Review The Articles Listed And Analyze Each Study's Use Of S

Review The Articles Listed And Analyze Each Studys Use Of Statistical

Review the articles listed and analyze each study’s use of statistical and nonparametric tests. Select an article to focus on for this discussion. Ask yourself: Which method is most commonly used in research studies that pertain to my area of nursing practice, and why this might be so? Post a critical analysis of the article that you selected by addressing the following: What are the goals and purpose of the research study described by the article you selected? How are nonparametric tests used in the research study? What are the results of their use? Be specific. Why are parametric methods (tests and ANOVA) inappropriate for the statistical analysis of the research study’s data? Be specific and provide examples. What are the strengths and weaknesses of the research study (e.g., study design, sampling, and measurement)? How could the findings and recommendations of the research study contribute to evidence-based practice for nursing?

For this assignment, I have selected the article by Schober and Vetter (2020) titled “Nonparametric statistical methods in medical research.” This article provides an insightful overview of the application, advantages, and limitations of nonparametric statistical methods in medical studies, which are essential for nursing research that frequently involves ordinal data, small sample sizes, or non-normal distributions.

Critical Analysis of the Selected Research Study

The primary goal of Schober and Vetter’s (2020) article is to elucidate the appropriate application of nonparametric tests in medical research where data do not meet the assumptions necessary for parametric tests. The paper emphasizes that nonparametric statistics are particularly useful when data are ordinal, skewed, or when sample sizes are insufficient to reliably assess normality. The article aims to aid researchers in selecting suitable statistical methods to enhance the validity and reliability of their findings, especially in situations where parametric assumptions cannot be satisfied.

Within the study, nonparametric tests are used extensively across various research contexts to analyze data types that violate the assumptions underlying parametric tests like t-tests and ANOVA. For example, the Mann-Whitney U test and the Wilcoxon signed-rank test are highlighted as alternatives to the independent and paired t-tests, respectively. These tests do not assume normal distribution and are less sensitive to outliers, making them ideal for small sample sizes or skewed data. In the article, Schober and Vetter discuss a hypothetical example where these tests are applied to compare patient outcomes between treatment groups, demonstrating how nonparametric methods can provide valid results when parametric tests are inappropriate.

The results of using nonparametric tests in the study reinforce that these methods are robust for analyzing ordinal or non-normal data. They produce p-values and significance testing similar to parametric tests but require no assumptions about the underlying data distribution. For instance, using the Mann-Whitney U test instead of an independent t-test yields accurate comparisons of two independent groups with skewed data or ordinal scales, such as Likert scale responses. The article demonstrates that nonparametric tests tend to have less statistical power than parametric tests when parametric assumptions are met but are more reliable in the presence of assumption violations.

Appropriateness of Parametric Methods

Parametric methods such as t-tests and ANOVA are inappropriate in many cases discussed by Schober and Vetter because they assume that the data are normally distributed, have homogeneity of variances, and are measured on interval or ratio scales. When these assumptions are violated—such as with ordinal data or small sample sizes—parametric tests can produce misleading results. For example, applying ANOVA on skewed data from Likert scales can lead to inflated Type I or Type II errors, distorting the inference about group differences. The article emphasizes that nonparametric tests serve as valid alternatives in these scenarios, offering more accurate interpretations without the assumptions of normality and homoscedasticity.

Strengths and Weaknesses of the Study

The strengths of Schober and Vetter’s (2020) article include its clarity in explaining complex statistical concepts and practical guidance for applying nonparametric methods in research. Its focus on real-world examples enhances understanding among healthcare professionals and researchers, emphasizing the importance of choosing appropriate statistical tools. Additionally, the article’s comprehensive overview of various nonparametric tests provides a valuable resource for designing methodologically sound studies.

However, a weakness is that the article primarily offers a theoretical review rather than presenting empirical data or case studies from actual research projects. This limits insights into the challenges researchers may face when implementing these tests in practice. Also, the article does not extensively discuss the limitations of nonparametric methods, such as potential reductions in statistical power, which could be crucial for researchers to consider when designing studies.

Implications for Evidence-Based Nursing Practice

The findings and recommendations from Schober and Vetter’s (2020) article can significantly contribute to evidence-based nursing practice by guiding researchers to select appropriate statistical analyses for their data. Accurate data analysis ensures valid conclusions, ultimately informing clinical decision-making and quality improvement initiatives. For example, in nursing research involving patient satisfaction or symptom severity measured on ordinal scales, nonparametric methods can provide valid insights that influence clinical guidelines and interventions. Furthermore, understanding when to use nonparametric versus parametric tests enhances the rigor of research, leading to more trustworthy evidence that can be translated into practice.

References

• Schober, P., & Vetter, T. R. (2020). Nonparametric statistical methods in medical research. Anesthesia & Analgesia, 131(6), 1862–1863. doi:10.1213/ANE.0000000000005427
• Hollander, M., Wolfe, D. A., & Chicken, E. (2013). Nonparametric statistical methods. John Wiley & Sons.
• Conover, W. J. (1999). Practical nonparametric statistics. John Wiley & Sons.
• Fagerland, M. W., & Sandvik, L. (2009). The Mann–Whitney U test under scrutiny. -statistics in medicine, 28(1), 88-112.
• Norman, G., & Streiner, D. (2008). Biostatistics: The bare essentials. PMPH-USA.
• Lehmann, E. L., & Romano, J. P. (2005). Testing statistical hypotheses. Springer Science & Business Media.
• McKnight, P. E., & Najab, J. (2010). The Kruskal–Wallis test. In The Corsini encyclopedia of psychology, 2000.
• Navidi, W. (2018). Statistics for data science and analytics: techniques for discovering patterns in data. McGraw Hill.
• Stevens, J. P. (2009). Applied multivariate statistics for the social sciences. Routledge.
• Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.