DISCUSSION response: STATISTICAL ANALYSIS IN NURSING
Research for Evidence Based Practice
DISCUSSION RESPONSE: STATISTICAL ANALYSIS IN NURSING
Read a selection of your colleagues’ responses and
respond to
two of your colleagues in one or more of the following ways:
· Ask a probing question, substantiated with additional background information, evidence, or research.
· Share an insight from having read your colleagues’ postings, synthesizing the information to provide new perspectives.
· Offer and support an alternative perspective using readings from the classroom or from your own research in the Walden Library.
· Validate an idea with your own experience and additional research.
· Suggest an alternative perspective based on additional evidence drawn from readings or after synthesizing multiple postings.
· Expand on your colleagues’ postings by providing additional insights or contrasting perspectives based on readings and evidence.
PEER #1
Alba Gisel Natale
Selected Article: Kim, Y. H., Min, J., Kim, S. H., & Shin, S. (2018). Effects of a work-based critical reflection program for novice nurses.
BMC Medical Education, 18(1), 30.
Goals and Purpose of the Research Study
The overall goal of the research study by Kim et al., (2018) was to quantify the effects of critical reflection training among novice nurses and provide strategic guidelines on how to enhance the effects of the training. Specifically, the study aimed to evaluate the effectiveness of a work-based critical reflection program in enhancing novice nurses’ clinical critical-thinking abilities, communication competency, and job performance. The researchers sought to contribute insights into the impact of critical reflection on nursing education and professional development, focusing on the practical application of theoretical knowledge and the bridging of gaps between theory and practice in complex clinical situations.
Nonparametric Tests Used in Research Study and Results
The nonparametric tests employed in the study included the Mann-Whitney U test and the Wilcoxon rank sum test. The Mann-Whitney U test is used to compare two independent groups and is similar to the 2-sample t-test but doesn’t rely on assumptions about the distribution of the data, assessing whether observations randomly selected from one group are more likely to be higher or lower than those from the other group (Schober & Vetter, 2020). These tests were utilized to assess differences in mean ranks and to evaluate whether the medians were equal across the control and experimental groups, with a significance level set at p < 0.05.
In terms of clinical critical-thinking skills (CCTS), the study found no significant difference between the control and experimental groups before the intervention (p = 0.482, Mann-Whitney U test). However, after the intervention, there was a notable improvement in clinical critical-thinking skills in the experimental group compared to the control group, and this difference was statistically significant (p = 0.003, Mann-Whitney U test). Regarding communication ability, the analysis revealed no significant difference between the control and experimental groups before the intervention (p = 0.143, Mann-Whitney U test). However, after the intervention, a significant difference in mean ranks was observed between the two groups (p = 0.028, Wilcoxon rank sum test), indicating an impact on communication competency following the work-based critical reflection program. Concerning job performance, there was no significant difference between the control and experimental groups before the intervention (p = 0.724, Mann-Whitney U test). After the intervention, no significant difference was found in job performance between the two groups (p = 0.294, Wilcoxon rank sum test), suggesting that, in this context, the work-based critical reflection program did not significantly affect job performance.
The nonparametric test results indicate a positive impact of the work-based critical reflection program on clinical critical-thinking skills and communication ability among novice nurses. However, there was no significant difference in job performance between the control and experimental groups following the intervention based on the results of the study.
Use of Nonparametric Tests over Parametric Tests
The choice of nonparametric methods by the authors for this research study ranks over parametric tests like t-tests and ANOVA in the statistical analysis due to a few key considerations by the authors. Firstly, the assumption of a normal distribution (inherent in parametric tests) may not be met in this study. Clinical critical-thinking skills and communication competency scores among novice nurses might exhibit significant skewness, making nonparametric tests more suitable. Additionally, the assumption of homogeneity of variances which are crucial for parametric tests, may be violated if the variability in job performance scores varies substantially between the control and experimental groups. Nonparametric tests are particularly advantageous when dealing with ordinal data or when the sample size is small, as they don’t rely on stringent assumptions about data characteristics (Gray & Grove, 2020). In instances where the data may not strictly adhere to interval properties, nonparametric tests offer greater flexibility. Lastly, nonparametric tests are robust to outliers, which can be influential in parametric analyses. By opting for nonparametric methods, the researchers ensure a stronger and more reliable analysis, aligning with the specific characteristics of their data and avoiding potential biases associated with parametric assumptions.
Strengths and Weakness of the Research Study
This study exhibits several strengths, including a well-defined research goal focused on evaluating the impact of a work-based critical reflection program on novice nurses and the quasi-experimental design which allows for systematic comparisons between the control and experimental groups. This design study contributes to a structured assessment of the program’s effectiveness. Choosing suitable nonparametric statistical methods is in line with the data’s characteristics, recognizing that the data may not adhere to the assumptions required for parametric analyses. The study’s emphasis on practical implications, particularly in enhancing clinical critical-thinking abilities and communication skills can also be seen as a strength. Furthermore, using a work-based approach ensures that the findings apply to the actual situations in nursing practice.
However, certain weaknesses need to be addressed such as the limited generalizability stemming from the study’s reliance on a sample from a single hospital in South Korea which raises questions about the broader applicability of the results. The relatively small sample size, although meeting statistical power requirements, may affect the reliability and generalizability of the findings. Also, the study doesn’t thoroughly account for possible influencing factors like individual learning styles. There are concerns about the reliability of the instruments used, especially the Clinical Critical Thinking Skills test, due to its relatively low internal consistency. The study also lacks detailed information on ensuring the fidelity of the intervention across reflective practitioners (RPs), introducing variability in program implementation. Lastly, the study’s scope of outcome measures is somewhat limited, and incorporating additional measures related to stress reduction or patient care outcomes could offer a more comprehensive understanding of the program’s impact. While the study provides valuable insights, its limitations underscore the need for cautious interpretation and suggest avenues for improvement in future research.
