A comparative statistical test should be used in combination with the chosen research to evaluate the findings of an evidence-based project proposal. These tests appear to be the most appropriate settings for evaluating nursing staffing policies since they analyze comparisons and differences across numerous variables (Siedlecki & Bena, 2021). In this case, a paired sample t-test is recommended for comparing and evaluating data on distinct variables.
On a continuous dependent variable, the paired sample t-test compares two sets of data from the same group of people (Gerald, 2018). By clearly integrating caregiver and support specialist duties to the nursing workforce, the study advocated actions connected to hospital nurse staffing patterns to enhance patient outcomes. Because the paired-sample t-test compares pre-and post-intervention phases, it may be used to analyze and change nurse staffing patterns (Siedlecki & Bena, 2021).
The paired comparison approach is a useful decision-making tool since it specifies values and compares them to one another. When you have a lot of choices, it might be tough to pick the ideal one. All possible solutions are graphically examined, resulting in an overview that indicates the proper solution right away (Gerald, 2018). This makes comparing the relative value of competing criteria straightforward if no objective data is available to make a conclusion (Gerald, 2018). When prospective choices compete with one another, it also benefits since the most efficient solution will win out in the end.
In the research setting, the comparison test can identify the most vital variables to evaluate the effectiveness of the intervention. For example, cost-effectiveness, the satisfaction level of patients, improvements in patients’ length of stay, and nurses’ workload levels. The paired sample t-test thus may be used to examine and adjust the structure of the nursing staffing policies according to the results. Outcomes of these adjustments will lead to the proclaimed goal – the increase in patients’ length of stay.
Siedlecki, S. L., & Bena, J. F. (2021). What you need to know about running and interpreting the t-test: A guide for the clinical nurse specialist. Clinical Nurse Specialist, 35(2), 56-61.
Gerald, B. (2018). A brief review of independent, dependent, and one-sample t-test. International Journal of Applied Mathematics and Theoretical Physics, 4(2), 50-54.