Doctor Of Nursing Program Analyzing Parametric Statistics

Doctor Of Nursing Programanalyzing Parametric Statisticsyou Locate A Q

Doctor of Nursing program Analyzing Parametric Statistics You locate a quasi-experimental research study as possible evidence to support a practice change. You notice that the study aims to make a prediction that relates to correlation between study variables. The study sample size is large and normally distributed. Reflect upon this scenario to address the following. · In your appraisal of the evidence, you note that an independent variable is not present and that a Spearman's ranked correlation is used to analyze data. Is this the correct level of correlational analysis? Explain your rationale. · Are association and correlational analysis equivalent in determining relationships between variables? · Do these findings impact your decision about whether to use this evidence to inform practice change? Why or why not? Reading: Polit, D. F., & Beck, C. T. (Eds.). (2017). Nursing research: Generating and assessing evidence for nursing practice (10th ed.). Wolters Kluwer. · Chapter 16: Descriptive Statistics · Chapter 17: Inferential Statistics · Chapter 18: Multivariate Statistics · Chapter 19: Processes of Quantitative Data Analysis · Chapter 20: Clinical Significance and Interpretation of Quantitative Result 1 page-no spacing, Times new Roman. APA format At least 2 references -must be within 5 yrs. Absolutely plagiarism free.

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In evaluating the appropriateness of using Spearman's rank correlation in a quasi-experimental study with a large, normally distributed sample, it is essential to understand the nature of the statistical methods and the data characteristics. Spearman's correlation is a non-parametric measure that assesses the strength and direction of association between two ranked variables. Its use is typically justified when the data do not meet the assumptions of parametric tests, such as normality or linear relationship. Since the scenario indicates that the sample is large and normally distributed, Pearson's correlation coefficient might have been more appropriate because it assesses linear relationships between continuous variables that meet parametric assumptions. However, the absence of an independent variable and the use of Spearman's correlation can still be justified if the data are ordinal or not normally distributed, or if the relationship between variables is monotonic but not strictly linear (Polit & Beck, 2017). Therefore, using Spearman’s correlation could be correct if the variables are ordinal or if assumptions for parametric tests are violated. It is crucial to evaluate whether the data meet conditions for parametric analysis; otherwise, the choice of a non-parametric test like Spearman's is justified.

Regarding the comparison between association and correlational analysis, these terms are often used interchangeably but can have nuanced differences. Correlational analysis specifically quantifies the degree and direction of the relationship between two variables, typically through a correlation coefficient such as Pearson's or Spearman's. Association, on the other hand, is a broader term that indicates a relationship or connection between variables, which may not necessarily be quantified or linear. For example, two variables can be associated without a measurable correlation coefficient if their relationship is non-linear or complex. Therefore, while all correlational analyses identify associations, not all associations are expressed through correlation coefficients. Understanding this distinction is vital when interpreting research findings in practice (Polit & Beck, 2017).

These findings influence the decision to use the evidence for practice change by highlighting the importance of understanding the nature of the relationship between variables. If the analysis employs appropriate methods considering the data's measurement level and distribution, the evidence can be valuable, even if the specific correlation type differs. However, if the statistical analysis does not match the data characteristics or research question, the credibility of the findings diminishes, affecting their utility in informing practice. In this case, recognizing that Spearman's correlation measures a monotonic relationship, not necessarily linear, is important. If the intervention or practice change depends on a linear relationship, the findings may need cautious interpretation. Nonetheless, if the evidence provides consistent and statistically significant associations relevant to clinical outcomes, it can support decision-making. Ultimately, critical appraisal of the methodology and the appropriateness of statistical tests is essential before integrating research evidence into practice (Polit & Beck, 2017).

References

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