Please reply to the post below using RISE positive feedback tool. Provide citations and references in APA format.

Exercise 1.

After studying the lecture materials and reviewing the additional resources, I decided to use the G Power software to complete the task of calculating the sample size based on the provided assumptions. For the test, I used a T-test, which is useful in determining the statistical difference between two groups (Sylvia & Terhaar, 2018). In this exercise, the groups were independent, consisting of a control and an experimental group. After selecting two-tailed T-tests and setting the effect size to 0.5 and the power to 0.80, I ensured the allocation ratio (N2/N1) was 1, and then I clicked "Calculate". The result obtained was a total of 128 for the sample size, with each group containing 64 samples each. Considering the possibility of attrition in a real-life study, it is suggested that about 71 sample size or above (this accounts for an additional 10% which gives approximately 7) can be considered a good sample size per group.

To support this sample size for the quality improvement project, a recent study by Martos-Cabrera et al. (2021) that explored the effect of education intervention on Type II diabetes patients used a sample size of 78 for the intervention group. Hence, the researchers considered the minimum number necessary to obtain a significant result and also gave room for attrition. Therefore, the sample size used in the methodology for the intervention group by the investigators in the report by Martos-Cabrera et al. (2021) agrees with the sample size obtained in the quality improvement project discussed in this exercise. Hence, the DNP nurse can conveniently use a sample size that is higher than 71 to determine changes in self-efficacy levels before and after the intervention.

Exercise 2.

Just like the first exercise, I also used G Power software for this second exercise. However, there were some changes made in calculating the sample size. This time, I ensured that the type of test selected was F-test, ANOVA fixed effects, omnibus, one-way. My rationale for selecting this type of test is based on the fact that the quality improvement project was aimed at determining whether there were differences between four options. In this case, ANOVA is the recommended test because it tests for differences of means between and within groups (Sylvia & Terhaar, 2018).

Furthermore, I had to first determine the effect size "f" by calculating the mean for each option from the data provided. The results were 6.6, 3, 3.4, and 1.2 for Option 1, Option 2, Option 3, and Option 4, respectively. Then, I calculated the effect size and transferred it to the main window. The value of "f" obtained was f=1.946. The error of probability was set as 0.50, and the power as 0.80 based on the assumptions. The number of groups was set as four, then I clicked "Calculate." The result obtained was a total sample size of 8, which gives a minimum of 2 per sample per group. As mentioned in one of the assumptions, "the groups are larger than two.". Hence, this sample size agrees with the assumption because it would be recommended that the DNP nurse add a few more participants to account for attrition. Consequently, a minimum ofthree samples is recommended for each group. This size is the minimum that can be used, and researchers could use higher sample sizes. For instance, in a recent study by Onyegasi et al. (2023), which explored a behavioral counseling program on obesity management, the researchers utilized a sample size of 36

References

Martos-Cabrera, M. B., Gmez-Urquiza, J. L., Caadas-Gonzlez, G., Romero-Bejar, J. L., Suleiman-Martos, N., Caadas-De la Fuente, G. A., & Albendn-Garca, L. (2021). Nursing-intense health education intervention for persons with type 2 diabetes: A quasi-experimental study. Healthcare, 9(7), 1-9. https://doi.org/10.3390/healthcare9070832Links to an external site.

Onyegasi, N., Harris, S., & Holmes, G. (2023). Enhancing obesity management through a nurse practitioner-guided behavioral counseling program. The Journal for Nurse Practitioners, 19(6), 104593. https://doi.org/10.1016/j.nurpra.2023.104593Links to an external site.

Sylvia, M. L., & Terhaar, M. F. (2018). Clinical analytics and data management for the DNP. Springer Publishing Company, LLC