Instructions-Research PaperFinal ProjectNow that you have completed the first six assignments, it is time to complete your research project for the course. Include the following sections in your submission.§ Title Page§ Table of Contents§ Executive Summary§ Introduction§ Statement of the Problems§ Literature Review§ Research Objectives§ Research Questions and Hypotheses§ Research Methodology, Design, and Methodso Research Methodologyo Research Designo Research Methodso Data Collection Methodso Sampling Designo Data Analysis Procedures§ Data Analysis: Descriptive Statistics and Assumption Testing§ Data Analysis: Hypothesis Testing§ Findings§ Recommendations§ ReferencesPlease follow the Unit VII project template here to complete your assignment.Please refer to the Course Project Guidance document here for help.The title and reference pages do not count toward the page requirement for this assignment. This assignment should be no less than three pages in length, follow APA-style formatting and guidelines, and use references and citations as necessary.Resources
Instructions-Research Paper Final Project Now that you have completed the first six assignments, it is time to complete your research project for the course. Include the following sections in your sub
Running head: SUN COAST REMEDIATION RESEARCH PROJECT 0 Sun Coast Remediation Research Project Columbia Southern University RESEARCH OBJECTIVES Sun Coast’s main objective is the welfare and safety of its employees. Through research, the company will obtain valuable information that will guarantee the health, welfare, and safety of its employees while minimizing worksite injuries. Sun Coast has put together a team of experts that have identified 6 health and safety risks and concerns that affect employees at the sites. These particular matters will require further research. This study will pinpoint and examine if there is a relationship that involves PM(particular matter) and the exposure and impacts it has on its employees relatively to size and employee health, noise levels, lead poisoning, and training. Their objective is to compare outdated safety modules or data with the newly formed safety training material to determine its effectiveness and consistent with reducing lost-time hours or the predicting thereof as it pertains to training. The organization main objective is to test the condition of its data and then make decisions based upon its return investments concerning various training and testing of its land sites. The following return investments comprise the organization’s safety training program, air quality and control monitoring, soil outcome testing, and the water recovery program. RO1: To determine the related variables between the size of the particulate matter and employee health impactions. RO2: To determine if lost-time hours are directly related to safety training. RO3: To determine through data collected whether it predicts the decibel level of healthy work environments. RO4: To determine if the training programs with various revision is more actual than those that have been archived. RO3: To determine if the blood lead level has increased in employees working on the site. RO2: To determine the existence of the return-on-investment controls within the services they provide. RESEARCH QUESTIONS AND HYPOTHESIS After carefully reviewing all data collected, we have formulated questions with the corresponding hypothesis for each question. The following are the research questions and the hypothesis that will help the researcher to achieve the results of the study. RQ1: What is the relationship between variables that determine the size of particulate matter and employee health impactions? H01: There is no significant correlation between variables of particulate matter and employee health impactions of decline. HA1: There is a statistically significant correlation between variable particulate matter size and employee health impactions of decline. RQ2: What is the relationship between safety and training program expenses as it relates to the reduction of lost-time hours encompassing training hours? H02: There is no significant correlation between training program expenses and losing hours while training. HA2: There is a significant correlation between training expenses and reducing lost-time hours. RQ3: What is the relationship between decibel level work environments and healthy work environments? H03: There is no significant correlation between data that can determine if the decibel level work environments are healthy prior to place employees on sites. HA3: There is a significant correlation between data that substantiates a work-free healthy environment free of decibel levels versus environments that have been surveyed with decibel levels before the arrival of employees on various sites. RQ4: What is the relationship between the revision of and effectiveness of training programs in contrary to outmoded and inadequate training programs? H04: There is no significant correlation between the revision of and effectiveness of training programs in contrary to outmoded and inadequate training programs. HA4: There is a significant correlation between the revision of and effectiveness of training programs in contrary to outmoded and inadequate training programs. RQ5: What is the relationship between the employee blood level rising and lead poisoning? H05: There is no statistical correlation between data collected which determines the employees’ blood