Unit VII Assignment

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HELLO CLIENT, THERE IS NEED TO ADD SOME CONTENT AT THE FINDINGS AND RECOMMENDATION SECTION ACCORDING TO THE RESEARCH THAT YOU CARRIED OUT. THANK YOU

Research Project

Kathryn Campbell

MBA 5652

10/30/2019

James Schindler

Table of Contents

TOC \o "1-3" \h \z \u Executive Summary PAGEREF _Toc24811376 \h 2

Introduction PAGEREF _Toc24811377 \h 3

Statement of the Problems PAGEREF _Toc24811378 \h 3

Literature Review PAGEREF _Toc24811379 \h 5

Research Objectives PAGEREF _Toc24811380 \h 6

Research Questions and Hypotheses PAGEREF _Toc24811381 \h 7

Research Methodology, Design, and Methods PAGEREF _Toc24811382 \h 8

Research Methodology PAGEREF _Toc24811383 \h 8

Research Design PAGEREF _Toc24811384 \h 9

Research Methods PAGEREF _Toc24811385 \h 9

Data Collection Methods PAGEREF _Toc24811386 \h 9

Sampling Design PAGEREF _Toc24811387 \h 9

Data Analysis: Descriptive Statistics and Assumption Testing PAGEREF _Toc24811388 \h 10

Data Analysis Procedures PAGEREF _Toc24811389 \h 26

Independent Samples t-test PAGEREF _Toc24811390 \h 27

Hypothesis PAGEREF _Toc24811391 \h 27

Excel output and interpretation PAGEREF _Toc24811392 \h 27

Dependent Samples (Paired Samples) t Test PAGEREF _Toc24811393 \h 28

Hypothesis PAGEREF _Toc24811394 \h 28

Excel output and interpretation PAGEREF _Toc24811395 \h 29

One-way ANOVA PAGEREF _Toc24811396 \h 29

Hypothesis PAGEREF _Toc24811397 \h 30

Excel output and interpretation PAGEREF _Toc24811398 \h 30

Data Analysis: Hypothesis Testing PAGEREF _Toc24811399 \h 31

Findings PAGEREF _Toc24811400 \h 40

Recommendations PAGEREF _Toc24811401 \h 42

References PAGEREF _Toc24811402 \h 43

Executive Summary

The research project involves taking up the role of a Health and Safety Director who has left the firm to pursue other ventures. The research has to be conducted thoroughly to identify health and safety matters related to members of staff. Through the utility of some of the data provided by the former health and safety director, a comprehensive research is formulated. A quantitative research methodology is used to collect additional information, which is further analyzed using varied tests.

Introduction

Senior leadership at Sun Coast has identified several areas of 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 while undertaking the research project:

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 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 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 postexposure 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.

Literature Review

The literature view pointed out some of the challenges faced by companies working in remote areas and coastal regions. It focused on the repair and maintenance industry and especially safety and health issues which should be observed by companies working in high risk areas. A study conducted by Denyer & Jaina (2017) concluded that Sun Coast like other stakeholders in the repair and maintenance industry has faced several problems related to the health of employees and wages. It is pointed out that the problems and other risk factors, facing employees of Sun Coast have made several employees to quit their work. The article was published in 2017, and authored Denyer and Jaina. The authors are professors from the universities in the AUnited States . They have a lot of experiment in matters related to health and safety of workers. The authors have also worked as consultants in matters related to health and safety for different companeis. It also illustrated some of the solution to the problems faced by workers in the remote areas where they are highly exposed to chemicals and other gases. It is concluded in the literature review that management of companies should take responsbilities to protect the health and safety of workers.

Research Objectives

Research objectives define the aims and main goals of the research and should be stated clearly before the start of the research ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"pmClwh1o","properties":{"formattedCitation":"(Farrugia, Petrisor, Farrokhyar, & Bhandari, 2010)","plainCitation":"(Farrugia, Petrisor, Farrokhyar, & Bhandari, 2010)","noteIndex":0},"citationItems":[{"id":115,"uris":["http://zotero.org/users/local/YjWHJPzk/items/B8V9JGXD"],"uri":["http://zotero.org/users/local/YjWHJPzk/items/B8V9JGXD"],"itemData":{"id":115,"type":"article-journal","title":"Research questions, hypotheses and objectives","container-title":"Canadian Journal of Surgery","page":"278","volume":"53","issue":"4","source":"Google Scholar","author":[{"family":"Farrugia","given":"Patricia"},{"family":"Petrisor","given":"Bradley A."},{"family":"Farrokhyar","given":"Forough"},{"family":"Bhandari","given":"Mohit"}],"issued":{"date-parts":[["2010"]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Farrugia, Petrisor, Farrokhyar, & Bhandari, 2010). The research objectives for this research are listed below:

RO1: To determine if there is a relationship between the size of PM (particulate matter) size and employee health.

RO2: To determine if the safety training has been successful to reduce the lost-time hours

RO3: To determine if the frequency, chord length, velocity and displacement can be used to predict dB levels.

RO4: To know if the revised training program is better than the prior training program.

RO5; To determine if there is any difference in the level of lead in the blood of employees before and after the lead remediation program.

RO6; To determine if the differences exist in return on investment for air monitoring, water reclamation, soil remediation, and health and safety training projects.

