Benefits of Using a Formula to Get a Sample Size

A sample size determination formula

A sample size determination formula is one of the most important factors in ensuring the statistical validity of studies (Charan & Biswas, 2013). The viewpoint stems from the fact that most research are intended to aid in decision-making on numerous issues confronting humanity. Regardless of the connection to real-life concerns, engaging the entire population is wasteful due to the time, resources, and analytic effort required to handle the group. The problem is known as sampling, and a representative number can only be obtained accurately using statistical techniques and calculations. While the issue of a sufficient number of participants has been an overlooked topic in research, its criticality is highlighted by a growing body of evidence, which is challenging the current stock of literature. An underlying supposition is that most of the studies have been utilizing a minuscule sample size, and are thus less powerful in promoting evidence-based practices (Jaykaran, Saxena, Yadav, & Kantharia, 2011).

Different designs call for different approaches

While sample size determination formula is a fundamental aspect of all studies, different designs call for different approaches. The choice is determined by various factors, with the expansiveness of the study problem being the primary determinant. For instance, if the issue being researched has affected only a small subset of the population, the sample size is big, and vice versa. A single blanket formula cannot also be employed in all studies, as different research approaches because every scientific inquiry serves a specific role. For instance, Fishers’ et al. (1998) method has emerged as an acceptable technique for determining the sizes of sample in cross-sectional descriptive studies, where the focus is to understand the prevalence. However, the formula cannot be employed in clinical trials and interventional studies where the focus is assessing efficaciousness. Instead, the sufficiency of participants in the experimental group, as well as control, is determined using sophisticated techniques, which entails employing statistical software such as G power (Charan & Biswas, 2013).

References

Charan, J. & Biswas, T. (2013). How to calculate sample size for different study designs in medical research?. Indian Journal Of Psychological Medicine, 35(2), 121. http://dx.doi.org/10.4103/0253-7176.116232

Jaykaran, Saxena, D., Yadav, P., & Kantharia, N. (2011). Negative studies published in medical journals of India do not give sufficient information regarding power/sample size calculation and confidence interval. Journal Of Postgraduate Medicine, 57(2), 176. http://dx.doi.org/10.4103/0022-3859.81861