level rises and lead poisoning. HA5: There is a statistical correlation between data collected which determines if the employee’s blood levels rise and lead poisoning. RQ6: What is the relationship between an investment return and services rendered for evaluation and increase financial gains? H06: There is no statistical correlation that determines an investment return between services rendered and evaluated for increased financial gains. HA6: There is a statistical correlation between demonstrations of return investments and service and evaluated for the increase of financial gains. REFERENCES Evaluation of nonproduction area air and surface lead levels, employee blood lead levels, and psychosocial factors at a battery manufacturing plant. (2018). doi:10.26616/nioshhhe201302263314 Environmental Impact Statements for Noise. (1976). The Impact of Noise Pollution, 419-424. doi:10.1016/b978-0-08-018166-0.50036-1 Hadfield, L. (2011). Health hazard evaluation report: HETA-2008-0155-3131, lung function (spirometry) testing in employees at a flavorings manufacturing plant – Indiana. doi:10.26616/nioshheta200801553131 Toxicity Assessment. (n.d.). doi:10.1002/(issn)10982256 Wells, A. T., & Hopper, P. L. (1992). Measuring Hearing Protection Device Performance Using the Metrosonics db-3100 Sound Level Analyzer(Dosimeter). doi:10.21236/ada260852 Zhan, C. (2019). Health Services Information: Patient Safety Research Using Administrative Data. Health Services Evaluation, 241-264. doi:10.1007/978-1-4939-8715-3_12
Instructions-Research Paper Final Project Now that you have completed the first six assignments, it is time to complete your research project for the course. Include the following sections in your sub
Running head: SUN COAST REMEDIATION RESEARCH PROJECT 0 Sun Coast Remediation Research Project Columbia Southern University RESEARCH OBJECTIVES The main objective of the Sun Coast remediation is to ensure that the welfare and the safety of the employees are catered for. This research that will be done to ensure that the company get valuable information that will ensure that the employee’s welfare, health and safety are carefully considered. It will also ensure that the worksite injuries that might occur are also minimized. The company put about ten safety risks and health concerns that affect the welfare of the employees at the work station. This matter will thus require well done research. This study that I about to do will be able to determine if there is a significant relationship that involves the particulate with the exposure and the impacts that the PM has on the employee’s relatively to their health, lead poisoning, noise levels and training. The objective is to make a comparison on the outdated safety modules and data with its newly formed training equipment that are safe so as to help in determining its effectiveness and its consistency with the reduced lost time hours and the prediction when it comes to training activities. The main objective of the organization is to test the data condition and make appropriate decisions on the basis of return on investments that is concerned with the training as well as with the testing that has to do with the land sites. The return on investments will be comprise of the safety training programs of the organization, the quality of air and monitoring control, water recovery programs and lastly the soil outcome on testing. RO1: To establish whether the lost-time hours are related to safety training at the work place. RO2: To establish whether there exists relationship between variables that exist between the size of the particulate matter and the impact on the health of the employees. RO3: To establish whether training programs that is done together with revision is more effective than the prior training programs. RO4: To establish whether the blood lead level has been increasing in employees working at the site. RO5: To establish the existence of the return-on-investment controls that exist within the services that are provided. RO6: To establish through the data that are collected if the desired level of healthy work environment is predicted. RESEARCH QUESTIONS AND HYPOTHESIS The following research questions were carefully formulated after a careful review of the data that was collected. The research questions and the hypothesis that will help the researcher to achieve the desired outcomes were written below. RQ1: What is the relationship that exist between expenses on the safety and training program with the reduction of lost-time hours? H01: There is no significant correlation between lost hours while training and the expenses on training. HA1: There is a significant correlation between lost time hours while training and the expenses on training. RQ2: What is the relationship between variables determining the size of PM and the health impact on the employees? H02: There is no significant correlation between variables of PM and health impact on the employees. HA2: There is a statistically significant correlation between variables of PM and the health impact of the employees. RQ3: What is the relationship between the revision of the various training programs and the training that is archived? H03: There is no significant correlation between the revision associated with the training programs of group A scores with the archived training programs of group B scores. HA3: There is a significant correlation between the revision associated with the training programs of