NR 716 Week 6 Using Non-Parametric Statistical Tests

NR 716 Week 6 Using Non-Parametric Statistical Tests

Student Name

Chamberlain University

NR-716: Analytic Methods

Prof. Name:

Date

Using Non-Parametric Statistical Tests

Discussion

Purpose

The purpose of this discussion is to enhance understanding of non-parametric statistical tests and their significance in clinical research. Unlike parametric tests, non-parametric approaches are advantageous when sample sizes are small, data distributions are skewed, or assumptions of normality are not met. Such circumstances are frequently encountered in healthcare-related studies. Evaluating the appropriate use of these tests allows practice scholars to determine whether the results of a study are reliable enough to guide evidence-based practice changes.

Instructions

As developing practice scholars, it is crucial to assess the validity and applicability of research findings before implementing them into clinical settings. In this scenario, a quasi-experimental study is proposed to support a clinical practice change. The researchers attempt to analyze potential relationships between variables but face challenges due to a small sample size and non-normally distributed data. Despite this, the study applies Pearson’s r correlation, raising concerns about the appropriateness of the statistical method used.

The following guiding questions provide a structured approach to critically appraising the evidence and determining whether the findings can be translated into practice.

Question 1: In your appraisal of the evidence, you note that a Pearson’s r correlation is used to analyze data. Is this the correct level of correlational analysis? Explain your rationale.

Pearson’s r correlation is a parametric test that requires assumptions of normal distribution, linearity, and homoscedasticity (equal variance). When these assumptions are not satisfied, results may be misleading or invalid. Given that the study involves a small sample size and non-normal distribution, Pearson’s r is not the most suitable choice.

Instead, a non-parametric test such as Spearman’s rank-order correlation would be more appropriate. Spearman’s rho measures the strength and direction of a monotonic relationship between two variables without requiring normally distributed data. Another potential alternative is Kendall’s tau, which is often preferred when dealing with very small datasets because it provides a more robust measure of correlation.

Using Pearson’s r under these conditions risks producing biased findings and weakens the credibility of the research. Employing non-parametric methods would ensure more valid and reliable outcomes that better support clinical decision-making.

Question 2: Are association and correlational analysis equivalent in determining relationships between variables?

Association and correlation are related but not interchangeable concepts. Their differences are summarized below:

While association is a broader term that suggests a relationship exists, correlation specifically measures and quantifies that relationship. Association can sometimes be identified through simple observation or descriptive statistics, whereas correlation requires analytical testing. Therefore, correlation can be viewed as a subset of association that provides greater precision in determining the nature of the relationship between variables (Schober et al., 2018).

Question 3: Do these findings impact your decision about whether to use this evidence to inform practice change? Why or why not?

Yes, the findings substantially affect the decision to use this evidence in practice. If the researchers relied on Pearson’s r for a small, non-normally distributed dataset, the validity of their results is questionable. Evidence-based practice demands robust, credible findings that can be trusted to guide patient care. Since statistical assumptions were violated, the study’s conclusions may not be reliable enough to warrant practice changes.

To improve the credibility of the research, the dataset should be reanalyzed using non-parametric methods such as Spearman’s rho or Kendall’s tau. Only after reassessing the findings with appropriate statistical tools can the evidence be considered for integration into clinical practice. This approach ensures that practice changes are grounded in accurate and trustworthy research outcomes.

Program Competencies

This discussion aligns with and supports the development of several key program competencies, including:

• Integration of scientific knowledge into clinical practice (POs 3, 5).

• Application of analytic methods to critically evaluate research and translate findings into innovative clinical improvements (POs 3, 5).

• Evaluation of information systems and technologies to enhance healthcare outcomes (POs 6, 7).

• Analysis of healthcare policies to promote equity and social justice (POs 2, 9).

• Translation of population data into preventive strategies that improve community health outcomes (PO 1).

• Leadership in professional practice that supports accountability, resilience, and ethical judgment in care delivery (POs 1, 4).

Course Outcomes

Through this activity, students will:

• Evaluate statistical techniques to appraise research rigor and enhance evidence-based practice (PCs 1, 3, 5; POs 3, 5, 9).

• Critically analyze research and non-research data to guide clinical decision-making and practice translation (PCs 1, 3, 4, 5, 7, 8; POs 1, 3, 5, 7, 9).

References

Conover, W. J. (1999). Practical nonparametric statistics (3rd ed.). Wiley.

Polit, D. F., & Beck, C. T. (2021). Nursing research: Generating and assessing evidence for nursing practice (11th ed.). Wolters Kluwer.

NR 716 Week 6 Using Non-Parametric Statistical Tests

Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763–1768. https://doi.org/10.1213/ANE.0000000000002864