Sampling theory comprises one of the most effective theoretical frameworks deployed in generalizing population or study units. In the modern world, business organizations aim to examine the product quality to enhance customer safety and customer satisfaction. In such a situation, there are huge volumes of data that need to be analyzed. In such situations, the sample selected for the study must be representative of the entire population (Wu & Thompson, 2020). Selecting an adequate population helps to make the results of the study generalizable. The sampling theory offers an equal opportunity for the population to participate in a research project. Sampling theory offers an opportunity to study essential characteristics of a population at a minimized cost.
In the evaluation of product quality to enhance customer satisfaction, the quality of the sampling process can be enhanced through selecting a high and a reasonable sample size. There are several risks associated with selection of a small sample size such as limitation in generalization of the study findings and results (Hankin et al., 2019). The other approach that can be deployed in enhancing the sampling theory in the process is deploying random sampling. Random sampling will offer equal opportunities for people to take part in the research.
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Business organizations in the contemporary world are collecting big data to facilitate timely decision making. The collection and analysis of this data is a sensitive process that requires data experts to enhance data integrity and data transparency (Arnab, 2017). In the sampling theory, data integrity and confidentiality is another essential element that enhances the quality of study findings. The study participants have a right to privacy and confidentiality. Data collected in determining the levels of customer satisfaction must only be used for purposes of business research (Zayed, 2018). Business organizations must ensure that such data is kept safe from access by unauthorized people. During the process of sharing data in sampling theory, study participants should give consent before their data is shared.
References
Arnab, R. (2017). Survey sampling theory and applications . Academic Press.
Hankin, D., Mohr, M. S., & Newman, K. B. (2019). Sampling theory: For the ecological and natural resource sciences . Oxford University Press, USA.
Wu, C., & Thompson, M. E. (2020). Sampling Theory and Practice . Springer International Publishing.
Zayed, A. I. (2018). Advances in Shannon’s sampling theory . Routledge.