group A scores with the archived training programs of group B scores. RQ4: Determine the relationship between the level of blood of the employees and lead poisoning? H04: There is no statistical correlation between the level of blood of the employees and lead poisoning. HA4: There is a statistical correlation between the level of blood of the employees and lead poisoning. RQ5: Determine the relationship between return on investment and the services that are used to evaluate the financial gain? H05: There is no statistical correlation that exists between returns on investment and the services that render financial gain? HA5: There is a statistical correlation between returns on investments and services that are used to render financial gains. RQ6: Determine the relationship that exists between the desired level of a work environment and the noise level of environment? H06: There is no significant correlation between desired level of work environment and the noise level environment that exist. HA6: There is a significant correlation between desired level of work environment and the noise level environment that exist. S Environmental Impact Statements for Noise. (1976). The Impact of Noise Pollution, 419-424. doi:10.1016/b978-0-08-018166-0.50036-1 Hadfield, L. (2011). Health hazard evaluation report: HETA-2008-0155-3131, lung function (spirometry) testing in employees at a flavorings manufacturing plant – Indiana. doi:10.26616/nioshheta200801553131 Toxicity Assessment. (n.d.). doi:10.1002/(issn)10982256 Wells, A. T., & Hopper, P. L. (1992). Measuring Hearing Protection Device Performance Using the Metrosonics db-3100 Sound Level Analyzer(Dosimeter). doi:10.21236/ada260852 Evaluation of nonproduction area air and surface lead levels, employee blood lead levels, and psychosocial factors at a battery manufacturing plant. (2018). doi:10.26616/nioshhhe201302263314 Zhan, C. (2019). Health Services Information: Patient Safety Research Using Administrative Data. Health Services Evaluation, 241-264. doi:10.1007/978-1-4939-8715-3_12
Instructions-Research Paper Final Project Now that you have completed the first six assignments, it is time to complete your research project for the course. Include the following sections in your sub
Running head: SUN COAST CORRELATION AND REGRESSION ANALYSIS 0 Sun Coast Correlation and Regression Analysis Columbia Southern University Data Analysis: Hypothesis Testing Correlation and Regression tests are two parametric approaches that create data that show and describe the relationship and differences between groups, populations, or samples. The correlation tests and regression tests have been applied to the Sun Coast Safety Project. (Creswell & Creswell, 2018). Correlation: Hypothesis Testing Ho1: The first hypothesis is that there is no statistical significance between micron and the mean annual sick days per employee. Ha1: The second hypothesis that is being tested is that there is a statistical significance between micron and the mean annual sick days per employee. Correlation data Column 1 Column 2 Column 1 Column 2 -0.71598 A Pearson correction coefficient of r=0.716 shows that there is a moderately strong positive correlation. It equates to an r2 of .51, which exemplifies 51 % of the variance between the independent and dependent variables. Using an alpha of .05, the test result indicates there is a p-value of 1.05 < .05. Therefore, the null hypothesis is accepted, and the alternative hypothesis is rejected that there is a statistically significant relationship between micron and the number of sick leaves. The P-value and multiple R were obtained by running the data on sheet one using simple regression. Simple regression for sheet one Regression Statistics Multiple R 0.719543 R Square 0.517742 Adjusted R Square 0.512919 Standard Error 1.299576 Observations 102 ANOVA df SS MS F Significance F Regression 181.3162 181.3162 107.3578 1.59919E-17 Residual 100 168.8897 1.688897 Total 101 350.2059 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 10.01017 0.309974 32.29362 1.05E-54 9.395194602 10.62515 9.395195 10.62515 -0.51501 0.049705 -10.3614 1.6E-17 -0.613625981 -0.4164 -0.61363 -0.4164 Simple Regression: Hypothesis Testing Ho2: There lacks a statistical relationship between safety training hours and lost time hours as the predicted outcome Ha2: There is a statistical relationship between safety training hours and lost time hours as the predicted outcome. SUMMARY OUTPUT Regression Statistics Multiple R 0.939559 R Square 0.882772 Adjusted R Square 0.882241 Standard Error 24.61329 Observations 223 ANOVA df SS MS F Significance F Regression 1008202 1008202 1664.211 7.6586E-105 Residual 221 133884.9 605.814 Total 222 1142087 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 273.4494 2.665262 102.5976 2.1E-188 268.1968373 278.702 268.1968 278.702 X Variable 1 -0.14337 0.003514 -40.7947 7.7E-105 -0.150293705 -0.13644 -0.15029 -0.13644 The multiple r coefficient of r= 0.939 shows that there is a very strong correlation between lost time hours and safety and training. This equates to a multiple R of .88, explaining 88 % of the variance between the independent and dependent variables. With a large Anova F value of 1664. 