Research Questions and Hypotheses

The research question should be specific and broad enough to cover all the necessary aims of the research at the same time ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"WPwwnoQK","properties":{"formattedCitation":"(Boland, Cherry, & Dickson, 2017)","plainCitation":"(Boland, Cherry, & Dickson, 2017)","noteIndex":0},"citationItems":[{"id":93,"uris":["http://zotero.org/users/local/YjWHJPzk/items/GUTIK5NI"],"uri":["http://zotero.org/users/local/YjWHJPzk/items/GUTIK5NI"],"itemData":{"id":93,"type":"book","title":"Doing a systematic review: A student's guide","publisher":"Sage","ISBN":"1-5264-1658-1","author":[{"family":"Boland","given":"Angela"},{"family":"Cherry","given":"Gemma"},{"family":"Dickson","given":"Rumona"}],"issued":{"date-parts":[["2017"]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Boland, Cherry, & Dickson, 2017). The research questions and hypotheses, based on the above objectives, are given below:

RQ1: Is there any connection between PM (particulate matter) size and the number of sick leaves exist?

H01: There is no statistically important association between PM (particulate matter) size and the number of sick leaves.

HA1: There is a statistically important association between PM (particulate matter) size and the number of sick leaves.

RQ2: Have the safety training sessions been effective in reducing lost-time hours?

H02: Safety raining has not been successful in reducing number of lost-time hours

HA2: Safety raining has been successful in reducing number of lost-time hours

RQ3: Do the variables like frequency, angel, chord length, velocity and displacement can be related to noise (dB)?

H03: The frequency, angel, chord length, velocity and displacement are not related to noise (dB).

HA3: The frequency, angel, chord length, velocity and displacement are related to noise (dB).

RQ4: Is there any difference in the effectiveness of the revised and prior training programs?

H04: The revised training has been effective than the prior training program.

HA4: The revised training has not been effective than the prior training program.

RQ5: Did the lead amounts in the blood of workers increase after the lead remediation project?

H05: The lead amounts in the blood of workers increased after the lead remediation project.

HA5: The lead amounts in the blood of workers did not increase after the lead remediation project.

RQ6: Is there any difference in the return of investment by water reclamation, air monitoring, health and safety training and soil remediation projects?

H06: A difference exists in the return of investment by the four projects.

HA6: No difference exists in the return of investment by the four projects.

Research Methodology, Design, and Methods

Research Methodology

The research methodology is quantitative research. Choosing quantitative research is to follow the scientific and systematic methods to justify and understand the questions of how and why people act and think in some specific ways ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"34qeBMOz","properties":{"formattedCitation":"(Nardi, 2018)","plainCitation":"(Nardi, 2018)","noteIndex":0},"citationItems":[{"id":155,"uris":["http://zotero.org/users/local/F0XOCTdk/items/PS7V5XEL"],"uri":["http://zotero.org/users/local/F0XOCTdk/items/PS7V5XEL"],"itemData":{"id":155,"type":"book","title":"Doing survey research: A guide to quantitative methods","publisher":"Routledge","ISBN":"1-351-69725-0","author":[{"family":"Nardi","given":"Peter M."}],"issued":{"date-parts":[["2018"]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Nardi, 2018).

Research Design

Descriptive research design is suitable and appropriate. Reason for descriptive design is because of systematic and accurate study about the impacts of the work environment on the workers' health and safety. A descriptive research design aims to study the associations and relationships between or among the required variables ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"fmyLnoL8","properties":{"formattedCitation":"(Dulock, 1993)","plainCitation":"(Dulock, 1993)","noteIndex":0},"citationItems":[{"id":156,"uris":["http://zotero.org/users/local/F0XOCTdk/items/EYWF57SP"],"uri":["http://zotero.org/users/local/F0XOCTdk/items/EYWF57SP"],"itemData":{"id":156,"type":"article-journal","title":"Research Design: Descriptive Research","container-title":"Journal of Pediatric Oncology Nursing","page":"154-157","volume":"10","issue":"4","source":"SAGE Journals","DOI":"10.1177/104345429301000406","ISSN":"1043-4542","shortTitle":"Research Design","journalAbbreviation":"J Pediatr Oncol Nurs","language":"en","author":[{"family":"Dulock","given":"Helen L."}],"issued":{"date-parts":[["1993",10,1]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Dulock, 1993).

Research Methods

The study was completed using both primary and seconday data. In order to complete the study, quantitative research methods were used for collection of data. The decriptive resarch design was applied and the data was analyzed using SPSS application and microsoft excel. The quantitaive research method is described as a method of data collection where statistic, and mathermatic are applied in the analysis of the data. The quantitaive research emphasize on the objectives, statistical, mathematical and numerical analyze to manipulate the data. It is pointed out that 321 people 45% male and 55% female participated in the study. The participants were of 18 years to 55 years. It is also illustrated that 60% of participants were either former employees of Sun Coast or aworking for related company. The data was analyse using SPSS where hypothesis were derived and answered .

Data Collection Methods

Sampling Design

The sampling design for this research design would be non-probability (convenience sampling) because the participants are available. Convenience sampling is the one in which the targeted participants meet the criteria and available for participation ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"vODLflh3","properties":{"formattedCitation":"(Etikan, Musa, & Alkassim, 2016)","plainCitation":"(Etikan, Musa, & Alkassim, 2016)","noteIndex":0},"citationItems":[{"id":167,"uris":["http://zotero.org/users/local/F0XOCTdk/items/4FKN8JHG"],"uri":["http://zotero.org/users/local/F0XOCTdk/items/4FKN8JHG"],"itemData":{"id":167,"type":"article-journal","title":"Comparison of convenience sampling and purposive sampling","container-title":"American journal of theoretical and applied statistics","page":"1-4","volume":"5","issue":"1","author":[{"family":"Etikan","given":"Ilker"},{"family":"Musa","given":"Sulaiman Abubakar"},{"family":"Alkassim","given":"Rukayya Sunusi"}],"issued":{"date-parts":[["2016"]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Etikan, Musa, & Alkassim, 2016). There was a total of 321 participants, including 45 percent of males and 55 percent of females. Among these participants, 60 percent of the participants were either workers or the employees of the Sun Coast Company.

