Summary
The presented proposal includes an approximate plan for data analysis in the research devoted to the Cocreation of Possibilities: a new intervention that can be used by nurse practitioners (NPs) in palliative care and has been defined by Bergdahl, Benzein, Ternestedt, Elmberger, and Andershed (2013).
Data Analysis Plan: Demographic Variables
As it was mentioned before, the specific data that will be gathered from the NPs and especially patients may change, but some demographic variables are sure to be used. For NPs, the information concerning their age, sex, years of experience, and education will be collected since they may influence the outcomes. The patients’ demographic variables will mostly include their age, gender, and marital status along with the family size. The socioeconomic variables are not supposed to influence the outcome, and they will not be included to avoid testing too many aspects. The data will be coded to make its processing easier and summarized with the help of the frequency tables. According to Wetcher-Hendricks (2011), this approach is probably the only one that is properly-suited for the analysis of such information (p. 50). Specific software (SPSS Statistics) will be used to facilitate the process. The data will also be depicted with the help of graphs (most definite histograms), which ensures improved display (Wetcher-Hendricks, 2011).
Data Analysis Plan: Study Variables
The study variables include the patients’ quality of life defined by the WHO (2016) preexisting tool and the relationships with the NPs defined with the help of the instrument created by Cossette (2006); also, the diagnosis and the disease-specific aspects of the quality of life are likely to need to be measured. Since the latter is not defined yet and depends on the final sample, there can be variations.
Both tools provide ordinal data that will be descriptively analyzed for every patient, NP, and group, thus demonstrating the changes in the states of the former and the performance of the latter. As a result, the information will be presented as ratio data, which indicates that if the rest of the conditions for a parametric test (PT) are met (in particular, the normal distribution), a PT will be chosen. However, the requirements for PTs are not too likely to occur, which is why we expect and plan to use a non-parametric test (NPT) as it is usually done if a PT is not an option (Rubin & Bellamy, 2012, p. 157). The reporting measure of central tendency depends on the final data, but it is unlikely that the mean will be used since normal distribution is not expected.
NPTs can be applied to a broader scope of conditions and, even though the results of PTs have a lower probability of error, NPTs are also very reliable and often considered equivalent to PTs (Anastas, 2012). For this plan, the Mann–Whitney U test is suggested since it is meant to deal with the data that we are going to have and is suited for the aim of testing the null and research hypothesis. In other words, the instrument is meant for the comparison of two groups, which is why it can be used to find out if there are differences in the performance of the experimental and control groups. Apart from that, it is an NPT, which is why it can be used for any type of data and skew distribution (Rubin & Bellamy, 2012, p. 172).
References
Anastas, J. (2012). The research design for social work and human services. New York: Columbia University Press.
Bergdahl, E., Benzein, E., Ternestedt, B., Elmberger, E., & Andershed, B. (2013). Co-creating possibilities for patients in palliative care to reach vital goals: A multiple case study of home-care nursing encounters. Nursing Inquiry, 20(4), 341-351. Web.
Cossette, S. (2006). CNPI User Guide and Related Articles. Web.
Rubin, A., & Bellamy, J. (2012). Practitioner’s guide to using research for evidence-based practice. Hoboken, N.J.: Wiley.
Wetcher-Hendricks, D. (2011). Analyzing quantitative data. New York, BY: John Wiley & Sons.
WHO. (2016). The World Health Organization Quality of Life (WHOQOL). Web.