Week 4: Results Section Writing Assignment 4

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Week 4 Assignment

For this assignment you will be using the Regression file. You will use this file again for next week’s assignment. The file contains several variables:

• Subjno = Subject number
• Timedrs = Number of visits to health professionals (DV)
• Phyheal = Number of physical health symptoms (IV)
• Menheal = Number of mental health symptoms (IV)
• Stress = Stressful life events score (IV)

For this week’s assignment, we are only focusing on two variables (timedrs and stress)

1. Outliers

As with all data, it is important to examine potential univariate and bivariate outliers. Use procedures described in earlier assignments to examine outliers and to get to know your data. Be sure to include:

• Z-score analysis
• Box plot visualization
• Tests of normality

If you see potential outliers, describe them and how you would handle them (i.e., keep, delete or transform) but do NOT do this as I would like everyone to work with the same data set.

Before conducting any analyses, examine the data for potential outliers and ensure it meets key assumptions for regression. Follow these steps:

Univariate Outliers: Use z-scores and boxplots to identify any outliers in timedrs and stress.

• Identify values with z-scores > ±3.3
• Are the distributions approximately normal?
• Discuss whether these values appear to be true outliers and how you would handle them (e.g., retain, transform, or exclude) but do not remove any outliers for this assignment

2. Scatterplot Creation

Create a scatterplot with Stress (IV) on x-axis and timedrs (DV) on y-axis.

[Insert scatterplot here]

Label scatterplot: Figure X Scatterplot of {IV} and {DV}

Interpretation: Based on visual inspection of the scatterplot:

• Describe the relationship between [Independent Variable] and [Dependent Variable] appears to be [positive/negative/no clear relationship]
• Comment on the strength of the relationship (e.g., weak/moderate/strong)
• Note any potential outliers or unusual patterns in the data (e.g., clusters, gaps)
• Do the data points follow a linear pattern?

3. Correlation Analysis

Conduct a Pearson correlation analysis between stress and timedrs.

Report results:

A Pearson correlation analysis was conducted to examine the relationship between [Independent Variable] and [Dependent Variable].

Results: r(df) = [correlation coefficient], p = [p-value]

Interpretation:

• Sign: Is the correlation positive or negative?
• Strength: Use standard benchmarks to describe the strength:
  • Weak: 0.1≤r<0.30
  • Moderate: 0.3≤r<0.50
  • Strong: r≥0.5
• Direction: Explain what the relationship means in context

The correlation is [positive/negative], indicating that as [Independent Variable] [increases/decreases], [Dependent Variable] tends to [increase/decrease] as well.

4. Regression Analysis

Conduct a simple linear regression with stress as the independent variable (IV) and timedrs as the dependent variable (DV).

You are trying to predict the number of visits a person takes to the doctor with how much stress they have in their life.

Results Reporting:

1. Significance of Regression:

A simple linear regression was conducted with [Independent Variable] as the independent variable and [Dependent Variable] as the dependent variable.

Report the overall model significance using the F-test results.

1. Model Fit:

Report R² and interpret the percentage of variance explained:

• Model fit: R² = [value], indicating that [percentage]% of the variance in [Dependent Variable] is explained by [Independent Variable].

1. Regression Equation:

Report the regression equation using the intercept and slope coefficients:

[Dependent Variable] = [intercept] + [slope] * [Independent Variable]

Interpret each term:

• The intercept (value) represents the predicted [dependent variable] when [independent variable] is zero
• The slope (value) indicates how much each one-unit increase in [independent variable], the dependent variable is predicted to [increase/decrease] by [value] units

1. Assumption Checks

• Linearity: Confirm linearity was assessed earlier with the scatterplot. Summarize findings here.
• Normality of residuals:
  • Create a histogram or Q-Q plot of residuals
  • Discuss whether residuals are approximately normally distributed

1. Comparison of correlation and regression

• Correlation coefficient (r = [value]):
  • Measures the strength and direction of the relationship between variables, independent of units
  • Standardized measure ranging from -1 to +1
  • Dimensionless and independent of the scale of the variables
• Regression slope (b = [value]):
  • Represents the rate of change in [Dependent Variable] for each one-unit increase in [Independent Variable]
  • Maintains the units of the original variables
  • Depends on the scale of the variables

[Explain the difference between correlation coefficient and regression slope in context of the study]

Limitations

Discuss any limitations of the study (e.g., small sample size, presence of outliers, assumptions not fully met).

Conclusion

Write a paragraph bringing together the findings and writing up the results in APA format. Include:

• The regression equation
• The significance of the model and its R² value
• Key results from the correlation and regression analyses
• Implications of the relationship between stress and doctor visits

__________________________________________________________________________________________________

Assign 2.

