Academy for Practical Nursing Formulating Hypotheses Peer Response

Academy for Practical Nursing Formulating Hypotheses Peer Response

1——–The relationship between independent and dependent variables is the basis for formulating hypotheses for correlational, quasi-experimental, and experimental studies” (Grove, Gray, & Burns, 2015, p. 153). Independent variables can be changed (controlled, manipulated) by the person doing the research to see what effect it has on the dependent variable (Grove et al., 2015). The dependent variable is measured to see what the results or the outcome is for the study. In a study on SBIRT (screening, brief intervention, and referral to treatment), the dependent variable would be substance use (alcohol or drug use), and the independent variables would include screening, brief intervention, and referral to treatment. “The independent variable must be clearly defined, often by a protocol, so that it can be implemented precisely and consistently as an intervention in a study”, according to Grove et al. (2015, p. 152). Unfortunately, extraneous variables can affect the measurement of this type of study because there are barriers to providing SBIRT, for example, inaccurate screening techniques, missed opportunities to provide a brief intervention or referral to treatment, and patients may or may not want to quit using drugs or alcohol. “Extraneous variables exist in all studies and can affect the measurement of study variables and the relationships among these variables” (Grove et al., 2015, p. 154).

In addition, there is also environmental variables, which includes the environment where the study takes place (Grove et al., 2015). With SBIRT, this could include not having a place to put a counselor in the department, so SBIRT may not be performed. In some studies, descriptive or correlational studies (qualitative and a few kinds of quantitative studies), the researcher may want to see how the study plays out without controlling the extraneous variables. “If a researcher is studying humans in an uncontrolled or natural setting, it is impossible and undesirable to control all the extraneous variables” (Grove et al., 2015, p. 154). The researcher can control some of these variables by educating staff on correct screening techniques or using iPads to screen patients, can make sure someone is assigned to meet with each patient who screens positive for SBIRT to provide an intervention, and to make sure referrals are given at the time of the visit. However, the researcher cannot make the patient want help. “If change is implemented, there is an ethical and moral responsibility of the health care provider to evaluate the quality of patient outcomes derived from the change” (McLaughlin, & Sanchez, 2017, p. 101). Accuracy and validity of data is important, so that changes are made based on good, solid evidence. It is important for the change agent to use variables that will produce an answer for the question (PICOT) in his or her study.

2—-Lets briefly review the difference between independent and dependent variables. An independent variable represents a number that can be changed in an experiment. Alternately, a dependent variable reflects a quantity that depends on how the independent variable is influenced. Khan Academy (2019) offers the following example to give further definition to the two examples “you are doing chores to earn your allowance. For each chore you do, you earn $3”. In this example, the independent variable would be the number of chores you do and the dependent variable would be the amount of money that is earned.

My capstone project is related to how education can reduce 30-day readmission rates related to heart failure. In this topic, the independent variable would be the education of heart failure and the dependent variable would be 30-day readmission rates. This is related to the topic of education being influenceable, where the 30-day readmission rate is directly dependent on the independent variable of education. It’s important to appreciate the values of dependent and independent variables in order to ascertain the outcome of our projects. To be effective predictors, independent variables need to have a strong correlation with the dependent variable (Grove & Cipher, 2017). With this in mind, it’s important to appreciate how the independent and dependent variables come together to form a cohesive research finding. Why this is so important is to relate conclusive findings to our research to promote validation. Without the data to back up our findings, the research that we have done doesn’t hold the same scientific weight and thus doesn’t promote a necessity for change.

3—The two main variables in an experiment are the independent and dependent variable. An independent variable is a variable that is changed or controlled in a scientific experiment to test the effects on the dependent variable. A dependent variable is a variable being tested and measured in a scientific experiment. To transform single-input affine systems into linear control systems, one suggests using control-dependent changes of the independent variable. It shows that the use of such changes of variables in conjunction with feedback linearization enables one to linearize systems to which known linearization methods do not apply. The reason for collecting this variable, it is proven that a linearizing change of independent variable can be found by solving a system of partial differential equations. The approach developed in the paper is applied to the construction of solutions of terminal problems Fetisov, (2017).

4– Statistical significance testing and clinical trials” by one author provides a thought-provoking and critical discussion of the conventional analytical testing in clinical research. The author argues that, by focusing exclusively on mean differences between groups and their statistical significance, relevant information about the individual participant is ignore. The writer also, calls for a different methodology that examines client covariates with the outcome and then compares the treatment outcome distributions and their overlaps for each of the covariate-defined subgroups. The problem is well described, and the possible solutions articulated well. However, the problem the author is tackling stays at the initial stage of a multilevel problem Hofmann, S. G. (2011).

One of the central issues argument relates to client characteristics. Clinical researchers typically deal with populations that are defined by a medical classification system that categorizes people, with a different history, course of illness, and etiology, as well as cultural and social feature, into a diagnostic group that is defined based on more-or-less arbitrary symptom patterns. I will use clinical significance to support positive outcomes in my project by collecting data from reputable databases. I will also make sure the evidence-based practices are in collaborate throughout my project. Even the complex issues that future generations of clinical researchers will have to tackle, and that will likely take away the best results.

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