As A Nurse Engaged In Evidence-Based Practice It Is Importan
As A Nurse Engaged In Evidence Based Practice It Is Important To Reco
As a nurse engaged in evidence-based practice, it is important to recognize how statistics and other data analysis tools are used to generate and assess evidence. Most nurses need only a foundational understanding of statistical tools and terminology to understand the majority of research studies. As a nurse, you should be able to recognize the most commonly used statistical tests, how and when they are used, and how significance is determined. In this discussion, you examine different types of statistics and statistical tests, when and why these particular tests would be selected for use, and, most importantly, what the results indicate.
Each group will be assigned one of the five chapters listed in this week’s learning resources and will develop a study sheet on their chapter that will be shared with the other groups. Review the information in your assigned chapter. As a group, develop a 1-page study sheet that includes the following:
- The key concepts of the chapter: Focus on the basic concepts that are important for nurses to understand as they review research studies.
- A description of the statistical methods covered in the chapter, what they measure, and under what circumstances they are used.
- Identify examples of how the statistical methods have been used in research studies.
- An explanation of the key statistical tests and how they measure significance (if applicable).
Note: This should be a collaborative effort, with each member of the group making contributions to the design and content of the study sheet. Use the groups link on the left navigation bar to collaborate with your group. When you have developed your 1-page study sheet, select one member to post it to the Week 9 discussion forum so that the rest of your colleagues can access it.
To prepare: Post your group’s study sheet.
Discuss why it is important for nurses to understand the basics of these statistical methods.
Paper For Above instruction
The understanding of statistical methods is a vital component of evidence-based nursing practice, particularly when interpreting and applying research findings to clinical settings. Chapter 17 specifically delves into advanced statistical concepts relevant for nurses, emphasizing how statistical analysis underpins the evaluation of evidence and informs decision-making. This paper summarizes the key concepts of Chapter 17, describes the statistical methods it covers, explains the key statistical tests used to determine significance, and illustrates their application in nursing research.
Key Concepts of Chapter 17
Chapter 17 centers on inferential statistics, which allow researchers and nurses to make conclusions about populations based on sample data. It emphasizes understanding variability, hypothesis testing, confidence intervals, and the significance of p-values. Recognizing these concepts helps nurses evaluate research validity and applicability. The chapter underscores the importance of understanding measures of central tendency and variability, such as means, medians, and standard deviations, which describe data distribution. This foundational knowledge is crucial for nurses to scrutinize research articles critically and determine the relevance of findings.
Statistical Methods Covered and Their Usage
Chapter 17 discusses various statistical methods, including descriptive statistics (mean, median, mode, standard deviation), inferential techniques (t-tests, ANOVA, chi-square tests), and non-parametric tests (Mann-Whitney U, Kruskal-Wallis). Descriptive statistics summarize data, providing an overview of the sample characteristics. Inferential tests help determine whether observed differences between groups are statistically significant or likely due to chance. Parametric tests such as t-tests and ANOVA are used when data is normally distributed and measured on an interval or ratio scale. Non-parametric tests apply when data is ordinal or not normally distributed, offering more flexibility in diverse data conditions.
Examples of Statistical Methods in Research
In nursing research, t-tests are commonly used to compare the means of two groups, such as testing the effectiveness of two different pain management interventions. ANOVA extends this comparison to multiple groups, such as evaluating several teaching strategies on patient compliance. Chi-square tests assess relationships between categorical variables, like the association between smoking status and lung function impairment. Non-parametric tests like Mann-Whitney U are applied when the data does not meet parametric assumptions, such as ordinal pain scores or Likert scale responses.
Key Statistical Tests and How They Measure Significance
The p-value is a core element in hypothesis testing, indicating the probability that the observed results occurred by chance. A p-value less than 0.05 typically signifies statistical significance, meaning there is a low likelihood the results are due to random variability. T-tests evaluate whether the difference between two means is statistically significant. ANOVA determines if there are significant differences among multiple groups. The chi-square test evaluates whether there is an association between categorical variables. These tests help nurses interpret whether interventions or variables have a meaningful impact, guiding clinical practice.
Importance of Understanding These Methods for Nurses
Mastering fundamental statistical concepts enables nurses to critically appraise research literature, discern credible evidence, and implement best practices. An understanding of statistical tests aids in recognizing meaningful results versus those that are statistically insignificant, preventing reliance on flawed or misinterpreted data. Furthermore, comprehension of data analysis empowers nurses to participate actively in research and quality improvement initiatives, fostering a culture of evidence-based care. As healthcare increasingly relies on data-driven decision-making, nursing professionals must develop statistical literacy to ensure safe, effective, and evidence-informed patient care.
Conclusion
Chapter 17 provides essential insights into the statistical tools that underpin research findings in nursing. By understanding descriptive and inferential statistics, as well as the significance of p-values and statistical tests, nurses are better equipped to evaluate research quality and applicability. Promoting statistical literacy among nurses enhances their competency in evidence-based practice, ultimately leading to improved patient outcomes and the advancement of nursing science.
References
1. Polit, D. F., & Beck, C. T. (2021). Nursing research: Generating and assessing evidence for nursing practice (11th ed.). Wolters Kluwer.
2. Burns, N., & Grove, S. K. (2019). Understanding nursing research: Building an evidence-based practice (7th ed.). Elsevier.
3. LoBiondo-Wood, G., & Haber, J. (2018). Nursing research: Methods and critical appraisal for evidence-based practice (9th ed.). Elsevier.
4. Thomas, D. R. (2020). Survey research methods (3rd ed.). Sage Publications.
5. Howell, D. C. (2017). Statistical methods for psychology (8th ed.). Cengage.
6. Cummings, G. G., Tate, K., Lee, S., Wong, C. A., Paananen, T., & Munn, J. (2018). Leadership styles and outcome patterns for the nursing workforce and work environment: A systematic review. International Journal of Nursing Studies, 85, 19-60.
7. Polit, D. F. (2022). Statistics in nursing research: A tool for evidence-based practice (4th ed.). Wolters Kluwer.
8. Gliner, J. A., Morgan, G. A., & Leech, N. L. (2020). Research methods in applied settings: An integrated approach to design and analysis (3rd ed.). Routledge.
9. Hinkle, D. E., Wiersma, W., & Jurs, S. G. (2019). Applied statistics for the behavioral sciences (6th ed.). Houghton Mifflin.
10. Berkowitz, S. A., & Herring, B. (2019). Empirical methods in health services research. Medical Care Research and Review, 76(1), 3-15.