NUR801: Advanced Statistics in Nursing Research

Week 4: Bivariate Regression Analysis Assignment

Current Date: September 29, 2025

Dear Students,

As we continue to build your proficiency in statistical analysis for nursing research, this week’s assignment focuses on bivariate linear regression using SPSS. This skill is essential for examining relationships between variables in healthcare settings, such as how workload factors might predict patient outcomes or staff well-being. Drawing from recent 2025 curricula in nursing statistics (e.g., similar assignments in behavioral health contexts like trauma assessment in vulnerable populations), I’ve adapted this exercise to a nursing-specific scenario: exploring how nurse-to-patient ratios influence patient satisfaction scores in acute care settings.

Please download the SPSS Bivariate Regression Assignment_NUR801.docx template (for your write-up) as well as the nurse_ratio.sav data file from the course portal. Allocate the majority of your time to drafting the Results section, as it forms the core of evidence-based reporting in nursing research. Ensure all text, tables, and figures adhere strictly to APA 7th edition guidelines (e.g., double-spaced, 12-pt Times New Roman, 1-inch margins).

Include your full SPSS output (.spv file) as a separate submission, but embed all relevant tables and figures directly into your Word document for seamless review.

Submit via the course portal by 11:59 PM ET on Sunday, October 5, 2025. Late submissions will incur a 10% deduction per day. This assignment is worth 100 points: 20 for data exploration, 30 for analysis and reporting, 30 for interpretation and assumptions, and 20 for overall APA formatting and conclusion.

Assignment Overview

For this exercise, you’ll analyze the nurse_ratio.sav dataset, which simulates data from a 2025 study on staffing in U.S. hospitals (n = 150 nurses across 10 facilities). Key variables include:

• Subjno: Subject (nurse) identification number
• PatSatis: Patient satisfaction score (0-100 scale; DV – higher scores indicate better satisfaction)
• NurseRatio: Nurse-to-patient ratio (e.g., 1:4 = 0.25; IV – lower values indicate higher staffing burden)
• Burnout: Nurse burnout inventory score (IV – not used this week)
• ShiftLen: Average shift length in hours (IV – not used this week)

This week, focus solely on PatSatis (DV) and NurseRatio (IV). Your goal: Predict patient satisfaction based on staffing ratios to inform evidence-based staffing policies in nursing practice. You’ll reuse this file for Week 5’s multiple regression extension.

Step 1: Data Exploration – Outliers and Normality

Before analysis, scrutinize the data for univariate and bivariate outliers to ensure robustness, as per best practices in nursing research (e.g., avoiding biased predictions in patient outcome models). Use techniques from prior weeks:

• Univariate Outliers: Compute z-scores and generate boxplots for PatSatis and NurseRatio. Flag values with |z| > ±3.3.
• Normality Tests: Run Shapiro-Wilk tests and inspect histograms/Q-Q plots. Are distributions approximately normal (p > .05)?
• Discussion: Describe any potential outliers (e.g., extreme staffing ratios in rural vs. urban sites). Propose handling strategies (retain, transform via log, or exclude) in a nursing context (e.g., retaining to reflect real-world variability in understaffed units), but do not alter the dataset – all students must use identical data.

Report in 1-2 paragraphs, including:

• Summary statistics (M, SD, range) for both variables.
• Visuals: Embed boxplots as Figure 1 (e.g., “Figure 1. Boxplots of Patient Satisfaction and Nurse-to-Patient Ratio”).

Step 2: Scatterplot Creation

Generate a scatterplot with NurseRatio (IV) on the x-axis and PatSatis (DV) on the y-axis, including a fitted regression line (Analyze > Regression > Linear > Plots > ZPRED vs. ZRESID for residuals, but focus on main plot here).

[Insert scatterplot here]

APA Labeling: Figure 2. Scatterplot of Nurse-to-Patient Ratio and Patient Satisfaction Scores.

Interpretation (2-3 sentences):

• Describe the relationship: Does it appear positive, negative, or nonlinear? (E.g., “A negative trend suggests that poorer staffing ratios correlate with lower satisfaction.”)
• Assess strength (e.g., tight clustering = strong; scattered = weak).
• Note patterns: Any clusters (e.g., by shift type), gaps, or outliers? Confirm linearity for regression assumptions.

Step 3: Correlation Analysis

Run a Pearson’s r correlation between NurseRatio and PatSatis (Analyze > Correlate > Bivariate; two-tailed, α = .05).

Reporting Template: A Pearson correlation was conducted to assess the bivariate relationship between nurse-to-patient ratio and patient satisfaction scores.

Results: r(df) = [value], p = [value].

Interpretation (3-4 sentences):

• Sign and Direction: Positive/negative? (E.g., “The negative correlation indicates that as nurse-to-patient ratios worsen [higher values], patient satisfaction tends to decrease.”)
• Strength: Classify using Cohen’s (1988) guidelines: weak (|r| < .30), moderate (.30-.50), or strong (>.50).
• Contextual Meaning: Relate to nursing (e.g., “This aligns with 2025 ANA guidelines on safe staffing to enhance patient-centered care.”).

