Project 3 Submission Is An APA Style Paper

Project 3 Submission Is An Apa Style Paper There Is Not A Page Limit

Develop a data analysis plan for answering your research question, including both nonparametric and parametric approaches. Write a short project report with the following components:

Section 1: Research Question (10 Points)—state your research question, explain why it is important to you, and discuss what it could contribute to nursing.

Section 2: A Nonparametric Plan (15 points)—identify the independent and dependent variables with their specific levels of measurement (Nominal or Ordinal); state your null hypothesis and research hypothesis; describe the statistical analyses with rationale.

Section 3: A Parametric Plan (15 points)—identify the independent and dependent variables with their specific levels of measurement (Interval/Ratio); state your null hypothesis and research hypothesis; describe the statistical analyses with rationale.

The research question is provided in the attached PICO format and must be used. Additional supporting material from Sections 1 and 2 of this project is also attached to inform your plan if needed. Your paper must be formatted according to APA style, including a cover page and references. An APA template is provided.

Ensure your submission is close to an A+ quality, with thoroughness, clarity, and proper grammar and spelling.

Paper For Above instruction

The purpose of this paper is to develop a comprehensive data analysis plan to answer a specific research question within the domain of nursing. To achieve this, the plan incorporates both nonparametric and parametric statistical methods to ensure robustness and applicability. The research question, already provided in the PICO format, serves as the foundation for this analysis. Here, I will elucidate the research question, justify its importance, and outline detailed analytical strategies for both approaches.

Section 1: Research Question

The research question addressed in this project is: "Does the implementation of a new nurse-led intervention reduce patient readmission rates within 30 days compared to standard care?" This question stands at the intersection of healthcare quality improvement and patient safety, making it highly relevant to nursing practice. It aims to identify effective strategies to enhance patient outcomes, which is crucial for evidence-based nursing interventions. The question is important to me because improving patient outcomes directly aligns with patient-centered care principles, and understanding the efficacy of specific interventions can shape nursing policies and practices.

Furthermore, this research can contribute to the nursing field by providing empirical data on intervention effectiveness, guiding resource allocation, and informing best practices in discharge planning and follow-up care. It also aligns with ongoing efforts to reduce hospital readmissions, a key indicator of healthcare quality, and supports nursing's role in health promotion and disease prevention.

Section 2: A Nonparametric Plan

In the nonparametric analytical approach, the independent variable is the type of care received, with two levels: intervention (nurse-led intervention) and control (standard care). This variable is nominal because it categorizes groups without inherent order. The dependent variable is the patient's readmission status within 30 days, which is also nominal—either "readmitted" or "not readmitted."

The null hypothesis (H0) for the nonparametric analysis states that there is no difference in readmission rates between the intervention and control groups. Conversely, the research hypothesis (H1) posits that the nurse-led intervention significantly reduces readmission rates compared to standard care.

The appropriate statistical test for this analysis is the Chi-square test of independence. This test evaluates whether the distribution of readmitted versus not readmitted patients differs significantly between the two groups. The rationale for choosing the Chi-square test lies in its suitability for categorical data, and it does not assume normality of the data, making it ideal for nominal variables with independent groups.

Section 3: A Parametric Plan

In the parametric approach, the independent variable remains the type of care—intervention vs. standard care—but now measured at the interval or ratio level. For instance, if the dependent variable is the number of days until readmission or a continuous score assessing readmission risk, these would be interval/ratio data. For this case, assuming the outcome is the number of days until readmission, the variables are as follows:

• Independent variable: Type of care (intervention or control), measured at the nominal level
• Dependent variable: Days until readmission, a ratio level measurement

The null hypothesis (H0) posits that there is no difference in the mean number of days until readmission between groups. The research hypothesis (H1) suggests that the nurse-led intervention leads to a statistically significant difference in the mean days until readmission.

To analyze this data, an independent samples t-test is appropriate, as it compares the means of two independent groups on a continuous outcome. The rationale for choosing this parametric test hinges on the data meeting assumptions of normality and homogeneity of variance. If these assumptions are violated, a nonparametric alternative such as the Mann-Whitney U test would be considered.

In conclusion, employing both nonparametric and parametric statistical analyses allows for comprehensive testing of the research question. The Chi-square test evaluates categorical readmission rates, whereas the t-test or Mann-Whitney U test assesses the impact on continuous measures like days to readmission. Together, these methods ensure robust, valid insights into the effectiveness of nurse-led interventions in reducing readmissions, thereby advancing evidence-based nursing practice.

References

• Polit, D. F., & Beck, C. T. (2020). Nursing research: Generating and assessing evidence for nursing practice (11th ed.). Wolters Kluwer.
• Levin, K. A. (2017). Study design III: Cross-sectional studies. Evidence-Based Dentistry, 18(2), 35-36. https://doi.org/10.1038/sj.ebd.6400469
• McHugh, M. L. (2013). The Chi-square test of independence. Biochemia Medica, 23(2), 143–149. https://doi.org/10.11613/BM.2013.022
• Field, A. (2013). Discovering statistics using IBM SPSS Statistics (4th ed.). Sage Publications.
• Gravetter, F. J., & Wallnau, L. B. (2016). Statistics for the behavioral sciences (10th ed.). Cengage Learning.
• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.
• Ghasemi, A., & Zahediasl, S. (2012). Normality tests for statistical analysis: A guide for non-statisticians. International Journal of Endocrinology and Metabolism, 10(2), 486–489. https://doi.org/10.5812/ijem.3505
• Vogt, W. P. (2011). Quantitative research methods in education and the social sciences. Pearson Education.
• Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd
• Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.