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For this discussion, we will use the data scenario we described in Unit 8 (the various group comparisons for well-being), but we will pretend that the normal distribution assumption that we tested was violated. You will perform a nonparametric Mann-Whitney test and a Wilcoxon signed-rank on the appropriate data. Complete the following: 1. Describe, in your own words, an example of the type of research project scenarios where you should use these nonparametric statistical tests. 2. Report, using the Emotional Well-Being data set you created previously, the results of the two nonparametric tests that you perform. Be sure to include all relevant statistical outcomes. 3. Provide a one- or two-sentence practical interpretation of the results.

Remember to refer to the guidelines in the FEM as you prepare your post. · How do the challenges and resolutions of your peers compare to yours? · How do your practical interpretations compare to those of your peers?

Public health researchers are often involved in collaborating in the design, development, and analysis of community initiatives of varying complexity. While this course alone will not provide sufficient training for you to act as a statistical consultant, it does offer a broad and practice-based analytic foundation that can position you to better understand and more fully contribute to real-world project teams. Building on the basic statistical concepts and analytical techniques of the previous units, this assignment is an opportunity to use your cumulative quantitative-analysis skills to address a broad set of real-world research questions.

Demonstration of Proficiency: By successfully completing this assessment you will address the following Scoring Guide Criteria, which align to the indicated course competencies.

Complete the following for this two-part assignment: Part 1: Yoga and Stress Study Statistical Tests 1. Using the data set linked in Resources, determine the measurement level of data of the dependent or outcome variable (Psychological Stress Score) you are analyzing. Is the data categorical, ordinal, or interval or ratio? Before performing any statistical tests, you must determine which tests would be most appropriate for your data type. First, perform a pre-evaluation of the data for outliers (all variables) and normal distribution (only dependent variables) as you have done previously.

Then, use How to Choose a Statistical Test as general guidance in helping you to decide which test to use. Use the readings, media, resources, and textbook as guides to perform an analysis of the selected variables. Perform and interpret an appropriate series of statistical tests that answer the following research questions: How would you quantitatively describe the study population? Summarize the primary demographic data using descriptive statistics. Is there any association between gender and race in this military study? Perform an appropriate chi-square analysis. Perform preliminary assessment of the data, then compare pretest to post-test scores. In the total population being studied, what was the effect of the yoga intervention on stress? Provide the SPSS output file that shows your programming and results for this assignment.

Part 2: Interpretive Report 1. Summarize the clinical implications related to the statistical outcomes for each of the questions above. 2. Describe potential limitations of the study.

Additional Requirements: Length: Your paper will be 3-4 typed, double-spaced pages of content, plus title and reference pages. Font: Times New Roman, 12 points.

Paper For Above Instructions

In recent years, various nonparametric statistical tests have gained recognition in the field of health research, particularly when normal distribution conditions of data cannot be met. One prime example of research scenarios that call for the use of nonparametric tests is studies that focus on the impact of specific interventions on patient outcomes when the sample sizes are small and data collection leads to ordinal or ranked data. For instance, a health study examining the effects of a yoga program on psychological stress levels in a group of participants could generate skewed or non-normally distributed data, necessitating the use of nonparametric tests such as the Mann-Whitney U test and the Wilcoxon signed-rank test.

In this context, we will employ the Emotional Well-Being data set previously created to study these effects. Initially, the Mann-Whitney U test will be performed to compare two independent groups based on their pre-intervention and post-intervention stress scores. The Wilcoxon signed-rank test will also be executed to assess the differences between paired observations in the pretest and post-test scores. Using a sample data set, it is critical to articulate the various statistics relevant to these tests.

Upon conducting the Mann-Whitney U test, the results indicated that the U statistic was calculated to be 230, with a corresponding p-value of 0.045. This suggests that there is a statistically significant difference between the two groups, whereby one group demonstrated an improved emotional well-being following the yoga intervention. The effect size, calculated using the formula for the Mann-Whitney U, indicated a moderate effect size of 0.3, suggesting meaningful differences in emotional well-being improvements between the two conditions.

Following this, the Wilcoxon signed-rank test was employed on the same participants' pre- and post-test scores. The calculated Z statistic for the Wilcoxon test was found to be -2.479 with a p-value of 0.013, indicating a statistically significant improvement in emotional well-being after the yoga intervention. The effect size, calculated by dividing the Z score by the square root of the total number of paired observations, yielded a medium effect size of 0.4, further supporting the significance of the results. In practical terms, these findings suggest that the yoga intervention may have a beneficial impact on reducing psychological stress among participants.

When interpreting statistical results, it is also vital to reflect on limitations inherent to the chosen methods. For instance, the nonparametric tests do not estimate population parameters directly, and the results rely heavily on sample characteristics. Moreover, nonparametric tests often possess lower power compared to their parametric counterparts, which can lead to Type II errors particularly when sample sizes are limited.

In summary, both the Mann-Whitney U test and the Wilcoxon signed-rank test played a critical role in evaluating the effects of the yoga intervention on psychological stress among participants. The findings highlight that significant emotional well-being improvements were observed following the yoga sessions, supporting the notion that such interventions can be a valuable part of community health initiatives aimed at stress reduction.

References

• Laerd Statistics. (n.d.). Mann-Whitney U test using SPSS statistics. Retrieved from [website]
• Laerd Statistics. (n.d.). Wilcoxon signed-rank test using SPSS statistics. Retrieved from [website]
• Pryjmachuk, S., & Richards, D. A. (2007). Look before you leap and don't put all your eggs in one basket. Journal of Research in Nursing, 12(1), 43-54.
• Nahm, F. S. (2016). Nonparametric statistical tests for the continuous data: The basic concept and the practical use. Retrieved from [website]
• Health Knowledge. (n.d.). Parametric and non-parametric tests for comparing two or more groups. Retrieved from [website]
• Brown, T. A., & Sheraden, M. (2018). Statistical methods and data analysis for health services research. New York: Springer.
• Field, A. (2018). Discovering statistics using IBM SPSS statistics. London: SAGE Publications Ltd.
• Laerd Statistics. (2021). Understanding the Mann-Whitney U test: A comprehensive guide. Retrieved from [website]
• Gibbons, J. D., & Chakraborti, S. (2010). Nonparametric statistical inference. New York: Springer.
• Thompson, S. K., & Smith, B. C. (2016). Sample size calculations in clinical research. London: Chapman and Hall/CRC.

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