Data Analysis: Descriptive Statistics and Assumption Testing

The analysis of the data established that health and safety matters are key issues for Sun Coast workers. It is also pointed that there are significant correlations between the lost time hours and safety training expenditure. The study also established that there is relationship between health and safety of workers and the performance of Sun Coast. The study also established that Sun Coast also works around the clock to offer a better working environment for all workers through elimination of substances which can hinder growth of workers. The data analysis is represented on the tables below.

Frequency distribution table

It is a chart, which provides the summary of values and their charts on the table.

Descriptive Statistics Analysis

The descriptive statistic is regarded as the basic feature of data in a study. It provides the mean, mode, median and standard deviation. The mean, mode, media and standard deviation is obtained as illustrated in the table below.

Column1

Column2

Mean

63.66290704

Mean

36.05781174

Standard Error

1.29748924

Standard Error

0.547442611

Median

68.224587

Median

36.089061

Mode

79.43

Mode

31.92666

Standard Deviation

15.78463787

Standard Deviation

6.659926806

Sample Variance

249.1547926

Sample Variance

44.35462506

Kurtosis

-1.024398771

Kurtosis

-1.325145342

Skewness

-0.246301829

Skewness

0.136789913

Range

66.1409

Range

23.034936

Minimum

26.75474

Minimum

24.407484

Maximum

92.89564

Maximum

47.44242

Sum

9422.110242

Sum

5336.556138

Count

148

Count

148

Confidence Level (95.0%)

2.564141412

Confidence Level (95.0%)

1.081874305

The study identified six problems as some of the major concerns for Sun Coast. The first major concern is job site particle pollution affecting the health of employees. It also reported that Sun Coast suffers from high job lose and this could be as a result of poor working condition. Though respirators are needed in certain environment, the particulate matter (PM) differs in size and mostly depends on the job site and project which are being undertaken. There are also other issues such as safety training effectiveness, sound level exposure, new employee training and lead exposure which senior management must address.

Regression analysis

SUMMARY OUTPUT

Regression Statistics

Multiple R

0.057185425

R Square

0.003270173

Adjusted R Square

-0.003556744

Standard Error

6.67176012

Observations

148

ANOVA

Significance F

Regression

21.32195

0.479012

0.48997

Residual

146

6498.808

44.51238

Total

147

6520.13

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Lower 95.0%

Upper 95.0%

Intercept

34.52175714

2.286143

15.10044

1.26E-31

30.00355

39.03997

30.00355

39.03997

X Variable 1

0.024127937

0.034862

0.692107

0.48997

-0.04477

0.093026

-0.04477

0.093026

It is means that there a relationship between particulate matter size and employee sick days because of the p. value is 0.48997. It means that the hypothesis is accepted. It is also obtained that there a predictive relationship between safety training expenditure and lost time hours?

