Before making any decisions regarding the promotion of occupational health and the reduction of dust and particulate matter exposure, Sun Coast should evaluate the current situation. To do so, it needs to formulate a research framework and carry it out in practice, obtaining data and analyzing it. Such a structure will usually consist of six parts: research methodology, research design, research methods, data collection methods, sampling design, and data analysis procedures.
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There is a variety of options for each of these aspects that can change based on the nature of the study. As such, it is critical to settle on a particular approach before beginning the study to avoid inconsistencies. This paper will detail each of the six aspects and provide the reasoning behind specific choices.
There are two primary approaches to methodology, which are not mutually exclusive: qualitative and quantitative. The former discusses non-numerical data, trying to understand the meaning and process of a phenomenon. The latter works with numbers, applying statistics to data sets to determine whether specific relationships are likely to exist. In the case of Sun Coast, the mechanisms of dust and particulate matter exposure and their influence of health are already well understood. It is more important to understand whether it represents a substantial danger to the company’s employees and needs to be addressed. To that end, a quantitative approach would be more suitable than a qualitative one. It can determine whether PM is a danger and which methods are effective at reducing the hazard.
Research designs are typically separated into three types: exploratory, descriptive, and causal. Each is intended for a particular type of problem, and therefore, the choice of research design can influence the outcome of the study substantially. The causal design may be the most appropriate in this case because it coincides with the research questions. According to Emilien, Weitkunat, and Lüdicke (2017), it is used to establish cause-and-effect relationships. Sun Coast wants to know whether PM influences health and whether various training and monitoring methods can improve the outcomes of the employees. As such, a design that compares the absence and presence of these methods to identify the relationships between them is the most suitable.
The research method is the specific paradigm used to govern the collection and analysis of data. Experimentation, descriptive statistics, correlation, and causal-comparative studies are the most prominent approaches. The first method is not viable because of the time required as well as ethical concerns regarding knowingly exposing workers to potential health hazards. Each of the other three is non-experimental and applicable to some degree, and it can be challenging to determine the best option. Patten and Newhart (2018) claim that causal-comparative studies, which describe a current condition and try to identify its past causes, can be applied to health issues successfully. As such, this approach is well-suited to the particulars of the issue and will be chosen for this study.
Data Collection Methods
The study needs to obtain information about PM size, employee health and training, and the number of sick days that they have used. Observation would be the best approach for the first data point, as it is unlikely that employees or the company have been noting down these statistics in the past. Employee health is a personal matter that they do not necessarily report to the company in detail. As such, self-reporting via surveys appears to be the optimal approach for evaluating health concerns and connecting them to various factors. Lastly, training sessions and health evaluations will likely be recorded by Sun Coast, and the human resource department should have information on the sick days taken by each employee. For these qualities, records analysis will be optimal because of the existence of accurate and processed information.
One of the research questions in this study is whether the PM size is related to health outcomes among the staff. It implies that workers in different positions or facilities are consistently exposed to particles of different diameters. As such, to answer the question, the researcher will have to compare the health outcomes of people in these varying circumstances. The sample will have to include a substantial number of people from each such group to guarantee accuracy and be segregated based on this principle. Elmoselhy (2016) describes this approach as quota sampling, a subset of purposive sampling, which is part of the nonprobability sampling design approach. The workers will have to be separated into other groups to answer other research questions.
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Data Analysis Procedures
RQ1 is concerned with the differences in the means of a variety of populations that internally share the same factor, and the ANOVA test is the most suitable as a result. RQ2 discusses safety training improvements, the results of which change independently based on the employee in question, and the reduction of lost-time hours. A correlation test for the two variables would be the most appropriate in this case. In RQ3, the same considerations apply, and the same variety of analysis should yield the best results. RQ4 has two dependent variables and one independent one, which makes a regression analysis the most appropriate.
The same method is suitable for RQ5, which features two independent variables and one dependent one. Lastly, in RQ6, several populations and their means are compared, which makes ANOVA the most appropriate to test its hypotheses.
Elmoselhy, S. A. M. (2016). Design for profitability: Guidelines to cost effectively manage the development process of complex products. Boca Raton, FL: CRC Press.
Emilien, G., Weitkunat, R., & Lüdicke, F. (eds.). (2017). Consumer perception of product risks and benefits. Cham, Switzerland: Springer.
Patten, M. L., & Newhart, M. (2018). Understanding research methods: An overview of the essentials (10th ed.). New York, NY: Routledge.