Chemotherapy Study: Variables and Data Analysis

Research Paper: Data Analysis Plan

The proposed research has an experimental design. The research sample, consisting of fifty patients, will be divided into two groups: control and intervention. The inclusion criteria for the sample will incorporate participants’ ethnic background (only patients from the Hispanic population will be included as per the requirements of the research question) and their current treatment of chemotherapy (FOLFOX) for gastric cancer. The criterion for exclusion is the termination of chemotherapy for any reason.

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For the results of the study to be accurate, it is essential to identify the demographic independent variables to be used. These can be defined as the characteristics of patients that are required to describe the population group and determine whether it can be called representative of the particular sample (Houser, 2016).

The demographic variables that are to be collected and described include the following:

  • Ethnicity: The study is devoted to the problems and conditions experienced exclusively by Hispanic patients, which means that no participants of other backgrounds can be included in either of the research groups.
  • Age: It is important to find out how a patient’s ability to cope with post-treatment nausea and vomiting is determined by his or her age.
  • Sex: The research will investigate whether male and female patients exhibit a different reaction to the intervention.
  • Weight: The connection between weight status (e.g. obese and underweight) will be traced (if there is any).
  • Access to health care: Since the study focuses on a minority group, some of the participants are likely to have limited access to health care, which may have prevented them from receiving regular treatment for nausea.

To evaluate the chosen variables more demonstrative, the researcher will retrieve data collected by other researchers (including data regarding other population groups) and compare them to determine whether their results are significantly different than those of the population for this study. This analysis will be done via a chi-square test. This descriptive test was chosen because it demonstrates relationships between categorical variables that are not related to one another (Parahoo, 2014). Crosstabulation (or a bivariate table) will be used to present the distributions of variables for the current study and compare them to those obtained by other researchers.

Outcome variables are dependent variables that appear as a result of the intervention and are determined by the changes made to independent variables. They show the cause-effect relationship between the experiment and the states of the participants and help researchers make conclusions about the effectiveness of the measures (Grove, Burns, & Gray, 2014).

A t-test was chosen for data analysis because it suits the study’s small sample size and allows for the identification of differences between the two groups. Furthermore, it is typical to use this test when variances of the two normal distributions are unknown. The measure of central tendency for the t-test is the mean or the arithmetic average, a sum of the value of each observation in the sample divided by the number of these observations. This inferential test is an example of a statistical test that involves repeated measures, which implies that the participants of the study are to be tested more than once (Houser, 2016). Indeed, each participant will be measured on several occasions on the same dependent variable: the appearance and intensity of nausea and vomiting after either conventional or herbal treatments. This strategy is the most suitable for the present study as it will allow for an estimation of the effect of each intervention in its relation to the selected demographic variables coupled with the type of treatment.

The results obtained during the statistical analysis will be compared with the patient-reported results collected through the FACT questionnaire.

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References

Grove, S. K., Burns, N., & Gray, J. (2014). Understanding nursing research: Building an evidence-based practice. Amsterdam, Netherlands: Elsevier Health Sciences.

Houser, J. (2016). Nursing research: Reading, using and creating evidence. Burlington, MA: Jones & Bartlett Learning.

Parahoo, K. (2014). Nursing research: Principles, process and issues. Basingstoke, UK: Palgrave Macmillan.

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