Name
Chamberlain University
NR-716: Analytic Methods
Prof. Name
Date
The primary purpose of this discussion is to enhance understanding of non-parametric statistical tests and highlight their significance in healthcare research. These tests are particularly useful when data violate assumptions required for parametric tests, such as normality or equal variances. Clinical research frequently involves small sample sizes or skewed distributions, making non-parametric approaches critical in ensuring reliable evidence. By critically examining their application, scholars can evaluate whether findings from such studies are valid enough to support practice change and clinical decision-making.
As practice scholars, it is crucial to appraise evidence carefully before translating it into clinical settings. In the provided scenario, a quasi-experimental study aims to test correlations between variables. However, the dataset is limited in size and not normally distributed. Despite this, the researchers used Pearson’s r correlation, a statistical method that assumes normality. This raises important concerns regarding the appropriateness of the test and the trustworthiness of its findings.
To address this issue, the following guiding questions are explored:
Pearson’s r correlation is a parametric test that assumes linearity, normal distribution, and homogeneity of variance. Because the study’s sample is both small and non-normally distributed, Pearson’s r is not the most appropriate statistical choice. Using it in this situation risks producing misleading results due to violated assumptions.
Instead, a non-parametric alternative, such as Spearman’s rank-order correlation (rho) or Kendall’s tau, is better suited. These methods evaluate the monotonic relationship between two variables without requiring normality. Specifically, Spearman’s rho can identify whether higher values of one variable correspond to higher (or lower) values of another, regardless of whether the data follow a linear pattern.
Using non-parametric methods strengthens the credibility and validity of findings, thereby supporting more accurate evidence-based practice decisions.
Although the terms association and correlation are sometimes used interchangeably, they are not synonymous.
| Concept | Definition | Key Features |
|---|---|---|
| Association | A general term describing a relationship between two or more variables. | Broad in scope, does not specify the strength, type, or direction of the relationship. |
| Correlation | A statistical measure that quantifies the strength and direction of a linear or monotonic relationship. | Provides numerical value, ranging from -1.0 to +1.0, and requires statistical testing. |
In summary, association can be observed without statistical testing (e.g., two variables appearing related), while correlation specifically quantifies that relationship through statistical methods. Therefore, correlation provides a more precise measure of association.
Yes, these findings directly influence the decision about using the evidence for practice change. Evidence-based practice relies on the use of rigorous, methodologically sound studies. If an inappropriate statistical test such as Pearson’s r is applied to data that are non-normal and small in size, the validity of results is compromised. This creates uncertainty regarding whether the findings can truly be trusted to guide clinical improvements.
Before adopting this evidence, the study should undergo reassessment, ideally with reanalysis using Spearman’s rho or Kendall’s tau. Only through proper statistical testing can the results be deemed reliable enough to support evidence-based clinical interventions.
This discussion aligns with the following competencies:
Integration of scientific knowledge into daily clinical practice (POs 3, 5).
Application of analytical methods to transform research evidence into innovative clinical improvements (POs 3, 5).
Evaluation of information systems and technologies for enhancing healthcare delivery (POs 6, 7).
Policy analysis to support social justice and equitable healthcare access (POs 2, 9).
Translation of research findings into preventive strategies to strengthen population health (PO 1).
Leadership in professional identity and judgment, promoting resilience and accountability in clinical settings (POs 1, 4).
Through this discussion, learners are expected to:
Critically evaluate statistical methods to improve the appraisal of evidence (PCs 1, 3, 5; POs 3, 5, 9).
Analyze both research-based and non-research data to guide decision-making and practice translation (PCs 1, 3, 4, 5, 7, 8; POs 1, 3, 5, 7, 9).
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.
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