Cumulative Analysis

Bin

Frequency

Cumulative %

Bin

Frequency

Cumulative %

24.407

0.00%

127

85.81%

24.407

0.00%

42.519

89.19%

25.770

0.00%

41.574

91.22%

25.770

0.00%

34.771

92.57%

25.909

0.00%

36.034

93.92%

25.909

0.00%

26.854

94.59%

26.854

0.68%

29.291

95.27%

26.854

0.68%

29.689

95.95%

27.003

0.68%

38.849

96.62%

27.003

0.68%

42.947

97.30%

27.073

0.68%

44.608

97.97%

27.073

0.68%

45.473

98.65%

27.352

0.68%

46.746

99.32%

27.352

0.68%

47.442

100.00%

27.451

0.68%

24.407

100.00%

27.451

0.68%

24.407

100.00%

27.550

0.68%

25.770

100.00%

27.550

0.68%

25.770

100.00%

27.899

0.68%

25.909

100.00%

27.899

0.68%

25.909

100.00%

28.088

0.68%

26.854

100.00%

28.088

0.68%

27.003

100.00%

29.291

1.35%

27.003

100.00%

29.291

1.35%

27.073

100.00%

29.390

1.35%

27.073

100.00%

29.390

1.35%

27.352

100.00%

29.689

2.03%

27.352

100.00%

29.689

2.03%

27.451

100.00%

29.977

2.03%

27.451

100.00%

29.977

2.03%

27.550

100.00%

30.027

2.03%

27.550

100.00%

30.027

2.03%

27.899

100.00%

30.027

2.03%

27.899

100.00%

30.037

2.03%

28.088

100.00%

30.037

2.03%

28.088

100.00%

30.037

2.03%

29.291

100.00%

30.236

2.03%

29.390

100.00%

30.236

2.03%

29.390

100.00%

30.345

2.03%

29.689

100.00%

30.345

2.03%

29.977

100.00%

30.703

2.03%

29.977

100.00%

30.703

2.03%

30.027

100.00%

31.051

2.03%

30.027

100.00%

31.051

2.03%

30.027

100.00%

31.051

2.03%

30.037

100.00%

31.081

2.03%

30.037

100.00%

31.081

2.03%

30.037

100.00%

31.240

2.03%

30.236

100.00%

31.240

2.03%

30.236

100.00%

31.240

2.03%

30.345

100.00%

31.509

2.03%

30.345

100.00%

31.509

2.03%

30.703

100.00%

31.927

2.03%

30.703

100.00%

31.927

2.03%

31.051

100.00%

31.927

2.03%

31.051

100.00%

31.927

2.03%

31.051

100.00%

31.927

2.03%

31.081

100.00%

31.927

2.03%

31.081

100.00%

32.374

2.03%

31.240

100.00%

32.374

2.03%

31.240

100.00%

32.404

2.03%

31.240

100.00%

32.404

2.03%

31.509

100.00%

33.120

2.03%

31.509

100.00%

33.120

2.03%

31.927

100.00%

33.339

2.03%

31.927

100.00%

33.339

2.03%

31.927

100.00%

33.339

2.03%

31.927

100.00%

34.771

3.38%

31.927

100.00%

34.771

3.38%

31.927

100.00%

34.771

3.38%

32.374

100.00%

34.771

3.38%

32.374

100.00%

36.034

4.73%

32.404

100.00%

36.034

4.73%

32.404

100.00%

36.034

4.73%

33.120

100.00%

36.144

4.73%

33.120

100.00%

36.144

4.73%

33.339

100.00%

36.144

4.73%

33.339

100.00%

36.144

4.73%

33.339

100.00%

36.850

4.73%

34.771

100.00%

36.850

4.73%

34.771

100.00%

36.850

4.73%

34.771

100.00%

36.850

4.73%

36.034

100.00%

37.198

4.73%

36.034

100.00%

37.198

4.73%

36.144

100.00%

37.198

4.73%

36.144

100.00%

37.198

4.73%

36.144

100.00%

37.745

4.73%

36.144

100.00%

37.745

4.73%

36.850

100.00%

37.745

4.73%

36.850

100.00%

37.745

4.73%

36.850

100.00%

38.203

4.73%

36.850

100.00%

38.203

4.73%

37.198

100.00%

38.203

4.73%

37.198

100.00%

38.203

4.73%

37.198

100.00%

38.670

4.73%

37.198

100.00%

38.670

4.73%

37.745

100.00%

38.670

4.73%

37.745

100.00%

38.670

4.73%

37.745

100.00%

38.849

5.41%

37.745

100.00%

38.849

5.41%

38.203

100.00%

38.849

5.41%

38.203

100.00%

38.849

5.41%

38.203

100.00%

41.574

7.43%

38.203

100.00%

41.574

7.43%

38.670

100.00%

41.574

7.43%

38.670

100.00%

41.574

7.43%

38.670

100.00%

42.519

10.81%

38.670

100.00%

42.519

10.81%

38.849

100.00%

42.519

10.81%

38.849

100.00%

42.519

10.81%

38.849

100.00%

42.947

11.49%

41.574

100.00%

42.947

11.49%

41.574

100.00%

42.947

11.49%

41.574

100.00%

42.947

11.49%

42.519

100.00%

44.170

11.49%

42.519

100.00%

44.170

11.49%

42.519

100.00%

44.170

11.49%

42.947

100.00%

44.170

11.49%

42.947

100.00%

44.409

11.49%

42.947

100.00%

44.409

11.49%

44.170

100.00%

44.409

11.49%

44.170

100.00%

44.409

11.49%

44.170

100.00%

44.558

11.49%

44.170

100.00%

44.558

11.49%

44.409

100.00%

44.558

11.49%

44.409

100.00%

44.558

11.49%

44.409

100.00%

44.608

12.16%

44.409

100.00%

44.608

12.16%

44.558

100.00%

44.608

12.16%

44.558

100.00%

44.608

12.16%

44.558

100.00%

45.055

12.16%

44.558

100.00%

45.055

12.16%

44.608

100.00%

45.055

12.16%

44.608

100.00%

45.055

12.16%

44.608

100.00%

45.473

12.84%

45.055

100.00%

45.473

12.84%

45.055

100.00%

45.473

12.84%

45.055

100.00%

45.473

12.84%

45.055

100.00%

45.503

12.84%

45.473

100.00%

45.503

12.84%

45.473

100.00%

45.503

12.84%

45.473

100.00%

45.503

12.84%

45.503

100.00%

46.746

13.51%

45.503

100.00%

46.746

13.51%

45.503

100.00%

46.746

13.51%

45.503

100.00%

46.746

13.51%

46.746

100.00%

47.442

14.19%

46.746

100.00%

47.442

14.19%

46.746

100.00%

127

100.00%

47.442

100.00%

Data for the company:

