RSCH FPX 7864 Assessment 1: Quantitative Analysis Plan
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Assessment Overview:
RSCH FPX 7864 Assessment 1: course shifts from general exploration to the specific mechanics of data analysis. As a capella nursing professional, your task is to develop a robust quantitative analysis plan. This involves opting for applicable statistical tests similar to t-tests, ANOVA, or chi-square—and, most importantly, applying ethical principles to the running and interpretation of numerical data.
The thing about this assessment is to demonstrate that you can not only calculate figures but also ensure those figures represent a veracious, unprejudiced, and morally sound clinical narrative.
How to Pass RSCH FPX 7864 Assessment 1: Quantitative Analysis Plan
- Define Variables Clearly identify your independent variable (the cause) and dependent variable (the effect) and specify their level of measurement (nominal, ordinal, interval, or ratio).
- Select the Correct Statistical Test: Choose a test that fits your variables—use an independent-samples t-test for two groups, ANOVA for three or more, or Pearson’s r for continuous relationships.
- State Your Hypotheses: Clearly write out both the Null Hypothesis (H0) and the Alternative Hypothesis (H1) to establish a logical framework for your analysis.
- Prioritize Data Integrity: Explicitly discuss how applying ethical principles means reporting all results, even if they are not statistically significant (p>0.05).
- Address Outliers Ethically: Explain your criteria for identifying outliers and justify why you would keep or remove them without biasing the study’s outcome.
- Verify Statistical Assumptions: Describe how you will test for normality (e.g., Shapiro-Wilk) and homogeneity of variance (Levene’s Test) before running your main analysis.
- Detail the SPSS Workflow: Provide a step-by-step narrative of the software commands you would use (e.g., Analyze > Compare Means > Independent-Samples T-Test) to show technical competency.
- Commit to Privacy: Explain how you will protect patient data during analysis, such as using de-identified datasets or a “data honest broker” to comply with HIPAA.
- Plan for Missing Data: Describe a transparent method for handling missing values (like listwise deletion) to avoid the ethical pitfall of “data cleaning” to force a specific result.
- Use Scholarly References: Support your plan with at least five credible sources, including the APA Ethics Code, the Belmont Report, and a recognized statistics manual.
Sample Assessment:
RSCH FPX 7864 Assessment 1:Applying Ethical Principles in Quantitative Analysis
Introduction
Quantitative research provides the backbone of evidence-based nursing. However, numbers can be misleading if not handled with integrity. This paper outlines a statistical analysis plan for a study evaluating the effectiveness of a new sepsis bundle on patient recovery times. A critical component of this plan is applying ethical principles to ensure that data management is transparent and results are generalizable.
Problem Statement and Research Question
The clinical problem is the high mortality rate associated with late-stage sepsis detection. The research question is: “Is there a significant difference in recovery time (days) between patients treated with the standard sepsis protocol versus the enhanced digital alert protocol?”
Selection of Statistical Test
Because the independent variable (protocol type) is categorical with two groups, and the dependent variable (recovery time) is continuous (ratio level), an independent-samples t-test is the most appropriate statistical method. This test allows the researcher to determine if the mean difference between the two groups is statistically significant or due to chance.
Applying Ethical Principles to Data Management
Ethical research involves more than just getting IRB approval; it requires ethical behavior during the analysis phase.
- Data Integrity: Applying ethical principles means reporting all findings, even if they do not support the hypothesis. Suppressing non-significant results is a form of scientific misconduct.
- Handling Outliers: While extreme values can skew a t-test, they cannot be deleted simply to achieve significance. Any outlier removal must be justified and documented to maintain the principle of beneficence.
- Privacy in Large Datasets: When analyzing patient records, the researcher must ensure that “Statistical Disclosure Control” is used to prevent the re-identification of individuals within the aggregate data.
Assumptions of the Quantitative Plan
Before running the analysis, four assumptions must be met:
- Independence: Observations must be independent of one another.
- Normality: The dependent variable should be approximately normally distributed.
- Homogeneity of Variance: The variance among the groups should be equal (tested via Levene’s Test).
- Absence of Outliers: Significant outliers can disproportionately influence the mean.
Step-by-Step SPSS Procedure
To execute this analysis ethically and accurately:
- Input the de-identified dataset into SPSS.
- Navigate to Analyze → Descriptive Statistics → Explore to check for normality and outliers.
- Select Analyze → Compare Means → Independent-Samples T-Test.
- Define the grouping variable (Protocol A vs. B) and the test variable (Days to Recovery).
- Interpret the p-value. If $p < 0.05$, the null hypothesis is rejected.
Conclusion
By applying ethical principles to the quantitative analysis plan, the nurse-researcher ensures that the resulting evidence is trustworthy. This integrity is essential for translating research findings into clinical policies that truly improve patient outcomes.
References (APA 7 Format)
- American Nurses Association. (2015). Code of ethics for nurses with interpretive statements. https://www.nursingworld.org/coe-view-only
- Department of Health and Human Services. (1979). The Belmont Report. https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/index.html
- Field, A. (2018). Discovering statistics using IBM SPSS statistics. SAGE Publications. https://www.sagepub.com/en-us/nam/discovering-statistics-using-ibm-spss-statistics/book254924
- Pallant, J. (2020). SPSS survival manual: A step-by-step guide to data analysis using IBM SPSS. https://www.routledge.com/SPSS-Survival-Manual-A-step-by-step-guide-to-data-analysis-using-IBM-SPSS/Pallant/p/book/9781760875534
- World Medical Association. (2013). Declaration of Helsinki. https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-principles-for-medical-research-involving-human-subjects/
Rubric Breakdown
| Criteria | Proficient (Pass) | Distinguished (Excellence) |
| Statistical Method | Identifies a statistical test suitable for the data. | Justifies the test choice based on variable levels (Nominal, Ordinal, Interval/Ratio). |
| Applying Ethical Principles | Lists basic ethical considerations for data. | Analyzes the ethical implications of data cleaning, outlier removal, and bias mitigation. |
| Data Assumptions | Mentions assumptions like normality or homogeneity. | Evaluates how violating these assumptions impacts the validity of clinical conclusions. |
| Software Application | Describes using SPSS or similar tools for analysis. | Provides a detailed walkthrough of the analytical steps within the software environment. |
Step-by-Step Guide
- Identify Your Variables: Easily define your Independent Variable (IV) and Dependent Variable (DV).
- Check data situations Is your data categorical or nonstop? This determines whether you use a KI forecourt or a correlation.
- Address the Ethics of Data: Focus on how you’ll handle missing data without “p-hacking” or manipulating results.
- Draft the “How-To” for Analysis: Describe the exact way you would take in SPSS (e.g., Dissect> Compare Means> Independent-Samples T-Test).
Frequently Asked Questions
Q: What if my data doesn’t meet the assumption of normality?
You should use a non-parametric alternative, such as the Mann-Whitney U Test, which does not require a normal distribution.
Q: Is “Applying Ethical Principles” only for the consent phase?
No. In RSCH FPX 7864, ethics apply to how you handle numbers, avoid bias in reporting, and ensure the privacy of electronic health data.
Q: Do I need to include actual SPSS screenshots?
Usually, describing the steps is sufficient, but adding screenshots of your output (with de-identified data) often helps reach the “Distinguished” level.
Integrity Note
Note: Only use this assessment example for learning and structure purpose. Do not submit as your own work.
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