demonstrates that Sun Coast spent an average of $595.984 and
188.004 hours each year to train its employees on workplace safety measures. Analysis of the
impacts of training on employees using productivity before and after improving training
perspectives resulted in significant improvements. Group A had a mean score of 69.7 measured
in the prior training. However, after revising the training program, the same group recorded
higher productivity. The minimum and maximum productivity of employees in the previous
training program was 50 and 91 respectively, whereas the adjusted training program was 75 and
97 respectively. This score demonstrates the importance of having appropriate training programs
for the employees on the way to conduct themselves and avoid workplace injuries. As the
employees practice safety measures, they prevent unnecessary exposure to risk factors, making
them healthier and motivated. A healthy workforce results in healthy productivity due to the
mental assurance of surviving. Therefore, the level of training program delivered to the
employees increases their productivity and reduces the chances of becoming sick and missing
work.
RO2: Determine the impacts of particulate matter on employees’ health and productivity.
The descriptive analysis demonstrates the number of sick days that each employee of the Sun
Coast has in a year. The descriptive statistics demonstrate that Sun Coast employees get exposed
to particle matters between 5.9 and 9.7 microns. This result demonstrates the danger of Sun
Coast employees getting exposed to hazardous working conditions that adversely affect their

Sun Coast Remediation Project 40
health status. Pearson’s linear correlation analysis between the size of microns and the number of
sick days demonstrated a strong negative relationship with the coefficient of -0.716 and a p-value
less than 0.05. The finding illustrates than an increase in the number of microns leads to a
decrease in health risks. It means that the higher the exposure of employees to particle matters
with smaller micron, the weaker the health of employees and the bigger the rate of sick days. To
protect employees from the high rate of sick days, Sun Coast must ensure it offers sufficient
training to its employees on the particle matter and takes necessary actions to eliminate smaller-
size PM or provide protective gear that ensures employees do not inhale the particle matter.
RO3: Determine the Impacts of Sound Level Exposure on the Injury of Employees
A multiple regression analysis was conducted to determine the model between the noise
levels in decibels and the other factors related to noise, such as the frequency of sound, angle,
chord length, velocity of sound, and displacement. The analysis demonstrated that noise level in
a workplace determines 36% of other related noise factors such as sound frequency, angle, chord
length, the velocity of sound, and the displacement as demonstrated by the R Square. This model
follows that Sun Coast should consider the level of noise of its work sites as it increases the
health problems of employees, leading to higher absenteeism. Specifically, the company should
ensure that it provides protective gear to those working at noisy sites to protect them from
adverse impacts of noise, such as becoming deaf and developing hearing problems. The
significant p-values demonstrate the importance of having sufficient communication with the
employees on the ways to protect themselves from sound pollution that may lead to hearing
problems in the long run. Therefore, the level of sound exposure affects the overall health of
employees as it presents adverse effects on the hearing capabilities.
RO4: Determine the Impact of Lead Exposure on the Health of Employees

Sun Coast Remediation Project 41
According to an analysis on the health effects of employees pre- and post-exposure to the
work environment, the t-statistic value from the excel output is -1.9298. In the hypothesis, the
analysis investigated the difference in means from the two groups of data. According to the two-
tailed t-test, a p-value of 0.05955 at a 0.05 level of alpha. This p-value was higher than 5%,
leading to the acceptance of the null hypothesis that there is no statistically significant difference
between employees’ health before and after exposure to lead. As a result, Sun Coast should not
worry too much about its workplace having a higher presence of lead-exposed to employees. It
means that the different job sites have fewer health impacts irrespective of them having a lead
presence.
RO5: Determine the Relationship Between Appropriate Workplace Safety Management
and Employee’s Attitude Towards Work
Sun Coast incurs a total of 753 sick days of total employees working in the 103 job sites
that were analyzed. However, analysis of two groups of employees trained using the old
approach and new, augmented programs demonstrate the impacts of improving training
programs to enhance productivity and attitude towards work. According to the analysis of the
data, a 2-tailed t-test statistic of -9.666557191 at 87 degrees of freedom with a p-value of
9.69914E-16 at 95% confidence level demonstrated the effectiveness of training program to
enhance employee understanding and handling workplace injuries. Therefore, the study
concluded that there is a statistically significant difference in mean values for the training scores
between Group A and Group B. Such an outcome demonstrates that the company should adopt
the new training program and drop the previous one to attract potential new employees and retain
the existing ones.

Sun Coast Remediation Project 42
RO6: Determine The Impact of the Return on Investment of Health And Safety
Management Through the Inclusion of Different Factors.
An ANOVA analysis was used to determine the factors that Sun Coast should consider when
calculating the return on investment. The research utilized a multi-regression analysis that
resulted in a significant F of 11.9231. The critical value of the result was 2.7249, with a p-value
less than 5%. Hence, the findings demonstrate that each of the four variables, namely air, soil,
training, and water have significant differences in Consulting Project Return on Investment.
Accordingly, the results demonstrate that Sun Coast must consider the four variables in
determining the return on investment. The company should plan to incorporate air, soil, and
water in the training programs to enhance the success of employee protection and avoidance of
injuries.