211, there is a clue however that something is also significant in the relationship between the independent and dependent variables. Using an alpha of .05, the results indicate a p-value of 2.1< .05. A larger than 0.05 P-value indicates that the values do not fit well into the line (Zou, Tuncali, & Silverman, 2003). Therefore, the null hypothesis is accepted, and the alternative hypothesis is rejected that there is a statistically significant relationship between safety training hours and lost time hours. The x variable coefficient indicates a p-value of 7.7<.05, a statistical result that confirms that it is not statistically significant in the regression model. Dv=273.4494+-0.14337, which indicates that the model is nor predictive Multiple Regressions: Hypothesis Testing Ha3: There is no statistical relationship between decibel as the predicted outcome and frequency, angle in degree, chord length and velocity. Ha3: There is a statistical relationship between decibel as the predicted outcome and frequency, angle in degree, chord length and velocity. SUMMARY OUTPUT Regression Statistics Multiple R 0.602018 R Square 0.362425 Adjusted R Square 0.360294 Standard Error 5.519422 Observations 1502 ANOVA df SS MS F Significance F Regression 25906.34 5181.267 170.0782477 2.0796E-143 Residual 1496 45574.18 30.46402 Total 1501 71480.51 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 126.8097 0.624161 203.1683 125.5853697 128.034 125.5854 128.034 800 -0.00112 4.76E-05 -23.4962 3.6392E-104 -0.001211363 -0.00102 -0.00121 -0.00102 0.046383 0.037337 1.242292 0.214323474 -0.02685487 0.119621 -0.02685 0.119621 0.1809 -5.41565 2.930439 -1.84807 0.064789628 -11.16386013 0.332552 -11.1639 0.332552 71.3 0.083527 0.00931 8.971829 8.51468E-19 0.065265095 0.101789 0.065265 0.101789 0.002663 -240.385 16.52241 -14.549 5.9646E-45 -272.7947344 -207.976 -272.795 -207.976 The multiple R coefficient of R= .60 indicates a moderately strong correlation between decibel as the predicted outcome and frequency, angle in degree, chord length and velocity. This equates to an R2 of .36, explaining 36 % of the variance between the variables being tested. Using an alpha of .05, the results indicate a p-value of 0.21 < .05. Therefore, the null hypothesis is rejected, and the alternative hypothesis is accepted that there is a statistically significant relationship. The x variable coefficients indicate a p-value of 0.21, 0.06, for frequency and chord length respectively <.05 which shows that there are statistically significant in the regression model. The other three variables of frequency, velocity, and distribution have a greater significance level than their alpha values hence are statistically insignificant in the regression model (Levine, Berenson, Stephan, & Lysell, 1999). A significantly large Anova F statistic of 170.08 indicates that some variables are statistically significant in the prediction model. Therefore, the derived predictive model is: Dv=0.046383+-5.4, which indicates that the model is nor predictive. All the other variables are excluded because they have no statistical significance. References Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Thousand Oaks, CA: Sage. Levine, D. M., Berenson, M. L., Stephan, D., & Lysell, D. (1999). Statistics for managers using Microsoft Excel (Vol. 660). Upper Saddle River, NJ: Prentice-Hall. Zou, K. H., Tuncali, K., & Silverman, S. G. (2003). Correlation and simple linear regression. Radiology, 227(3), 617-628.
Instructions-Research Paper Final Project Now that you have completed the first six assignments, it is time to complete your research project for the course. Include the following sections in your sub
Running head: ADDRESSING SUN COAST’S PROBLEMS THROUGH RESEARCH 0 Addressing Sun Coast’s Problems through Research Columbia Southern University Literature Review (Song et al., 2015) are the faculty members of Tsinghua University, Beijing in the school of environment. The author believes that global sustainable remediation has become one of the leading trends in the contaminated land remediation field. Therefore, the main purpose of the study is to develop an effective sustainable remediation assessment indicator applicable in China including all other countries of the world. The tools like correlation and ANOVA have been used in the study for quantitative design research. The study made use of the LCA method applying the indicator to remediation mega-site in China for evaluating the impact over waste, worker safety, local impact, and resources. The study delivers a result that the indicator is appropriate for ensuring green and sustainable remediation. Sun Coast benefits from the study as it could use the same indicator for improving the safety of its workers during their work on the contaminated sites; thus, putting a positive impact on the organization. (Cappuyns, 2016) is currently working at the Centre for Economics and Corporate Sustainability in Belgium. The study attempts to evaluate the involvement of social aspects in the sustainability assessment of remediation projects. It makes use of decision support tools in the sustainability assessment of remediation project for analyzing the consideration of social aspects in the same tools. The study makes use of the ANOVA & t-test method for its quantitative design research to meet its objectives. The study finding reveals that the level of social factors involved in the relevant indicator is high in terms of human health and safety, neighborhood and locality, ethics, and equality. However, it is low in terms of uncertainty and evidence and specific legislation. The study results are beneficial for Sun Coast as it could make use of the same decision support tool for evaluating the sustainability