Respond Following Sampling Question & Answer Guide (With Explanation)

This question tests key academic concepts commonly covered in coursework.

What This Question Is About

This question relates to respond following sampling and requires a structured academic response.

How to Approach This Question

Start by identifying the main issue, then apply relevant academic frameworks.

Key Explanation

This topic involves respond following sampling. A strong answer should include explanation, application, and examples.

Original Question

Respond to the following: Sampling theory is the field of statistics that is involved with the collection, analysis and interpretation of data gathered from random samples of a population. Therefore, Sampling is the process of selecting a proportion of a population to be tested or studied. The application of sampling theory is concerned not only with the proper selection of observations from the population that will constitute the random sample; it also involves the use of probability theory, along with prior knowledge about the population parameters, to analyze the data from the random sample and develop conclusions from the analysis. The normal distribution, along with related probability distributions, is most heavily utilized in developing the theoretical background for sampling theory. This is to imply that, In theory, the sample selected should best represent the whole population so that any results from the sample can be applied to the entire population (Grove et al., 2015). Sampling theory was developed to be able to mathematically determine the most effective way to acquire a sample that would accurately reflect the population being studied (Grove, et al., 2015). The key elements of sampling theory are the populations, the elements of said population that the researcher wants to study, the criteria for selecting the sample, how much the sample is representative of the population being studied, any sampling errors or differences between the sample statistics and the population parameters, randomization, sampling frame or list of all those within a population who will be sampled, and plans for making the sample selections (Grove et al., 2015). If I wanted to study how many female nurses in the past year have been hospitalized with appendicitis, I could use sampling theory to best come up with a sample to study. The population would be nurses with an active female nursing license of 2 or more years who have been hospitalized in an acute hospital setting with a diagnosis of appendicitis. The elements of the population would be nurses with a documented diagnosis of appendicitis in my state who have been hospitalized. I would have to make a list of all the registered female nurses in the central area of my state. Due to HIPAA regulations, I would have to survey the female nurses in my sample either by phone or e-mail as opposed to looking at a national database. I would then have to come up with a sampling method for selecting which nurses I would survey that would be as random as possible like stratified sampling or clustered sampling. Generalizability is a phenomenon in quantitative research where a finding or findings in a small sample can then be generalized to a larger population (Leung, 2015). If I were to study different sub-types of nursing and discover the same cause-and-effect relationship of poor nurse working environments and an increased incidence of gallstones, then my findings could be applied to nursing in general. References Leung, L. (2015). Validity, reliability, and generalizability in qualitative research. Journal of Family Medicine and Primary Care , 4 (3), 324-327. http://doi.org/10.4103/2249-4863.161306 Grove, S. K., Gray, J. R. & Burns, N. (2015). Understanding Nursing Research: Building an evidence-based practice. St. Louis, MS: Saunders/Elsevier

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