Sampling theory is regarded as a branch of statistics dealing with the collection, analysis, as well as interpretation of the gathered data from random samples of the underlying phenomena being researched such as population (Royall, 2014). Sampling theory is being applied for example, during the appropriate selection of observations from the sample population, which represents the random sample.
Besides, sampling theory is as well involved with the utilization of probability theory in conjunction with earlier knowledge concerning the population parameters. As a result, this helps in analyzing the data obtained through random sampling in addition to developing conclusions from this analysis. Also, the sampling theory’s theoretical background is developed by heavily using the related probability distributions along with the standard distribution (Royall, 2014). Most importantly, sampling theory in nursing is a vital approach that allows the practitioners of medical research in drawing meaningful assumptions from their analysis of data, for example, converting medical issue into a statistical supposition.
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As it applies to nursing research, generalizability implies merely that the findings of a nursing study are applicable to more than just the sample being researched since such a sample represents the entire target population. As a result of the crucial role played by generalizability, some risks are too narrowly involved in defining the accessible population under research (Huang, Beck, Kind, McLauchlan, Gigante & Smith, 2017). For instance, the available population’s narrow definition can lower the capability of generalizing from the sample of study to the target population, and ultimately reduces the findings’ meaningfulness.
Finally, biases can be introduced in the research thus making it difficult in defending the generalizability to the population being targeted. For example, Huang, Beck, Kind, McLauchlan, Gigante and Smith (2017) posit that if the population under study is categorized as people in upper-middle-class, white setting, then it becomes impossible in generalizing to lower income or nonwhite populations.
References
Huang Schiller, J., Beck Dallaghan, G. L., Kind, T., McLauchlan, H., Gigante, J., & Smith, S. (2017). Characteristics of multi-institutional health sciences education research: a systematic review. Journal Of The Medical Library Association, 105(4), 328-335. doi:10.5195/jmla.2017.134
Royall, R. (2014). AN OLD APPROACH TO FINITE POPULATION SAMPLING THEORY. Journal Of The American Statistical Association, 63(324), 1269.
