Extraneous variables and ways to control them
In the context of this paper, it will be critical to describe the methodology to discover a correlation between the number of patients with congestive heart failure on a diet and their readmission rates. To establish a foundation for discussion, extraneous variables are different external factors that may affect the outcomes of the research (Grove, Gray, & Burns, 2014).
In the selected case, they may be related to the presence of different medical conditions and diseases apart from congestive heart failure among chosen participants, lifestyle, and the attitude towards medicine, and behavior of the researcher. These aspects may dramatically impact the number of readmitted patients and the outcomes of the study. To control them, their health status and lifestyle have to be assessed in detail, and only participants with similar characteristics have to be selected while the behavior of the researcher has to comply with the Code of Ethics and focus on avoiding bias.
Appropriate instruments to address the research question
To find the answers to the main research questions, it will be suitable to rely on descriptive statistics and t-test. Descriptive statistics will be used to calculate mean values of all required variables such as BMI, a degree of keeping a diet, a percentage of participants with congestive heart failure, readmission rates, and the health condition of the patients. After that, a correlation coefficient will find a relationship between the dependent and independent variables of each group, and the value close to one will imply a greater connection (Yokey, 2016).
In this instance, it will be linked to a relationship between BMI and health condition, and degree of keeping a diet and readmissions. Lastly, the t-test will compare the results of two sample groups (on a strict diet and the ones, who have exemptions) to understand the actual impact of dieting not only on readmissions but also on the health of the participants.
Description of the selected instruments, validity, and reliability estimates
All calculations can be presented with the help of SPSS that is the most appropriate statistical software used in the different spheres of research (Yokey, 2016).
Thus, to test validity and reliability, different estimates could be utilized. The information concerning the results of the research will be monitored in several phases:
- each group is evaluated before the start;
- two weeks from the start, and
- six weeks after the study.
This procedure will not only assist in discovering the progress of the research but also ensure that the proposed measures are valid and reliable, as they comply with test-retest and inter-rater reliability estimates and construct and content-related validation (Grove et al., 2014).
Intervention and data collection procedures
The chosen intervention is a diet that will imply healthy eating and following a particular schedule. This matter will be used to understand the correlation between a diet, readmission rates, and health of the patients. Simultaneously, selecting this intervention is rational since it will help determine whether a healthy lifestyle is a critical aspect that can reduce the levels of congestive heart failure.
In turn, to collect general information about participants, medical healthcare records will be used while it may be necessary to measure BMI to ensure the validity and reliability of the findings before experimenting, and similar measures will take in the middle (two weeks) and six weeks after the research. Data concerning dieting and its effect on different variables will be recorded when observing the behavior and medical conditions of the participants before, during, and after the study. In the end, the readmission rates will be gathered continuously, and the final measure will take place six weeks after the study while relying on medical records of the patients (their usage has to be agreed in advance).
References
Grove, S., Gray, J., & Burns, N. (2014). Understanding nursing research: Building an evidence-based practice. Amsterdam, Netherlands: Elsevier Health Sciences.
Yokey, R. (2016). SPSS demystified: A simple guide and reference. London, UK: Routledge.