Introduction
In this research, the focus is on studying the relationship between following a specific diet by patients with congestive heart failure and changes in their readmission rates. In order to determine the relationship, it is necessary to apply an experimental research design and analyze data with the help of certain statistical tests. The data analysis plans for demographic and study variables should be discussed in detail.
Data Analysis Plan for Demographic Variables
The first step in the data analysis is the description of data that were collected for a certain study. In this research, demographic variables include age, gender, ethnicity, and BMI. It is also important to focus on the number and percentage of participants involved in two groups that were identified for this study: the patients who follow a diet strictly and the patients who follow a diet with exceptions. Descriptive statistical tests selected for analyzing demographic variables will be based on the univariate analysis, and they will include determining the mean, the standard deviation, and the frequency distribution (Simpson, 2015). Thus, it is important to ensure that the data collected for the study are normally distributed to avoid negative impacts on the findings (Parahoo, 2014). As a result of conducting these statistical tests, it will be possible to summarize the descriptive data regarding the participants of the study, their individual features, and differences associated with the independent variable in this research.
Data Analysis Plan for Study Variables
The plan for the data analysis of study variables is based on the necessity of responding to research questions and testing hypotheses. In order to measure data related to study variables, it is important to conduct both descriptive and inferential statistical tests (Parahoo, 2014). Descriptive data will be analyzed with the help of determining the mean, the standard deviation, and the frequency distribution for patterns of following the selected diet and numbers of readmissions in relation to each participant. These tests are significant to provide the background for the further examination of relationships between variables.
Inferential statistical tests will include a correlation test based on determining the Pearson’s correlation coefficient and an unpaired t-test. A correlation test is important in order to determine the degree of the relationship between keeping a diet for patients with congestive heart failure and readmission rates (Parahoo, 2014; Simpson, 2015). Correlation coefficients will be calculated for two groups of patients in order to demonstrate the degree of the connection between keeping a diet without exceptions and possible readmissions. An unpaired t-test will be used in order to compare the characteristics and relationships between variables for two groups of patients: those who follow the prescribed diet without exceptions and those patients who make some exceptions. It is necessary to pay attention to the fact that t-tests are usually applied in experimental designs in order to compare the data for different sample groups in order to conclude about the set research questions.
Conclusion
The proposed data analysis plans are important to be used in the study in order to analyze the collected data according to the purpose of the research. Standard descriptive tests for analyzing demographic data are selected to accentuate similarities and differences among participants, as well as tendencies in sample groups. Inferential statistical tests are selected to conduct main analyses for this study to identify the relationship between the determined variables.
References
Parahoo, K. (2014). Nursing research: Principles, process and issues (3rd ed.). New York, NY: Palgrave Macmillan.
Simpson, S. H. (2015). Creating a data analysis plan: What to consider when choosing statistics for a study. The Canadian Journal of Hospital Pharmacy, 68(4), 311-317.