Targeting one population group eliminates the possibility of including participants who are unsuitable for the research objective. This is done by making a frame for sampling and determining the appropriate sample size. In the previous discussion, we discussed the methods of determining the sample, selecting the population, and accessing them.
In this discussion, I will address the necessary sampling procedures and the implications of these procedures.
Sample Volume:
The sample size is the number of units in one sample and depends on several factors, such as the cost and time required. Still, despite that, the larger the sample size, the better and closer the results will be to the truth (Corona, Saez & Stoffel, 2014); this equation is applied to obtain the appropriate number for sampling.
n = p (100-p) z 2 / E
n: the desired sample size
P: is the percentage of incidence of the condition
E: the maximum percentage of error required
Z: the value corresponding to the desired confidence level
(Coyne, 1997)
Necessary procedures and their implications:
After determining the population and the sample frame and size, the sample and the validity of the data taken from it are evaluated; this includes the quality of the data and the possibility of using it in the research and drawing conclusions from it. It is also necessary to assess the response rate, which is the percentage of cases that can be included in the sample. This is after excluding cases of ineligibility, inability to respond, and cases that refused to answer from the outset; this leads to reaching the maximum benefit from the sample and ensuring the validity of its results (Ford, 2016).
The Sample and the Research Question:
The research question concerns reducing pollution in construction sites and its impact on the population and the environment. The sample can provide specific answers, such as the extent of damage to residents and workers within the construction site framework. Statistical inference can be followed by deriving the confidence level about the target population; if the confidence level is high, then the sample results are closer to health (Robinson, 2014).
Conclusion
According to (Jain, Gupta & Pandey, 2016), in the 1990s, during the real estate boom in India, the capital, New Delhi, was one of the country's most affected areas due to pollution from construction sites. Absence of warnings and instructions for machinery and equipment, mitigating rising dust, and ways to dispose of waste in the surrounding soil. All this made the areas adjacent to the construction areas a massive hotbed of pollution and the spread of associated diseases. Problems such as asthma and shortness of breath were apparent in all samples on which the researchers conducted their research.
References
Corona, C., Saez, J. L., & Stoffel, M. (2014). Defining optimal sample size, sampling design, and thresholds for hydrogeomorphic landslide reconstructions. Quaternary geochronology, 22, 72-84.
Coyne, I. T. (1997). Sampling in qualitative research. Purposeful and theoretical sampling; merging or clear boundaries?. Journal of Advanced Nursing, 26(3), 623-630.
Ford, J. B. (2016). Cost vs credibility: The student sample trap in business research. European Business Review, 28(6), 652-656. Retrieved from ProQuest Central.
Jain, G., Gupta, V., & Pandey, M. (2016). Case study of construction pollution impact on the environment. Int. J. of Emer. Tech. in Eng. Res.(IJETER), 4(6).
Robinson, O. C. (2014). Sampling in interview-based qualitative research: A theoretical and practical guide. Qualitative research in psychology, 11(1), 25-41.