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Data Analysis for Demographic Variables
Demographic variables for this project include age, ethnicity, gender, lifestyle, and socioeconomic status because professionals tend to reveal that heart failure affects people when they become older. With the help of analyzing demographic variables and presenting descriptive statistics, it will be possible not only to identify at what age people are usually diagnosed with congestive heart failure but also to reveal the age range. The number and percentage of participants affected by the intervention will be measured with the focus on the univariate analysis approach. Thus, the researcher will start with finding the mean. The mean should be revealed for age, gender, race, lifestyle, and socioeconomic status. The next step will be measuring the standard deviation. This information is vital because it provides an opportunity to describe both the sample and the whole population affected by heart failure. The information will be organized in tables and graphs.
Data Analysis for Study Variables
The proposed research is meant to reveal the relationship between nursing care in the form of providing education for patients and admission rates. Thus, the study variables include education for the most vulnerable categories of the population and admission rates among patients with congestive heart failure. Descriptive data in this data will be analyzed with the help of measuring the mean, the frequency distribution, and the standard deviation for methods of education and the associated numbers of admissions about the group of participants. While admission rates are easy to measure, education can be discussed in two ways. First of all, it is possible to focus on whether it was performed or not. In addition to that, there is a possibility to discuss the number of hours spent on education. Nevertheless, the first approach seems to be more appropriate for the proposed study and its research question.
Inferential statistical tests for this study will be the chi-square test and a correlation test that is based on determining the specific Pearson’s correlation coefficient (Alli & Bhaskar, 2016). In this research, correlation coefficients will be calculated for the groups of patients who receive different forms of education or do not receive education at all to demonstrate the degree of the connection between this intervention and possible admissions. Furthermore, the proposed chi-square test will be based on the comparison of two groups of patients: those who were educated and those who were not educated in the context of admission rates associated with heart failure. The degree of the relationships between the two variables in the study will be determined.
References
Alli, Z., & Bhaskar, B. (2016). Basic statistical tools in research and data analysis. Indian Journal of Anesthesia, 60(9), 662-669.
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