at their remediation sites to meet its objectives; thus, putting a positive impact on the organization. (Damalas & Eleftherohorinos, 2011) are the present faculty members of the University of Thrace and the University of Thessaloniki at the Department of Agriculture. The study attempts to evaluate the effective use of pesticides at the remediation sites along with the application of alternative coping systems through regression and correlation method for its quantitative design research. The results of the study provide information regarding the appropriate use of cropping and pesticide utilization at the remediation sites for reducing the adverse impact on human health and the environment. The study carries extreme significance for the Sun Coast as the organization could make use of the same methodology for achieving its health and safety objectives oriented around their employees; thus, putting a positive impact on the organization. (Tang et al., 2012) are currently the faculty members in the school of environmental science and engineering at Sun Yat-Sen University, Guangzhou. The study attempts to evaluate, compare, and analyze the non-food energy and fiber plants carrying the potential of supplying excessive renewable energy resources and economic benefits at the remediation sites to the organizations. The study makes use of regression and correlation methods for its quantitative design research to evaluate the current options. The study finding reveals the information regarding the soil types, plants, and agronomic activities carrying the potential of boosting economic results for the organization from a remediation site. The study results are valuable for Sun Coast as the company could make use of the same strategies for improving their productivity; while ensuring appropriate safety to its employees; thus, putting a positive impact on the growth and success of the organization. (Hou & Al-Tabba, 2014) are the faculty members at the Department of Energy at the University of Cambridge. The authors state that the land is a critical element for the life support system as well as for the production of economic systems. The purpose of the study is to carry out an evaluation of sustainable remediation for the effective remediation of the project sites by developing and implementing various effective, norms and standards for practitioners. The study makes use of regression, t-test, and ANOVA for its quantitative design research. The findings of the study reveal that sustainability must be ensured for all the environmental remediation sites as it owes various effective implications towards regulation, liability owners, technology vendors, contractors, and consultants. The study carries much value for Sun Coast as the organization could make use of sustainable approach identified in the study for improving the productivity of their site along with providing effective health and safety care to its employees; thus, putting a positive impact on the organization. (Ridsale & Noble, 2016) are the faculty members at the Department of Geography and Planning at the University of Saskatchewan, Canada. The main purpose of the study is to investigate the sustainability in the remediation frameworks for providing effective guidance regarding practical implementation. The study makes use of correlation, regression, and ANOVA method in its quantitative design research to meet the objectives. The findings of the study provide information that there are not perfect criteria for remediation sites as trade-offs are always present. Therefore, the organizations working on remediation sites must properly analyze the trade-offs of making the right selection. The findings of the study are effective for the Sun Coast as the organization could analyze and select the appropriate criteria for meeting its health and safety objectives towards its employees; thus, putting a positive impact on the organization. References Cappuyns, V. (2016). Inclusion of social indicators in decision support tools for the selection of sustainable site remediation options. Journal Of Environmental Management, 184, 45-56. doi: 10.1016/j.jenvman.2016.07.035 Damalas, C., & Eleftherohorinos, I. (2011). Pesticide Exposure, Safety Issues, and Risk Assessment Indicators. International Journal Of Environmental Research And Public Health, 8(5), 1402-1419. doi: 10.3390/ijerph8051402 Hou, D., & Al-Tabbaa, A. (2014). Sustainability: A new imperative in contaminated land remediation. Environmental Science & Policy, 39, 25-34. doi: 10.1016/j.envsci.2014.02.003 Ridsdale, D., & Noble, B. (2016). Assessing sustainable remediation frameworks using sustainability principles. Journal Of Environmental Management, 184, 36-44. doi: 10.1016/j.jenvman.2016.09.015 Song, Y., Hou, D., Zhang, J., O'Connor, D., Li, G., & Gu, Q. et al. (2018). Environmental and socio-economic sustainability appraisal of contaminated land remediation strategies: A case study at a mega-site in China. Science Of The Total Environment, 610-611, 391-401. doi: 10.1016/j.scitotenv.2017.08.016 Tang, Y., Deng, T., Wu, Q., Wang, S., Qiu, R., & Wei, Z. et al. (2012). Designing Cropping Systems for Metal-Contaminated Sites: A Review. Pedosphere, 22(4), 470-488. doi: 10.1016/s1002-0160(12)60032-0
Instructions-Research Paper Final Project Now that you have completed the first six assignments, it is time to complete your research project for the course. Include the following sections in your sub
