The common most research design is sampling. This is because researchers often are unable to conduct straight observations of each and every individual or component of the population involved in the research. Therefore, most research involves collecting data from a portion of the population; a sample, then observe the portion and draw an inference of the larger population based on those observations. Chances are that the sample will correspond to the rest of the individuals or components in the population on the particular features which the researcher is interested in (Pals et al., 2015). Thus, it is most likely that the conclusions that will be drawn by observing the sample will be a representation of the larger population. This paper analyzes sampling as a common research design and in particular, convenience sampling.
Whilst there are two sampling approaches; probability sampling and convenience sampling, the latter is the most common research method. The researcher collects data from a sample based on availability, or records available within the area of interest (Pals et al., 2015). The particular sample is conducted and those who are available are observed. Another reason why convenience sampling is preferred is when the researcher is interested in describing a certain group in an explanatory manner, for example, to understand patients with Alzheimer Diseases (Velayutham, Chandra, Bharath, & Shankar, 2017). The implication of using a convenience sample is that an unknown sample of the vast population is exempted from the research, for example, if the research is about technology, how about those hospitals which have not embraced technology. Therefore, the degree to which a convenience sample signifies of the vast populace cannot be determined empirically irrespective of the magnitude of the sample which has been considered. However, it equally gives quantitative and qualitative results of the research of interest.