Sun Coast Closing

Sun Coast Return

30-Jun-2019

82.780

47.442

31-May-2019

78.510

45.503

30-Apr-2019

74.520

45.473

31-Mar-2019

70.640

44.558

28-Feb-2019

73.950

44.170

31-Jan-2019

69.910

42.947

31-Dec-2018

72.390

46.746

30-Nov-2018

71.230

45.055

31-Oct-2018

69.230

44.608

30-Sep-2018

71.410

44.409

31-Aug-2018

71.240

42.519

31-Jul-2018

74.790

41.574

30-Jun-2018

72.870

38.670

31-May-2018

69.300

38.203

30-Apr-2018

71.820

37.198

31-Mar-2018

72.310

38.849

28-Feb-2018

76.390

37.745

31-Jan-2018

78.870

36.850

31-Dec-2017

80.340

36.144

30-Nov-2017

79.430

34.771

31-Oct-2017

77.630

36.034

30-Sep-2017

75.250

33.339

31-Aug-2017

75.800

31.927

31-Jul-2017

83.730

31.240

30-Jun-2017

82.810

31.927

31-May-2017

79.650

30.027

30-Apr-2017

87.400

30.037

31-Mar-2017

85.910

31.051

28-Feb-2017

82.320

32.404

31-Jan-2017

81.660

32.374

31-Dec-2016

82.410

31.081

30-Nov-2016

78.650

33.120

31-Oct-2016

73.390

31.509

30-Sep-2016

72.400

30.345

31-Aug-2016

71.810

29.291

31-Jul-2016

77.350

27.451

30-Jun-2016

74.370

27.352

31-May-2016

77.430

27.550

30-Apr-2016

73.890

27.899

31-Mar-2016

74.920

29.689

29-Feb-2016

70.140

29.390

31-Jan-2016

78.670

28.088

31-Dec-2015

85.530

27.073

30-Nov-2015

79.430

25.909

31-Oct-2015

76.730

24.407

30-Sep-2015

72.720

25.770

31-Aug-2015

75.080

26.854

31-Jul-2015

87.087

27.003

30-Jun-2015

84.670

30.236

31-May-2015

84.631

29.977

30-Apr-2015

88.390

30.703

31-Mar-2015

92.896

45.503

28-Feb-2015

91.424

45.473

31-Jan-2015

88.848

44.558

31-Dec-2014

85.187

44.170

30-Nov-2014

80.284

42.947

31-Oct-2014

80.045

46.746

30-Sep-2014

74.883

45.055

31-Aug-2014

80.881

44.608

31-Jul-2014

83.298

44.409

30-Jun-2014

80.443

42.519

31-May-2014

81.149

41.574

30-Apr-2014

78.474

38.670

31-Mar-2014

77.022

38.203

28-Feb-2014

74.257

37.198

31-Jan-2014

73.829

38.849

31-Dec-2013

77.380

37.745

30-Nov-2013

77.400

36.850

31-Oct-2013

75.669

36.144

30-Sep-2013

70.825

34.771

31-Aug-2013

72.447

36.034

31-Jul-2013

73.809

33.339

30-Jun-2013

68.806

31.927

31-May-2013

66.499

31.240

30-Apr-2013

73.053

31.927

31-Mar-2013

67.643

30.027

28-Feb-2013

66.907

30.037

31-Jan-2013

64.102

31.051

31-Dec-2012

61.844

47.442

30-Nov-2012

59.368

45.503

31-Oct-2012

57.438

45.473

30-Sep-2012

55.469

44.558

31-Aug-2012

54.444

44.170

31-Jul-2012

57.219

42.947

30-Jun-2012

52.813

46.746

31-May-2012

49.133

45.055

30-Apr-2012

51.689

44.608

31-Mar-2012

49.829

44.409

29-Feb-2012

49.163

42.519

31-Jan-2012

50.386

41.574

31-Dec-2011

48.954

38.670

30-Nov-2011

47.144

38.203

31-Oct-2011

49.004

37.198

30-Sep-2011

45.304

38.849

31-Aug-2011

47.960

37.745

31-Jul-2011

49.004

36.850

30-Jun-2011

52.018

36.144

31-May-2011

50.347

34.771

30-Apr-2011

53.420

36.034

31-Mar-2011

52.117

33.339

28-Feb-2011

52.823

31.927

31-Jan-2011

52.177

31.240

31-Dec-2010

50.496

31.927

30-Nov-2010

48.019

30.027

31-Oct-2010

48.636

30.037

30-Sep-2010

50.894

31.051

31-Aug-2010

50.028

32.404

31-Jul-2010

52.276

32.374

30-Jun-2010

48.377

31.081

31-May-2010

51.093

33.120

30-Apr-2010

58.194

31.509

31-Mar-2010

55.986

30.345

28-Feb-2010

53.629

29.291

31-Jan-2010

52.943

27.451

31-Dec-2009

54.554

27.352

30-Nov-2009

52.515

27.550

31-Oct-2009

51.968

27.899

30-Sep-2009

51.471

29.689

31-Aug-2009

45.752

29.390

31-Jul-2009

42.569

28.088

30-Jun-2009

38.789

27.073

31-May-2009

34.950

25.909

30-Apr-2009

34.920

24.407

31-Mar-2009

34.542

25.770

28-Feb-2009

29.639

26.854

31-Jan-2009

26.755

27.003

31-Dec-2008

28.744

30.236

30-Nov-2008

33.906

29.977

31-Oct-2008

40.082

30.703

30-Sep-2008

42.390

45.503

31-Aug-2008

42.171

45.473

31-Jul-2008

39.535

44.558

30-Jun-2008

39.953

44.170

31-May-2008

42.091

42.947

30-Apr-2008

44.608

46.746

31-Mar-2008

41.584

45.055

29-Feb-2008

41.902

44.608

31-Jan-2008

49.133

44.409

31-Dec-2007

58.781

42.519

30-Nov-2007

59.328

41.574

31-Oct-2007

61.168

38.670

30-Sep-2007

56.085

38.203

31-Aug-2007

54.852

37.198

31-Jul-2007

53.967

38.849

30-Jun-2007

54.952

37.745

31-May-2007

55.101

36.850

30-Apr-2007

52.475

36.144

31-Mar-2007

49.989

34.771

Data Analysis Procedures

Using Suncoast dataset, effectiveness of training program, effects of lead exposure and difference in return of investment has been evaluated using statistical data analysis techniques in Excel data analysis Toolpak. The document includes the hypothesis, Excel outputs and interpretation of results for independent sample t-test, paired sample t-test and one-way ANOVA.

