n response to research study
Some questions were presented related to the research study…Please respond to the questions below
1. What will be the benchmarks for inclusion in the research study?
2. What are the reasons and significance for selecting this topic?
3. When considering your topic would you have inclusion criteria other than positive for COVID-19? Or would you use the stratified sampling technique to organize by age, weight, ethnicity, comorbidities, etc.?
Introduction
For my scientific research, the topic that I selected is “The effects of proning therapy in COVID 19 patients”. The PICOT question that will be utilized in the study is “For COVID-19 positive patients, has the use of proning therapy been effective in reducing mortality and intubation rates?” From the study’s PICOT question, the Population is Covid- 19 positive patients with the ICU, Intervention is proning therapy, Comparison is supine position, Outcome is reduced intubation and mortality of COVID-19 patients, and Time is during hospital admittance. According to the PICOT question, this study seeks to discover whether proning therapy as a COVID-19 treatment strategy will minimize intubation and mortality rates. This study will be quantitative research since these rates can be examined only through numerical data. This essay will highlight the sampling method I would use to conduct this study and how the sample type and size will be determined.
The Sampling Methods that Would be Used in the Study
The first sampling method utilized in my research will be simple random sampling. Frost (2022) stated that simple random sampling entails giving every person in the population an equivalent probability and opportunity of being chosen to participate. Therefore, I would use this sampling method to recruit patients from the desired number of ICUs in the country, particularly from those medical institutions that have used prone therapy in treating patients for more than five years. This method would be ideal because it allows a researcher to calculate the sampling error and minimize selection bias. Besides, simple random sampling is an effective sampling method in research because it is straightforward. In this context, since I will be selecting COVID-19 patients admitted to the ICU, simple random sampling will help me choose the desired number of participants to take part in prone therapy.
Additionally, I would utilize the stratified sampling method in this research. Essentially, in stratified sampling, the population is categorized into strata (subgroups) that share the same characteristic (Parsons, 2014). Moreover, this sampling method is used in studies where the researchers anticipate a variation of the measurement of interest between different strata. This ensures that there is representation from every subgroup. Therefore, I would employ the same sampling method in my research in the randomization and stratification of patients according to the ICU. This will allow me to assign patients randomly to a supine group or a prone group.
The main advantage of stratified sampling is it allows a researcher to select non-equal sample sizes from every subgroup. For instance, in medical research, to study the health outcomes of the hospital staff in a country, if there are four hospitals with different numbers of hospital staff, it would be advisable to select sample sizes from every hospital proportionally. If Hospital 1 has 100 staff members, Hospital 2 has 200, Hospital 3 has 300, and Hospital 4 has 400, the appropriate sample selected from every hospital is 10, 20, 30, and 40, respectively. This ensures a more accurate and realistic approximation of the health outcomes of medical staff across the country. Unlike simple random sampling, which would most likely lead to the over-representation of medical staff from every hospital, stratified sampling would ensure that the sample selected from each hospital to participate in the study is more proportionate. Therefore, since I will be choosing COVID-19 patients from different hospitals in the country, I would use stratified sampling to select the appropriate proportion of patients to be subjected to prone therapy. This will ensure that the study’s representativeness and accuracy are improved and free front bias.
The Methods that Will be Used to Determine the Sample Type and Size
A researcher must ensure that the sample size is sufficient enough to provide accurate results in every study. Therefore, I would use Andrew Fisher’s Formula to determine which sample size would provide accurate data and ultimately lead to correct conclusions. According to Kibuacha (2021), Andrew Fisher’s Formula is given by Sample Size = {Z2 * S.D * (1- S.D)}/ (C.I)2. If I decided to work with a 90 percent confidence level, a confidence interval of ± 5%, and a standard deviation of 0.5, the appropriate sample size for my study would be 272.25 {((1.96)2 x .5(.5)) / (.05)2}. Therefore, after determining the sample size that I should work with, I would apply the stratified sampling techniques to select the type of COVID-19 patients that I would subject to prone therapy to study if intubation or mortality rate would be reduced. More specifically, if I were selecting patients from two ICUs that have 1000 and 2000 COVID-19 patients, respectively, I would use stratified sampling to select non-equal sample sizes from every subgroup. The sample from ICU A would be 100, while the sample from ICU B would be 200, totalling 300. Even though this sample size is slightly larger than the calculated sample size, it will be sufficient to make accurate conclusions.
References
Frost, J. (2022). Simple Random Sampling: Definition & Examples. Statistics By Jim https://statisticsbyjim.com/basics/simple-random-sampling/ (Links to an external site.)
Kibuacha, F. (2021). How to Determine Sample Size for a Research Study. Geo Poll. htps://www.geopoll.com/blog/sample-size-research/ (Links to an external site.)
Parsons, V. L. (2014). Stratified sampling. Wiley StatsRef: Statistics Reference Online, 1-11.
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