**Research question: In patients with depression (P) does a depression screening on the initial visit (I) compared to only one done by professionals after six weeks (C) reduce the risk of depression (O) in a year (T)?
Does caffeine cause cancer? Is autism caused by childhood immunizations? What is the relationship between eating sugary sweets at night and weight gain? It is often helpful to determine and explore relationships between variables. This is especially important in health care, a field dedicated to providing quality care for patients and improving health outcomes. Examining relationships between variables forms the basis for correlational statistics.
In this Discussion, you identify a health care or nursing practice problem that can be explored with correlational statistics and formulate a research question for exploring that problem. You also develop a null and alternate hypothesis, determine the variables related to the study, and predict relationships between the variables based on what you know of correlational statistics.
• Review this week’s Learning Resources and the “Correlation” tutorial focusing on the types of research questions that can be answered using a correlational statistic.
• Brainstorm a number of health care delivery or nursing practice problems that could be explored using correlational statistics. Then, select one problem on which to focus for this Discussion.
• Formulate a research question (see ** above) to address the problem and that would lead you to employ correlational statistics.
• Develop a null hypothesis and alternate hypotheses.
• Ask yourself: What is the expected direction of the relationship?
Post a cohesive response that addresses the following:
• Identify your selected problem in the first line of your posting and post your research question.
• Post a null hypothesis and alternate hypotheses for your research question and identify the dependent and independent variables that would be associated with the research study.
• Provide your prediction for the expected relationship (positive or negative) between the variables. Why do you think that sort of relationship will exist? What other factors might affect the outcome? Correlations of Nursing research Essay
Gray, J.R., Grove, S.K., & Sutherland, S. (2017). Burns and Grove’s the practice of nursing research: Appraisal, synthesis, and generation of evidence (8th ed.). St. Louis, MO: Saunders Elsevier.
• Chapter 23, “Using Statistics to Examine Relationships”
Chapter 23 explains how to use statistics to examine relationships between groups using correlational analyses, scatter diagrams, Spearman rank-order correlation coefficient, and Kendall’s tau.
Statistics and Data Analysis for Nursing Research
• Chapter 4, “Bivariate Description: Crosstabulation, Risk Indexes, and Correlation” (pp. 59–61 and 68–78)
BUY A PLAGIARISM-FREE PAPER HERE
This chapter describes components of bivariate descriptive statistics, including crosstabulation, risk indexes, and correlation. The chapter also discusses the concepts of absolute risk, relative risk, odds ratio, and correlation matrices.
• Chapter 9, “Correlation and Simple Regression” (pp. 197–209)
This portion of Chapter 9 continues the discussion of inferential statistics and explores correlation and simple linear regression.
Nursing research like other scientific research is concerned with the investigation into the presence or otherwise of a relationship between two variables. These are the dependent and the independent variables. But nursing research is first and foremost motivated by the presence of a clinical practice problem that needs to be solved. To guide this solution in a scholarly fashion, nursing researchers formulate the research question or statement which can take the form of a PICOT question or statement as happens with clinical inquiry (Melnyk & Fineout-Overholt, 2019). Also, the nurse researchers may come up with a null hypothesis (H0) and an alternative hypothesis (H1). These are educated guesses as to the possible direction that the relationship between the two variables will take after the study is completed and data analyzed (Gray et al., 2017). The purpose of this paper is to look for correlations between screening and depression by coming up with a null and an alternative hypothesis and then predicting the direction of the expected relationship.
Identification of the Clinical Practice Problem
Depression is one of the mental health conditions that carry an increased risk of mortality due to suicidality (APA, 2013). According to the diagnostic criteria of major depressive disorder (MDD) in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), the patient with depression feels worthless and not worthy of continuing to live, suffers from guilt that is inappropriate, and has relentless anxiety that they cannot wish away (AP, 2013). Despite its severity, however, depression can be successfully screened for and the symptoms averted when treatment is started early. This is best practice and prevents morbidity and mortality from depression. Some of the screening tests for depression are the Beck Depression Inventory or BDI-II and the Patient Health Questionnaire or PHQ-9 (Willacy, 2019). The problem is that among patients with depression, the timing of the screening test by mental health professionals before overt symptoms arise is not certain. This makes reduction of the risk of depression difficult due to lack of consensus and consistency. The research question in PICOT format in this case is: In patients who end up being diagnosed with depression (P), does screening on the initial visit (I) compared to only one screening after six weeks of follow up (C) result in risk reduction for the development of depression (O) within a period of one year (T)? Correlations of Nursing research Essay
The Variables as Well as the Null and Alternative Hypotheses
From the research question above, two opposing hypotheses can be formulated. These are the null hypothesis (no relationship) and the alternative hypothesis (positive relationship). In this case, the two hypotheses would be as follows:
- Null hypothesis (H0): There is no relationship between the timing of depression screening and the development of depression.
- Alternative hypothesis (H1): There is a positive correlation between the timing of depression screening and the development of depression.
From the above hypotheses, the independent variable is depression screening while the dependent variable is the occurrence of depression.
Prediction for the Expected Relationship
My prediction for the expected relationship is that there will be a positive relationship between the timing of the screening for depression and the development of symptoms of the same. I am convinced that sort of relationship will exist because preventive healthcare principles dictate that early screening for any condition carries a high chance of preventing the occurrence of that condition. This is because evidence-based interventions can be started early before the symptom show and hence catch the condition before it establishes itself. The other factors that may affect the outcome are referred to as confounding variables in research and in this case they include the age of the patient, social support system, and educational achievement amongst others.
American Psychiatric Association [APA] (2013). Diagnostic and Statistical Manual of Mental Disorders (DSM-5), 5th ed. Author.
Gray, J.R., Grove, S.K., & Sutherland, S. (2017). Burns and Grove’s the practice of nursing research: Appraisal, synthesis, and generation of evidence, 8th ed. Saunders Elsevier
Melnyk, B.M., & Fineout-Overholt, E. (2019). Evidence-based practice in nursing & healthcare: A guide to best practice, 4th ed. Wolters Kluwer.
Willacy, H. (November 13, 2019). Screening for depression in primary care. https://patient.info/doctor/screening-for-depression-in-primary-care Correlations of Nursing research Essay