Simple random Sampling : My most preferred method of Sampling is Simple Random Sampling. This sampling technique determines the sample by randomly selecting a sample from a large population.

Simple random Sampling

My most preferred method of Sampling is Simple Random Sampling. This sampling technique determines the sample by randomly selecting a sample from a large population. Each individual is chosen randomly by chance from the population such that each individual has the same opportunity of being chosen (Sharma, 2017). A simple random sampling method is often an unbiased surveying technique.

I chose simple random sampling techniques over the other sampling techniques as it facilitates nursing research and is much efficient and reliable when it comes to dealing with large sample research populations. Moreover, it is advantageous as it is free of classification errors and requires less advanced knowledge of the population than the frame (Gray, Grove, & Sutherland, 2016). Data collected form this technique is simple to interpret and thus much suitable during situations where less information is available concerning the population of interest, and data collection can be randomly conducted at a low cost. It further offers accurate population representation and thus facilitating the smooth success of the research. Nursing research usually involves patients with specific diseases or certain nursing care conditions. This research’s generalizability is based on multiple factors, including the external and internal validity of research methods. From the research of a specific condition, the target population is often widespread, and universal access is usually hard; thus, the need for a sample population will describe the target population (Gray, Grove, & Sutherland, 2016). A simple random sampling technique ensures that the sample population has chosen to bear all the target sample variables and factors that are necessary for the research. This technique is cost not only efficient but also reliable in sample selection as it reduced errors that can result during population detection.

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