Independent Samples t-test

Hypothesis

The null and experimental hypotheses of the research measuring the effectiveness of training program are stated below.

Null hypothesis H0: The mean difference between two groups in the effectiveness of training program is equal to zero.

Experimental hypothesis H1: The mean difference between two groups in the effectiveness of training program is greater than zero.

Excel output and interpretation

Independent T-test has been chosen to check the difference as it the most suitable test to know the mean difference among two groups from different populations (Field, 2009). Figure 1 shows the Excel output of independent sample t-test. The sample comes from the different populations, group A, who received prior training and the other group B, who received the revised training.

Fig. 1. Excel output for independent sample T test

It can be observed from the figure 1 that means for two groups are not equal. Mean difference observed is equal to 14.98. The value for the test 1.93983E-15 is significantly less than 0.05. Hence, we reject the null hypothesis that the mean difference between groups is equal to zero and accept the experimental hypothesis. It can be noted that the average is greater in group B which suggests that the revised training program is better than the previous one.

Dependent Samples (Paired Samples) t Test

Paired sample t-test will be used to know the amount of lead before and after exposure as the sample meets the assumptions of paired sample t-test. The test variable has been recorded twice in the same group or same population before and after exposure (Field, 2009; Creswell & Creswell, 2017).

Hypothesis

Null hypothesis H0: The mean difference in the blood lead amounts is zero before and after the project.

Experimental hypothesis H1: The mean difference in the blood lead amounts is higher than zero before and after the project.

Excel output and interpretation

Fig. 2. Excel output for paired sample T test

It can be seen from the results that the mean difference, 0.428 microgram is closer to zero. The p value p = 0.056 suggests a slight increase after the exposure but the value rounded to two decimals, 0.06>0.05. We accept the null hypothesis that the mean difference before and after the exposure is zero. This suggests that the project does not contribute to higher level of lead in the blood of workers.

One-way ANOVA

One-way ANOVA test is used to know the differences in two or more groups for one variable Creswell & Creswell, 2017). The one-way ANOVA tells the difference in one variable measured in different groups from different samples. The variable in this study is return of investment measured for four different projects air, water, soil and training.

Hypothesis

Null hypothesis H0: No difference exists in the return of investment by the four projects.

Experimental hypothesis H1: A difference exists in the return of investment by the four projects.

Excel output and interpretation

Fig. 3. Excel output for one-way ANOVA

The excel output provided above show p value, 1.76E-06 is significantly higher than 0.05, so we reject the null hypothesis. The greatest amount of return in investment has been received from soil project with average 9.1 and second highest was air project, average 8.9. water project generated an average 7 and the least amount of return from the investment was given by training project. Therefore, the Suncoast can keep investing in air and soil projects for higher revenues.

Data Analysis: Hypothesis Testing

With the help of SPSS the data analysis was carried out. SPSS is a software to analyze the quantitative data, which is not time-consuming, and it has benefits of modeling on multilevel ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"0r7uHnwH","properties":{"formattedCitation":"(Heck, Thomas, & Tabata, 2011)","plainCitation":"(Heck, Thomas, & Tabata, 2011)","noteIndex":0},"citationItems":[{"id":174,"uris":["http://zotero.org/users/local/F0XOCTdk/items/XRADY2AW"],"uri":["http://zotero.org/users/local/F0XOCTdk/items/XRADY2AW"],"itemData":{"id":174,"type":"book","title":"Multilevel and Longitudinal Modeling with IBM SPSS","publisher":"Routledge","number-of-pages":"356","source":"Google Books","abstract":"This is the first book to demonstrate how to use the multilevel and longitudinal modeling techniques available in IBM SPSS Version 18. The authors tap the power of SPSS''s Mixed Models routine to provide an elegant and accessible approach to these models. Readers who have learned statistics using this software will no longer have to adapt to a new program to conduct quality multilevel and longitudinal analyses. Annotated screen shots with all of the key output provide readers with a step-by-step understanding of each technique as they are shown how to navigate through the program. Diagnostic tools, data management issues, and related graphics are introduced throughout. SPSS commands show the flow of the menu structure and how to facilitate model building. Annotated syntax is also available for those who prefer this approach. Most chapters feature an extended example illustrating the logic of model development. These examples show readers the context and rationale of the research questions and the steps around which the analyses are structured. The data used in the text and syntax examples are available at http://www.psypress.com/multilevel-modeling-techniques/ . The book opens with the conceptual and methodological issues associated with multilevel and longitudinal modeling, followed by a discussion of SPSS data management techniques which facilitate working with multilevel, longitudinal, and/or cross-classified data sets. The next few chapters introduce the basics of multilevel modeling, how to develop a multilevel model, and trouble-shooting techniques for common programming and modeling problems along with potential solutions. Models for investigating individual and organizational change are developed in chapters 5 and 6, followed by models with multivariate outcomes in chapter 7. Chapter 8 illustrates SPSS''s facility for examining models with cross-classified data structures. The book concludes with thoughts about ways to expand on the various multilevel and longitudinal modeling techniques introduced and issues to keep in mind in conducting multilevel analyses. Ideal as a supplementary text for graduate level courses on multilevel, longitudinal, latent variable modeling, multivariate statistics, and/or advanced quantitative techniques taught in departments of psychology, business, education, health, and sociology, this book''s practical approach will also appeal to researchers in these fields. The book provides an excellent supplement to Heck & Thomas''s An Introduction to Multilevel Modeling Techniques, 2nd Edition; however, it can also be used with any multilevel and/or longitudinal modeling book or as a stand-alone text. ps around which the analyses are structured. The data used in the text and syntax examples are available at http://www.psypress.com/multilevel-modeling-techniques/ . The book opens with the conceptual and methodological issues associated with multilevel and longitudinal modeling, followed by a discussion of SPSS data management techniques which facilitate working with multilevel, longitudinal, and/or cross-classified data sets. The next few chapters introduce the basics of multilevel modeling, how to develop a multilevel model, and trouble-shooting techniques for common programming and modeling problems along with potential solutions. Models for investigating individual and organizational change are developed in chapters 5 and 6, followed by models with multivariate outcomes in chapter 7. Chapter 8 illustrates SPSS''s facility for examining models with cross-classified data structures. The book concludes with thoughts about ways to expand on the various multilevel and longitudinal modeling techniques introduced and issues to keep in mind in conducting multilevel analyses. Ideal as a supplementary text for graduate level courses on multilevel, longitudinal, latent variable modeling, multivariate statistics, and/or advanced quantitative techniques taught in departments of psychology, business, education, health, and sociology, this book''s practical approach will also appeal to researchers in these fields. The book provides an excellent supplement to Heck & Thomas''s An Introduction to Multilevel Modeling Techniques, 2nd Edition; however, it can also be used with any multilevel and/or longitudinal modeling book or as a stand-alone text. techniques introduced and issues to keep in mind in conducting multilevel analyses. Ideal as a supplementary text for graduate level courses on multilevel, longitudinal, latent variable modeling, multivariate statistics, and/or advanced quantitative techniques taught in departments of psychology, business, education, health, and sociology, this book''s practical approach will also appeal to researchers in these fields. The book provides an excellent supplement to Heck & Thomas''s An Introduction to Multilevel Modeling Techniques, 2nd Edition; however, it can also be used with any multilevel and/or longitudinal modeling book or as a stand-alone text.","ISBN":"978-1-136-99635-1","note":"Google-Books-ID: XVOzAQAAQBAJ","language":"en","author":[{"family":"Heck","given":"Ronald H."},{"family":"Thomas","given":"Scott L."},{"family":"Tabata","given":"Lynn N."}],"issued":{"date-parts":[["2011",4,27]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Heck, Thomas, & Tabata, 2011).

