04 Dec DNP Biostatistics Final Project

Project Overview
This final project requires DNP students to apply advanced biostatistical methods to evaluate a clinical quality-improvement (QI) or evidence-based practice (EBP) intervention. Students must select a real or simulated dataset, apply descriptive and inferential statistics, test statistical assumptions, interpret findings, and translate results into clinical, organizational, and population-health implications.

Project Title
Applying Biostatistical Methods to Evaluate a Clinical Quality-Improvement or Evidence-Based Practice Intervention

1. Introduction (1–1.5 pages)
The introduction should describe the significance of the chosen clinical problem, the population affected, and the rationale for the intervention. Students must include a clear purpose statement, research question(s), and null and alternative hypotheses. Independent, dependent, and potential confounding variables should be identified.

2. Methods (2 pages)
A. Study Design

Students must specify the type of design used (e.g., quasi-experimental, retrospective cohort, pre/post design) and justify why the design is appropriate.

B. Sample

Describe the sample size, demographic characteristics, and sampling method. Indicate whether the dataset is real or simulated.

C. Variables

Provide operational definitions for all variables and state their measurement level (nominal, ordinal, interval, ratio).

D. Statistical Plan

Students must specify the descriptive statistics, assumption testing (normality, outliers, homogeneity), and inferential tests selected (2–3 tests required). Approved tests include: – Paired t-test – Independent t-test – One-way or repeated-measures ANOVA – Chi-square test – Pearson or Spearman correlation – Linear regression (simple or multiple) – Logistic regression

Effect size reporting is required (Cohen’s d, eta-squared, odds ratios, R²).

3. Data Analysis (Tables + Figures Required)
Students must conduct descriptive and inferential statistics and create the following:

A. Descriptive Statistics Table – Mean, median, SD, range, IQR (if non-normal) B. Output for each inferential test – Test statistic (t, F, χ², r, β) – Degrees of freedom – p-value – Effect size – 95% confidence interval C. Visualizations (choose at least two): – Histogram (normality) – Boxplot (group comparison) – Line graph (pre/post changes) – Scatterplot with regression line

4. Results (1–1.5 pages)
Results should be presented objectively without interpretation. Students must reference tables and figures properly. Statistical results must be reported clearly with numerical precision.

5. Discussion (2–3 pages)
The discussion must interpret statistical findings and explain their clinical significance. Students should compare findings with evidence in the literature (minimum of 5 peer-reviewed sources), identify at least five limitations, and discuss implications for DNP practice, including patient outcomes, healthcare quality, policy considerations, and population health. Recommendations for sustainability and future evaluation must be included.

6. Conclusion (1 page)
Summarize key findings, clinical relevance, and the DNP role in applying statistical evidence to practice.

Final Deliverable Requirements
• 10–15 pages, APA 7th edition format • Word document (.docx) • At least 5 peer-reviewed references • Tables and figures embedded • Statistical outputs included (screenshots allowed)

Learning Outcomes Assessed
• Ability to apply advanced biostatistical methods • Interpretation of complex statistical data • Translation of statistical findings into actionable clinical insights • Demonstration of doctoral-level writing and communication • Use of evidence-based decision-making for improved healthcare outcomes