Running Head: UNIT III SCHOLARLY ACTIVITY 0 Unit III Scholarly Activity Columbia Southern University Research Methodology, Design, and Methods Sun Coast has a challenge in promoting the safety of its employees. The management of the Sun Coast has not established effective measures to curb the impacts of lead on its staff. The management of the Sun Coast must come up with means of addressing the safety issues that it is facing. In this discussion, the problems facing the Sun Coast will be reviewed by conducting thorough research. The research methodology that will be employed here is quantitative research. The quantitative research design focus will be descriptive in nature. This scientific method of analyses will be used in this study to allow statistical data collected to be analyzed properly avoiding misinterpretation and manipulation of resulting facts. Research Methodology The form of research methodology utilizing numerical data, statistical values, and documentation of phenomena known quantitative research will be employed here is a positivist worldview. The positivist worldview focuses on hypotheses, dissecting data to determine its justification or explanation based on actual events and facts which validate their existence. According to the positivist worldview, science is the sole way of learning about the truth. The positivism worldview stresses that it is only the "truthful" knowledge acquired through observation, including measurement, which is trustworthy (Nelson, 2018). There are various reasons why the positivist worldview will be employed in this research. First, it will make the research more reliable as it relies on quantitative data. Secondly, the positivist worldview is more scientific in its research methodology hence producing trustful study results. Additionally, the positivism provides objective data that researchers can utilize in making scientific assumptions. The information used in its study will be useful in addressing various safety and health concerns of the company. Research Design The research design is descriptive in nature. The project utilizes a descriptive design because it allows the researcher “to gather quantifiable information that can be used to statically analyze a target audience or particular subject” (CIRT, 2019). The information collected will be analyzed scientifically resulting in data controls voided of behavioral influences or manipulation. Research Methods The study will employ a correlative research method with a descriptive research design. Correlative research is crucial in testing the relationship between two variables (Creswell & Creswell, 2018). It is the analysis of “two variables and assesses the statistical relationship between them with little or no effort to control extraneous variables” (Price, Jhangiani, & Chiang, 2015). The variables that will be tested in this study include time hours lost while training and training expenses, PM and health impact on the employees, revision of the various training programs present and prior training programs that have been archived, of blood levels of the employees increased and the lead poisoning on sites, returns on investment and the services that render financial gain, and desired level of work environment and the noise level environment that exist. The primary reason as to why a correlation research method was employed is due to its effectiveness in measuring the relationship between different variables. Correlational research is also crucial in determining how an incident is caused (Creswell & Creswell, 2018). Further, the correlation research compares the variables between collected data and previously collected data without the help of other sources of information from various studies. The scientific method of research has proved to be very reliable due to its effectiveness in all types of organizational studies (Savkovic-Stevanovic, 2017). Data Collection Methods The primary method employed in data collection is observation (Harwoodn & Hutchinson, 2018). The employees of the Sun Coast will be observed while in their daily organizational operations. Interviews will also be used in collecting data. The employees and the management of the Sun Coast will be interviewed. The meetings will be focused on underpinning the issues facing the employees on Sun Coast. Other methods of collecting data are the analysis of archival data collected previously from the organization which will help in identifying established health and safety measures of the organization. Sampling Design The sampling design that will be utilized in this research is the convenience sample (Creswell & Creswell, 2018). A convenience sample consists of people who are reached quickly. Convenience sampling was used in this study due to several reasons. First, it will save time and money as the subjects will be easily located. Secondly, it will ensure that the data is readily available. Moreover, the sampling method is crucial in pilot studies. Data Analysis Procedures The data analysis procedure will employ various methods. First, a simple regression will be used. Simple regression utilizes the values from available data set comprising of measurements of the two variables in developing a model that helps predict the dependent variable amount (Creswell & Creswell, 2018). Example: This procedure will test the RQ1 hypotheses to deconstruct the data, pinpoint the location of controlled values that determine whether statistically, a lack of safety training