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 main purpose of this discussion is to strengthen the understanding of non-parametric statistical tests and their relevance in clinical research. Non-parametric tests are particularly valuable when dealing with small samples, skewed data, or non-normal distributions, which are commonly observed in healthcare research. By evaluating their use, scholars can determine whether evidence derived from such studies is reliable enough to guide practice change.
Instructions
As a practice scholar, it is essential to critically evaluate evidence before translating it into clinical practice. In this scenario, a quasi-experimental study is identified as potential support for a practice change. The study attempts to establish a prediction related to correlations between variables. However, the sample size is small, and the data are not normally distributed. This raises questions about whether the statistical test applied—Pearson’s r correlation—is appropriate for such a dataset.
To address this scenario, the following guiding questions are explored:
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 assumes normal distribution, linearity, and equal variance. Since the study sample is small and not normally distributed, Pearson’s r may not be the most appropriate choice. A non-parametric alternative, such as Spearman’s rank-order correlation, would be more suitable. Spearman’s rho evaluates the monotonic relationship between variables without requiring normality.
NR 716 Week 6 Using Non-Parametric Statistical Tests
Using Pearson’s r in this scenario could lead to biased or inaccurate results because the test’s assumptions are violated. Thus, employing a non-parametric test would yield more credible findings and strengthen the validity of the research outcomes.
Question 2: Are association and correlational analysis equivalent in determining relationships between variables?
Although association and correlation are related concepts, they are not identical.
| Concept | Definition | Key Features |
|---|---|---|
| Association | A broad term indicating that two variables are related in some way. | Does not specify the strength, type, or direction of the relationship. |
| Correlation | A statistical measure that quantifies the degree and direction of a linear or monotonic relationship. | Provides both magnitude (strength) and direction (positive or negative). |
While both terms describe relationships, correlation is a more specific and quantifiable measure of association. Association can be observed without statistical testing, whereas correlation requires statistical analysis to quantify the strength and direction of the relationship.
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 significantly impact the decision to adopt the evidence for practice change. If an inappropriate test such as Pearson’s r is applied to non-normal, small-sample data, the validity of the results becomes questionable. Evidence-based practice requires confidence in the robustness of research findings. In this case, the misuse of statistical tests suggests that the study’s outcomes may not be reliable.
Therefore, before integrating this evidence into practice, the study should be reassessed, ideally reanalyzed using non-parametric methods such as Spearman’s rho or Kendall’s tau. Only then can the findings be considered trustworthy for informing clinical decision-making.
Program Competencies
This discussion supports the development of the following competencies:
- Integration of scientific knowledge into daily clinical practice (POs 3, 5).
- Application of analytic methods to transform critically appraised research into innovative clinical improvements (POs 3, 5).
- Evaluation of information systems and technologies to optimize healthcare delivery (POs 6, 7).
- Analysis of healthcare policies to advocate for social justice and equitable healthcare (POs 2, 9).
- Translation of research and population data into preventive care strategies to improve population health (PO 1).
- Leadership in professional identity and judgment, fostering resilience and accountability in clinical care (POs 1, 4).
Course Outcomes
Through this discussion, students achieve the following outcomes:
- Evaluate statistical methods to critique research and enhance evidence appraisal (PCs 1, 3, 5; POs 3, 5, 9).
- Analyze both research and non-research data for critical appraisal and judgment to guide practice translation (PCs 1, 3, 4, 5, 7, 8; POs 1, 3, 5, 7, 9).
References
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
NR 716 Week 6 Using Non-Parametric Statistical Tests
Conover, W. J. (1999). Practical nonparametric statistics (3rd ed.). Wiley.