The Pearson correlation coefficient p value < .05 (alpha), therefore, the null hypothesis (H01) is rejected and the alternative hypothesis (HA1) is accepted that there is a statistically significant relationship between particulate matter size and employee sick days.

The Pearson correlation coefficient r = - 0.715 indicates that particulate matter size, as measured in microns, is strongly and negatively correlated with mean annual sick days per employee. R2 = .51 indicates that 51 percent of the variability in employee sick days is explained by particulate matter size.

Correlation: Hypothesis Testing

The correlation hypothesis testing involves testing the relationship between two variables A and B (Field, 2000). In this test, the relationship of health of employees and particulate matter (PM) will be tested using chi-square correlation test, and simple regression will show the percentage of relationship between the variables. The test results will predict if the higher level of air pollution measured in microns of particulate matter's size is associated with a higher number of leaves from the employees in different sites.

Hypotheses

Null hypothesis Ho1: No statistically substantial relationship exists among particulate matter and number of sick leaves.

Experimental hypothesis Ha1: A statistically substantial relationship exists among particulate matter and number of sick leaves.

Correlation output and interpretation

Fig. 1 Correlation output

It is evident from the table that the R-square is equal to 0.513, Pearson’s coefficient r is ‘-0.715’. Hence, the two variables are 71.5% related to each other. The negative sign indicates an inverse relationship between the two. Lower the particle size, higher the number of sick leaves. It is important to note that the results of correlation analysis only show the relationship, which means it does not predict cause and effect. For example, we cannot say that particle size is causing ill health. However, there is surely a strong relationship observed among the two variables.

Therefore, we reject the null hypothesis and accept the experimental or alternative hypothesis, which states that a relationship exists between particulate matter size and a number of sick leaves.

Simple Regression: Hypothesis Testing

The regression analysis in addition to proving the relationship between the variables, determines the percentage of relationship between them (Field, 2000). The simple regression in this case will predict to what extent the safety training is correlated to lost time hours. The training has been measured by amount of money spent on safety training.

Hypotheses

Null hypothesis Ho1: Training has proven to be effective in reducing the lost-time hours.

Experimental hypothesis Ha1: Training has proven to be effective in reducing the lost-time hours.

Simple/linear regression output and interpretation

Simple Regression: Hypothesis Testing

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.

Regression Statistics

Multiple R

0.939559324

R Square

0.882771723

Adjusted R Square

0.882241279

Standard Error

24.61328875

Observations

223

ANOVA

Significance F

Regression

1008202.105

1008202.11

1664.210687

7.6586E-105

Residual

221

133884.8903

605.813983

Total

222

1142086.996

Coefficients

Standard Error

t Stat

p value

Lower 95%

Upper 95%

Intercept

273.449419

2.665261963

102.597577

2.1412E-188

268.1968373

278.702001

Safety Training Expenditure

-0.143367741

0.003514368

-40.7947385

7.6586E-105

-0.150293705

-0.13644178

The ANOVA F value < .05 (alpha) indicates that the simple regression model is statistically significant in its ability to predict the dependent variable. Therefore, the null hypothesis (H02) is rejected and the alternative hypothesis (HA2) is accepted that there is a statistically significant predictive relationship between safety training expenditure and lost-time hours.

Linear regression outputs from the data provided have been presented below.