will have an impact on the number of lost time hours for employees. Secondly, the multiple regression will be applied in learning the more about the link between independent and dependent variables (Creswell & Creswell, 2018). Example: This procedure will test the RQ2 hypotheses because the multiplication of data sources can change the outcome of a single hypothesis. Multiple regression data collected will be applied from former contracts to make useful decisions to engage the decibel ranges of Sun Coast work sites/environments. Thirdly, the independent t-test will be involved in comparing the means of two separate groups to conclude whether there exists a statistical proof that the related population means are considerably different (Creswell & Creswell, 2018). Example: This procedure will test the RQ3 hypotheses to determine the average scores of the previous training to compare with the scores of the active training, and then determine which one is more effective. Additionally, a paired sample t-test will be exercised to validate and signify the existence in differences between two observations will equal zero means. Example: This procedure will test the RQ4 hypotheses to determine if two separate groups and on different occasions was tested before and after working in a toxic environment at the organization. Finally, the one -way ANOVA testing will be used to determine whether there are any statistically significant differences among the study groups (Creswell & Creswell, 2018). Example: This procedure will test the RQ5 hypotheses to determine if all lines of service offer the same return on investment. References Center of Innovation in Research and Teaching. (2019). Overview of Descriptive Research. Retrieved from https://cirt.gcu.edu/research/developmentresources/research_ready/descriptive/overview Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approach (5th ed.). Thousand Oaks, CA: Sage Harwoodn, E., & Hutchinson, E. (2018). Data Collection Methods Series Part 5. Journal Of Wound, Ostomy And Continence Nursing, 36(5), 476-481. DOI: 10.1097/won.0b013e3181b35248 Nelson, E. S. (2018). Dilthey and Carnap: The Feeling of Life, the Scientific Worldview, and the Elimination of Metaphysics. The Worlds of Positivism, 321-346. doi:10.1007/978-3-319-65762-2_12 Price, P., Jhangiani, R., Chiang, I. (2015). Overview of Nonexperimental Research. Retrieved from https://opentextbc.ca./researchmethods/chapter/correlational-research/ Savkovic-Stevanovic, J. (2017). Modeling Method in the Scientific Research. Science Research, 3(3), 66. DOI: 10.11648/j.sr.20150303.14
Instructions-Research Paper Final Project Now that you have completed the first six assignments, it is time to complete your research project for the course. Include the following sections in your sub
ANOVA and T-test Empirical research requires an investigator at some time to test whether the hypothesis they made should be rejected or accepted. ANOVA and T-test are the parametric statistical measures that are commonly used in the dispensation. ANOVA is often used to compare the mean of two or more groups, while the T-test is used when only two groups are involved (Rojewski, Lee, and Gemici). The T-test is preferable when comparing the mean between two groups, with the aim of identifying whether the population means of both samples greatly differ from one another. An example would include comparing whether monetary incentives are related to employee turnover. The investigator in the instance may collect data from both the test group and the control or placebo group. In order to confirm whether the resulting data sets are from the same sample population, a T-test is conducted to confirm how far the mean differs between the two data sets. The use of the T-test in the instance assumes, however, that the variable is normally distributed, but may also have unknown variances. ANOVA, on the hand, is appropriate for use when there are more than two groups that are being compared. In the case of employee turnover, the investigator may choose to collect data on three groups mainly, permanent, casuals, and the control group. Results may indicate that permanent employees have a high percentage of turnover, but also at a very low standard of deviation, while casuals have low turnover at a high standard deviation. In order to eliminate the effect of unknown variance, ANOVA is appropriate to determine how far among each group were the means different. That being said, the T-test is only appropriate to use when a small sample is involved and the groups to be compared are equal to two. References Rojewski, Jay, In Heok Lee, and Sinan Gemici. "Use of t-test and ANOVA in career-technical education research." Career and Technical Education Research 37.3 (2012): 263-275.
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- Click on the “Place Order” tab at the top menu or “Order Now” icon at the bottom and a new page will appear with an order form to be filled.
- Fill in your paper’s requirements in the "PAPER DETAILS" section.
- Fill in your paper’s academic level, deadline, and the required number of pages from the drop-down menus.
- Click “CREATE ACCOUNT & SIGN IN” to enter your registration details and get an account with us for record-keeping and then, click on “PROCEED TO CHECKOUT” at the bottom of the page.
- From there, the payment sections will show, follow the guided payment process and your order will be available for our writing team to work on it.