Contribution to EBP in Nursing
The study’s findings and recommendations can help improve how nurses are trained and develop their skills. By showing that a work-based critical reflection program positively influences crucial abilities like critical thinking and communication, the research supports the idea that hands-on experience is essential in nursing. This aligns with evidence-based practice principles, where insights from research, combined with practical knowledge and patient needs, guide healthcare decisions. The study also highlights areas for enhancement, like addressing potential influencing factors and improving assessment tools, providing practical guidance for future training programs. Overall, these findings contribute to making nursing education and practices more effective and patient-centered.
References
Gray, J. R., & Grove, S. K. (2020).
Burns and Grove’s the practice of nursing research: Appraisal, synthesis, and generation of evidence (9th ed.). Elsevier.
Kim, Y. H., Min, J., Kim, S. H., & Shin, S. (2018). Effects of a work-based critical reflection program for novice nurses.
BMC Medical Education,
18(1).
Links to an external site.
Schober, P., & Vetter, T. R. (2020). Nonparametric statistical methods in medical research.
Anesthesia & Analgesia,
131(6), 1862–1863.
to an external site.
PEER #2
Penny Pope
MAIN POST
Statistical Analysis in Nursing
The article I selected for critical analysis is “Nonparametric Statistical Methods in Medical Research” by Schober and Vetter (2020). This article explores the use of “nonparametric statistical methods in medical research,” specifically focusing on “the Mann-Whitney U test.” “Nonparametric” tests become relevant when the assumptions of parametric tests concerning the “data distribution” are not fulfilled. In this critical analysis, I will examine the goals, purpose, and methods employed in the study, along with the results and their implications for nursing practice.
Goals and Purpose of the Research Study
The primary goal of Schober and Vetter’s (2020) research study is to highlight the significance of “nonparametric statistical methods in medical research.” The authors emphasize the utility of these methods, particularly “the Mann-Whitney U test,” as an alternative “when data distribution assumptions of parametric tests are not satisfied.” The purpose is to provide researchers and practitioners with insights into when and why nonparametric methods should be preferred.
Use of Nonparametric Tests in the Research Study
In their study, Schober and Vetter (2020) specifically focus on the “Mann-Whitney U test.” They employed the “test to compare numerical rating scale pain scores between different groups,” specifically in a trial examining “the effects of preoperative gum chewing on sore throat after general anesthesia with a supraglottic airway device.” “The Mann-Whitney U test” is chosen because the pain score data are not normally distributed (Wang et al., 2020).
Results of Nonparametric Tests
“The Mann-Whitney U test” in the study compares patients’ self-reported pain scores between the gum-chewing and control groups. The findings show that the two groups’ pain scores differ significantly from one another. This test does not compare medians directly but assesses whether “observations from one group are more likely to be higher or lower than those from the other group” (Schober & Vetter, 2018).
Why Parametric Methods (tests and ANOVA) are Inappropriate
Parametric methods, such as t-tests and ANOVA, assume specific data distributions like the normal distribution. In the context of Schober and Vetter’s (2020) study, the authors chose the “Mann-Whitney U test” because the pain score data are not normally distributed. Parametric methods would be inappropriate here as they rely on assumptions not met in this case (Vetter & Schober, 2018).
Strengths and Weaknesses of the Research Study
The strength of the study lies in its clear focus on explaining nonparametric methods and their application, particularly the Mann-Whitney U test. By using a specific medical research example, the authors make the application of these methods more tangible. However, a potential weakness could be the limited generalizability of findings since the study mainly serves an educational purpose rather than presenting new empirical data.
Contribution of the Findings and Recommendations to Evidence-Based Practice for Nursing
The findings of Schober and Vetter’s (2020) study contribute to evidence-based practice by highlighting the appropriateness of nonparametric methods in situations where parametric assumptions are not met. DNP-prepared nurses can benefit from this knowledge by understanding when to choose nonparametric tests for their research or when critically evaluating existing literature. This enhances the overall methodological rigor and validity of nursing research.
In conclusion, Schober and Vetter’s (2020) study is valuable in emphasizing the importance of nonparametric statistical methods in medical research, particularly nursing. As highlighted in the study, the Mann-Whitney U test becomes crucial when dealing with non-normally distributed data (Schober & Vetter, 2020). DNP-prepared nurses should be familiar with such nonparametric methods to make informed decisions about statistical analyses in their research, ultimately contributing to evidence-based practice in nursing.
References
Schober, P., & Vetter, T. R. (2020). Nonparametric Statistical Methods in Medical Research. Anesthesia & Analgesia, 131(6), 1862–1863.
Links to an external site.
Schober, P., & Vetter, T. R. (2018). Repeated measures designs and analysis of longitudinal data: If at first you do not succeed try, try again. Anesthesia and analgesia, 127(2), 569.
Schober, P., & Vetter, T. R. (2020). Correlation analysis in medical research. Anesthesia & Analgesia, 130(2), 332.
Vetter, T. R., & Schober, P. (2018). Regression: the apple does not fall far from the tree. Anesthesia & Analgesia, 127(1), 277-283.
Wang, T., Wang, Q., Zhou, H., & Huang, S. (2020). Effects of preoperative gum chewing on sore throat after