Fig.2 (a) Scatterplot showing correlation training expenses and lost time hours

The scatterplot above shows inverse relationship between the two variables. The rise in money spent on training is accompanied by the reduction of a number of lost hours. The plot shows the Pearson’s coefficient (r) 0.1434. Hence, the relationship between the two variables was 14.34%. It is important to note that the outlier exists, which was removed for better interpretation of results.

Fig.2 (b) Scatterplot showing correlation training expenses and lost time hours after removal of outliers

The scatterplot after removal of outlier is presented above. The relationship after removing outliers is 14.37% which is not statistically different from that obtained before removal of the outlier.

Fig.3 (a) Simple regression test result

It is evident from the table above that r square value is 0.883. Hence, the results show that the variables are 88% related to each other. The multiple R is 0.939, R square 0.883, ANOVA F value 1851.86 and alpha a value is 273.45.

Fig.3 (b) Simple regression test result

Y = bx + a

Where b is safety training expenditure and a is intercept. Hence,

Y = -0.143 * x + 273.45

The regression equation shows that lost time hours can be obtained by multiplying x to -0.143 and adding 273.45. Considering the above results, we do reject the null hypothesis and accept the experimental hypothesis that safety training is effective in reducing the lost time hours.

Multiple Regression: Hypothesis Testing

The multiple regression is used to study a number of factors linked to a variable Creswell & Creswell, 2017). In this case, it is tested if the variables like angle, velocity, chord length, frequency and displacement are contributing to noise measured in decibels.

Hypotheses

Null hypothesis Ho1: The variables frequency, angle, chord length, velocity and displacement do not contribute to noise (dB).

Experimental hypothesis Ha1: The variables frequency, angle, chord length, velocity and displacement contribute to noise (dB).

Multiple regression output and interpretation

Multiple Regression: Hypothesis Testing

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.

Regression Statistics

Multiple R

0.601841822

R Square

0.362213579

Adjusted R Square

0.360083364

Standard Error

5.51856585

Observations

1503

ANOVA

Significance F

Regression

25891.88784

5178.377569

170.0361467

2.1289E-143

Residual

1497

45590.48986

30.45456904

Total

1502

71482.3777

Coefficients

Standard Error

t Stat

p value

Lower 95%

Upper 95%

Intercept

126.8224555

0.623820253

203.2996763

125.5988009

128.0461101

Frequency (Hz)

-0.0011169

4.7551E-05

-23.48846042

4.0652E-104

-0.001210174

-0.001023627

Angle in Degrees

0.047342353

0.037308069

1.268957462

0.204653501

-0.025839288

0.120523993

Chord Length

-5.495318335

2.927962181

-1.876840613

0.060734309

-11.23866234

0.248025671

Velocity (Meters per Second)

0.083239634

0.009300188

8.950317436

1.02398E-18

0.064996851

0.101482417

Displacement

-240.5059086

16.51902666

-14.55932686

5.20583E-45

-272.9088041

-208.103013

The ANOVA F value < .05 (alpha) indicates that the multiple regression model is statistically significant in its ability to predict the dependent variable. Therefore, the null hypothesis (H03) is rejected and the alternative hypothesis (HA3) is accepted that there is a statistically significant predictive relationship between the independent variables in the model and dependent variable of decibel level.

Further analysis determined that frequency, velocity, and displacement coefficient p values < .05 (alpha); therefore, these are the only variables that are statistically significant in their ability to predict decibels.

The correlation coefficient of r = 0.60 indicates that frequency, velocity, and displacement are moderately to strongly correlated with decibel level. R2 = 0.36 indicates that 36% of the variability in decibel level is explained by frequency, velocity, and displacement.

Decibel level can be predicted by the following linear equation:

Y = a + b1X1 + b2X2 +…+ bnXn

dB = 126.8 + (-0.0011)(Frequency) + (.0.047)(Angle in Degrees) + (-5.49)(Chord Length) + (.083)(Velocity) + (-240.5)(Displacement)

The excel outputs of multiple regression analyses have been presented below.

Fig.4 (a) Multiple regression output

The Multiple R value observed is 0.00, R square is 0.36 and ANOVA F value 170.03.

Fig.4 (b) Multiple regression output

As evident from the table above, the regression outputs display non-significant outputs for angle and chord length and significant values for frequency, velocity, and displacement. Therefore, the velocity, frequency, and displacement contribute to noise at workplace. The amount of noise can be predicted by following equation if the frequency, velocity and displacement are known.

Dependent variable (Noise) = 126.82 – 0.00 (frequency) + 0.08(velocity) – 240(displacement), where 126.82 is constant alpha value.

Findings

The study discovered that health and safety matters are key issues for Sun Coast workers. It is also pointed that there are significant correlations between the lost time hours and safety training expenditure. The study also established that there is relationship between health and safety of workers and the performance of Sun Coast. The study also established that Sun Coast also works around the clock to offer a better working environment for all workers through elimination of substances which can hinder growth of workers.

RO1: To determine if there is a relationship between the size of PM (particulate matter) size and employee health.

RO2: To determine if the safety training has been successful to reduce the lost-time hours

RO3: To determine if the frequency, chord length, velocity and displacement can be used to predict dB levels.

RO4: To know if the revised training program is better than the prior training program.

RO5; To determine if there is any difference in the level of lead in the blood of employees before and after the lead remediation program.

RO6; To determine if the differences exist in return on investment for air monitoring, water reclamation, soil remediation, and health and safety training projects.

Recommendations

Employees working at the job site to remediate must be efficiently monitored to ensure the company can be able to achieve its core objectives. Health and safety were highlighted as major issues which the company should address effectively.

References

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