Sun Coast Remediation Project 43

Recommendations

Introduction
After the analysis of data collected and the statistical tests, the following section provides
recommendations that would help Sun Coast improve the productivity of its employees and
avoid injuries and sick days.
Recommendations
The study results demonstrated the importance of analyzing the particle matter of job
sites. From the study, the average particle matter of the 103 sites analyzed was 5.6573 micron.
The higher mean particle matter makes the company lose 7.12 days per employee per year,
resulting in a total of 734 days. This high rate demonstrates the need to engage in preventive
measures to ensure that employees remain safe as they work in their worksites. Therefore, Sun
Coast should buy protective gear for their employees to avoid cases of inhaling particle matters
ranging between 0.2 and 10 microns. Such a move will reduce the rate of sick days requested by
each employee in a year, thereby making the company remain productive and achieve its
objectives each year.
Analysis of training demonstrated the importance of upgrading training programs to meet
current workforce requirements. The productivity of the new group of employees exposed to
updated training programs had higher productivity scores and reduced lost hours than those
exposed to the previous programs. In this regard, Sun Coast should improve its safety training
programs to help employees learn different ways to protect themselves and their health while
handling various job sites. Such moves will ensure employees have the expert knowledge to
handle different scenarios and minimize the overall health impact. Similarly, the training
program will help address mental assurance that determines the rate of productivity. Frequent

Sun Coast Remediation Project 44
training program demonstrates to employees that Sun Coast values the employees, thereby
making them feel loved. Once employees feel that they are an integral part of Sun Coast’s
success, they become motivated to compensate for the effort that the organization puts towards
their health and wellbeing. Such outcomes enhance their productivity and loyalty.
Sound is a major factor impacting employees’ health. Accordingly, Sun Coast should
ensure that its employees wear protective gear each time they work in areas prone to sound
pollution. Such a move will help the organization remain competitive in the market and work
with a healthy group of employees. The protection of employees’ ears from sound pollution will
help Sun Coast attract and retain employees. The overall effect will prompt employees to feel
that the organization values their health, enticing them to work harder to compensate Sun Coast
for the efforts towards their protection. Similarly, Sun Coast should incorporate soil, water, and
air protection aspects in tis safety training programs for employees to obtain higher return on
investment. Similarly, such inclusion will also help it determine the appropriate return on
investment of its decision to appraise a given project. Therefore, protection of the employees
from sound pollution and inclusion of both air, water and soil in the calculation of return on
investment will help Sun Coast make appropriate investment decisions.
.

Sun Coast Remediation Project 45
References

Aono, Y., & Nguyen, P. Q. (2017, April). Random sampling revisited: Lattice enumeration with
discrete pruning. In Annual International Conference on the Theory and Applications of
Cryptographic Techniques (pp. 65-102). Springer, Cham. https://doi.org/10.1007/978-3-
319-56614-6_3
Basias, N., & Pollalis, Y. (2018). Quantitative and qualitative research in business & technology:
Justifying a suitable research methodology. Review of Integrative Business and
Economics Research, 7(7), 91–105. sibresearch.org/uploads/3/4/0/9/34097180/riber_7-
s1_sp_h17-083_91-105.pdf
Bayram, M. (2019). Safety training and competence, employee participation and involvement,
employee satisfaction, and safety performance: An empirical study on occupational
health and safety management system implementing manufacturing firms. Alphanumeric
Journal, 301-318. https://doi.org/10.17093/alphanumeric.555154
Boini, S., Colin, R., & Grzebyk, M. (2017). Effect of occupational safety and health education
received during schooling on the incidence of workplace injuries in the first 2 years of
occupational life: a prospective study. BMJ Open, 7(7), e015100.
bolt.mph.ufl.edu. (2018, May 12). Introduction to Statistical Inference. Retrieved from
https://bolt.mph.ufl.edu/6050-6052/unit-4/
Carlson, K. A., & Winquist, J. R. (2018). An introduction to statistics: An active learning
approach. Los Angeles: SAGE.
Ceballos, D. M., Gong, W., & Page, E. (2015). A pilot assessment of occupational health hazards
in the US electronic scrap recycling industry. Journal of Occupational and
Environmental Hygiene, 12(7), 482-488.

Sun Coast Remediation Project 46
Combs, J. G., Crook, T. R., & Rauch, A. (2019). Meta‐Analytic Research in Management:
Contemporary Approaches, Unresolved Controversies, and Rising Standards. Journal of
Management Studies, 56(1), 1-18.
Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed
approach. Thousand Oaks, CA: Sage publications.
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed
methods approaches (5th ed.). Thousand Oaks, CA: Sage.
Ghauri, P., Grønhaug, K., & Strange, R. (2020). Research methods in business studies.
Cambridge University Press.
Gray, D. E. (2019). Doing research in the business world. Sage Publications Limited.
Herkenhoff, L., & Fogli, J. (2013). Applied statistics for business and management using
Microsoft Excel. Springer.
Hoboken: John Wiley & Sons, Inc.
Hogg, R. V., McKean, J. W., & Craig, A. T. (2019). Introduction to mathematical statistics.
Boston: Pearson.
Jani, P. N. (2014). Business statistics: Theory and applications. PHI Learning.
Landers, R. N. (2019). A step by step introduction to statistics for business. Los Angeles:
Melbourne Sage.
Morse, J. M. (2016). Mixed method design: Principles and procedures (Vol. 4). Routledge.
Rossi, R. J. (2018). Mathematical statistics: an introduction to likelihood-based inference.
Hoboken: John Wiley & Sons, Inc.
Sweeney, D. J., Williams, T. A., & Anderson, D. R. (2011). Fundamentals of business statistics
(6. ed., internat. ed). South-Western/Cengage Learning.

Sun Coast Remediation Project 47
Wachter, J. K., & Yorio, P. L. (2014). A system of safety management practices and worker
engagement for reducing and preventing accidents: An empirical and theoretical
investigation. Accident Analysis & Prevention, 68, 117-130.
Wegner, T. (2010). Applied business statistics: Methods and Excel-based