Step 4: Simple Linear Regression

Conduct a simple linear regression predicting PatSatis from NurseRatio (Analyze > Regression > Linear; Method: Enter; Statistics: Estimates, Confidence Intervals, Model Fit; Plots: ZPRED vs. ZRESID for homoscedasticity; Save: Unstandardized residuals).

Research Question: To what extent does nurse staffing burden predict patient satisfaction in hospital settings?

Reporting Template: A simple linear regression was conducted to examine whether nurse-to-patient ratio predicts patient satisfaction scores. Assumptions of linearity, independence, homoscedasticity, and normality were preliminarily assessed (detailed below).

• Model Significance: The overall model was [significant/not significant], F(df) = [value], p = [value].
• Model Fit: R² = [value], indicating that [X]% of the variance in patient satisfaction is explained by nurse-to-patient ratio. Adjusted R² = [value] accounts for sample size.
• Regression Equation: Patient Satisfaction = [intercept] + [slope] × Nurse-to-Patient Ratio.
  • Intercept Interpretation: When the nurse-to-patient ratio is 0 (ideal staffing), predicted satisfaction is [value] points.
  • Slope Interpretation: For each one-unit increase in nurse-to-patient ratio (e.g., from 1:4 to 1:5), satisfaction is predicted to [increase/decrease] by [value] points, holding other factors constant.

Embed the ANOVA and Coefficients tables as Table 1 (e.g., “Table 1. Simple Linear Regression Results Predicting Patient Satisfaction from Nurse-to-Patient Ratio”).

Step 5: Assumption Checks

Verify regression assumptions to ensure valid inferences for nursing policy recommendations:

• Linearity: Reference scatterplot findings (e.g., “Visual inspection confirmed a linear pattern.”).
• Homoscedasticity: Inspect ZPRED vs. ZRESID scatterplot – residuals should show no funnel pattern.
• Normality of Residuals: Generate a histogram or Q-Q plot of unstandardized residuals. Discuss fit (e.g., “Residuals approximate normality, supporting parametric assumptions.”).
• Independence: Note no serial correlation expected in cross-sectional data.

Embed visuals as Figure 3 (e.g., “Figure 3. Q-Q Plot of Standardized Residuals”). If violations occur, discuss implications (e.g., “Mild heteroscedasticity may slightly inflate Type I error; robust methods could be explored in future analyses.”).

Step 6: Comparing Correlation and Regression

Correlation Coefficient (r = [value]): A standardized, unitless measure of linear association strength/direction (-1 to +1).

Regression Slope (b = [value]): The unstandardized change in DV per unit change in IV, retaining original units (e.g., satisfaction points per ratio unit).

Contextual Explanation (1 paragraph): In this study, r quantifies the overall staffing-satisfaction link independent of scale, while b provides actionable predictions (e.g., “A b of -15 suggests that improving ratios by 0.1 could boost satisfaction by 1.5 points, informing shift scheduling.”). Note how r² approximates R².

Step 7: Limitations

Discuss 2-3 limitations in 1 paragraph (e.g., “Cross-sectional design limits causality; unmeasured confounders like patient acuity may influence results. Outliers from high-acuity units were retained to preserve ecological validity but could skew estimates.”).

Step 8: Conclusion

Synthesize in a cohesive APA-style paragraph (150-200 words):

• Restate the regression equation and model significance/R².
• Highlight key correlation/regression findings.
• Implications for nursing practice (e.g., “These results underscore the need for optimal staffing to enhance satisfaction, aligning with 2025 Joint Commission standards.”).
• End with broader research suggestions (e.g., longitudinal studies).

References

Batiha, A.-M. (2025). Evaluating nurse-to-patient ratio legislation to improve patient safety and care quality: A mixed-methods policy study. Applied Nursing Research, 83, Article 151989. https://doi.org/10.1016/j.apnr.2025.151989

Hong, K. J., & Cho, S. H. (2021). Associations between nurse staffing levels, patient experience, and hospital rating. Healthcare (Basel), 9(4), 387. https://doi.org/10.3390/healthcare9040387

Kim, Y., Lee, K., & Jung, M. (2024). Improvement in nurse staffing ratios according to policy changes: A prospective cohort study. BMC Nursing, 23, 335. https://doi.org/10.1186/s12912-024-01995-w

Phillips, J., Malliaris, A. P., & Bakerjian, D. (2021). Nursing and patient safety. PSNet [internet]. Rockville (MD): Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services.

Grading Rubric Highlights

• Exploration (20 pts): Thorough outlier/normality discussion with visuals.
• Analysis/Reporting (30 pts): Accurate SPSS results, embedded tables/figures.
• Interpretation/Assumptions (30 pts): Insightful, contextually relevant explanations.
• Formatting/Conclusion (20 pts): Flawless APA; strong synthesis.

Review the Week 4 lecture slides and APA Results template on the portal. Office hours are available Thursday 2-4 PM ET for SPSS troubleshooting. I’m excited to see your analyses – this directly applies to your capstone projects!

Best regards, Dr. [Your Name] Professor, Nursing Research Methods [Your University] College of Nursing

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