The study under discussion is focused on the prevention of nausea and vomiting after chemotherapy. This theme is in need of a deeper research because nausea and vomiting present two very significant outcomes of chemotherapy that produce an adverse effect on the quality of life, as well as the ability to perform daily activities, of the patients undergoing chemotherapy (Tageja & Groninger, 2015). Treatment options for this outcome differ based on their kind and level of effectiveness (Boccia, 2016). As a result, finding the most effective option is important.
The research question of the study is the following: in Hispanic patients with gastric cancer, does the prevalence of chemotherapy-induced nausea and vomiting differ in the group treated with conventional medicine compared to the result of the group receiving herbal treatment and ginger?
In order to select the participants for the sample, it will be necessary to locate Hispanics patients with gastric cancer in one or several hospitals in one city or area. Since the participants targeted for this study will be selected based on a set of common characteristics such as their ethnicity and the type of cancer they suffer from, this sampling method will be non-probability purposive sampling (Gray, Grove, & Sutherland, 2013).
The study design is focused on the comparison of two groups of patients with the same characteristics suffering from the same issue but receiving different treatments. In that way, I would make sense to make sure that the patients selected for this research match the inclusion criteria fully.
This way, the chosen sampling method ensuring the homogeneity of the sample is suitable for the experimental design of this study and also reflects the population identified in the research question and problem statement. Further, the participants will be randomly assigned to the control and intervention groups; however, in case if some of the participants prefer one of the treatments to the other, the design of this study will be adjusted to quasi-experimental.
The proposed size of the sample will be about 30 to 50 people; this size was chosen because the aim of the study is to compare the prevalence of chemotherapy-induced nausea and vomiting in the two groups. As a method of data analysis, t-test used for the comparison of the ratio between the scores of variability and means in the two groups in order to determine whether or not the difference is dictated by the researched variable and not by bias or certain unrelated features of the sample (Warner, 2012). Additionally, t-test is usually employed for the smaller samples of about 15 to 30 subjects in one group (Warner, 2012). As a result, the sample size is appropriate.
The findings may be generalized to the populations characterized by the ethnic background other than Hispanic; but it may not be smart to generalize them to the patients with the types of cancer other than gastric due to different prevalence of nausea and vomiting.
Study design seems to flow from the proposed research problem, theoretical framework, literature review, and hypothesis. Practically, the hypothesis, as well as the information presented in the literature review and background determines the course of the current study by defining what needs to be evaluated, in what ways, and what kind of data is needed in order to shed light on the selected problem. In addition, study design is strongly impacted by the perspective from which the problem is studied and what the authors are attempting to find or evaluate.
References
Boccia, R. V. (2016). Chemotherapy-induced nausea and vomiting: Identifying and addressing unmet needs. JCOM, 20(8), 377-384.
Gray, J. R., Grove, S. K. & Sutherland, S. (2013). The practice of nursing research – e-book: Appraisal, synthesis, and generation of evidence. Amsterdam, The Netherlands: Elsevier Health Sciences.
Tageja, N., & Groninger, H. (2015). Chemotherapy-induced nausea and vomiting: An overview and comparison of three consensus guidelines. Postgraduate Medical Journal, 92, 34-40.
Warner, R. M. (2012). Applied statistics: From bivariate through multivariate techniques. New York, NY: SAGE.