Week 6 Using Non-parametric Statistical Tests Discussion Essay

Week 6 Using Non-parametric Statistical Tests Discussion Essay

Week 6 Using Non-parametric Statistical Tests Discussion Essay

Using Non-Parametric Statistical Tests

Using Non-Parametric Statistical Tests
Non-Parametric tests, also known as distribution-free tests, are a type of statistical test that do not require making assumptions from the underlying population. These tests are used when the data distribution in a given data set is not normal. This discussion presents the types of non-parametric statistical tests that can be used to analyze the data collected during the implementation of a selected practice change intervention.

ORDER A PLAGIARISM-FREE PAPER HERE

The selected practice problem is poor adherence to mental illness treatment and management, and the intervention implemented is caregiver support and education. The current intervention used in the healthcare setting is medication evaluation and management. According to Castillo et al. (2019), caregiver support and education effectively help improve mental illness treatment and management adherence. Caregivers can provide the required support to encourage patients to adhere to treatment.
The non-parametric statistical tests that can be used to evaluate whether the implementation of the new intervention is more effective than the intervention in current practice are the Mann-Whitney U-test and the Wilcoxon Signed-rank test. According to Orcan (2020), the Mann-Whitney U-test is a non-parametric statistical test used to test for continuous outcomes in two independent samples. Similarly, the practice change intervention sample and the current practice sample are independent; thus, this test is appropriate to assess the effectiveness of the change intervention.
Additionally, the test will enable logical comparison between the outcomes of the two samples since they are independent and contain similar population distributions. The Wilcoxon signed-rank test can also be used since it is used when comparing the outcomes of two matched or paired samples. The samples before and after the implementation of the change intervention can be paired to understand the outcomes of the intervention better. The test is appropriate in measuring the effectiveness of the practice change intervention since comparison of the outcomes from the paired sample is easier and can be used to determine which intervention has better outcomes.
The non-parametric statistical tests used to measure the effectiveness of the practice change intervention selected above are the Mann-Whitney U-test and the Wilcoxon Signed-rank test. These tests are the most appropriate non-parametric statistical tests for the intervention due to the ability to derive logical comparisons between independent samples.

References
Castillo, E. G., Ijadi-Maghsoodi, R., Shadravan, S., Moore, E., Mensah, M. O., 3rd, Docherty, M., Aguilera Nunez, M. G., Barcelo, N., Goodsmith, N., Halpin, L. E., Morton, I., Mango, J., Montero, A. E., Rahmanian Koushkaki, S., Bromley, E., Chung, B., Jones, F., Gabrielian, S., Gelberg, L., Greenberg, J. M., and Wells, K. B. (2019). Community Interventions to Promote Mental Health and Social Equity. Current Psychiatry Reports, 21(5), 35. https://doi.org/10.1007/s11920-019-1017-0
Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of Cardiac Anaesthesia, 22(1), 67. https://doi.org/10.4103%2Faca.ACA_157_18
Orcan, F. (2020). Parametric or non-parametric: Skewness to test normality for mean comparison. International Journal of Assessment Tools in Education, 7(2), 255–265. https://doi.org/10.21449/ijate.656077

ORDER A CUSTOM PAPER NOW

Week 6 Using Non-parametric Statistical Tests

Discussion

Purpose

The purpose of this discussion is to demonstrate your understanding of the use of non-parametric statistical tests.

Instructions

Select a practice-change problem and, from the literature, an intervention to impact outcomes. Imagine you are attempting to determine if the intervention is more effective than current practice. Explain the various types of non-parametric statistical tests that might be used to analyze the data collected during the implementation of the intervention. Provide a rationale for the use of non-parametric tests for this data set.

Please click on the following link to review the DNP Discussion Guidelines on the Student Resource Center program page:

Link (webpage): DNP Discussion Guidelines

Links to an external site.

Course Outcomes 

This discussion enables the student to meet the following course outcomes:

Evaluate selected statistical methods for the purposes of critiquing research to complement the critical appraisal of evidence. (POs 3, 5, 9)

Analyze research and non-research data for the purposes of critical appraisal and judgment of evidence for translation into practice.

Week 6, Discussion

Class, you may begin posting in this discussion for credit on Sunday, which begins Week 6.

LAUNCH POST – Using Non-Parametric Statistics

Welcome to our Week 6 Discussion! Please read the question carefully to answer all components. Our interactive discussion addresses the following course outcomes:

1. Evaluate selected statistical methods for critiquing research to complement the critical appraisal of evidence. (POs 3, 5, 9)

2. Analyze research and non-research data for critical appraisal and judgment of evidence for translation into practice. (POs 1, 3, 5, 7, 9)

3. Justify translation science theory when designing, implementing, and evaluating a practice change project. (POs 3, 4, 5, 6, 8, 9)

4. Formulate an emerging practice question focusing on the evidence-based intervention to influence practice outcomes. (POs 1, 3, 4, 5, 6, 7, 9)

5. Synthesize high-level research and non-research evidence relevant to practice problems. (POs 1, 3, 5, 9)

This week we explore inferential statistics: non-parametric statistics.

Non-parametric statistical tests describe how statistics derived from observations on samples from study populations can be used to deduce whether those populations are different. Many statistical tests can be used for this purpose; that test is used depends on the type of data being analyzed and the number of groups involved.

Each non-parametric statistical test includes assumptions about the data and the research design.

As we explore non-parametric assumptions this week, it is essential to remember that statistical assumptions must be met before Ph.D. nurse researchers conduct any statistical analysis.I look forward to our discussion.