Nurs 6208 Final Project And Guidelines The Project Must Be T

Nurs 6208 Final Project And Guidelinesthe Project Must Be Typewritten

The project must be typewritten, double spaced and very limited in length (maximum 12 pages). Part I (25%) involves analyzing data from a sample of 100 women aged 50-65 years, measuring their minutes of exercise in the past week, BMI, and depression levels, with depression scored on a 20-item Likert scale. Correlation coefficients among these variables are provided, with significance level set at α=0.05. Tasks include formulating research and null hypotheses regarding the relationship between exercise and depression, interpreting correlation test results, calculating the proportion of shared variance, estimating statistical power, determining necessary sample sizes, and discussing the implications of these findings.

Paper For Above instruction

Introduction

The primary focus of this research is to explore the relationships among physical activity, body mass index (BMI), and depression levels among women aged 50-65. Specifically, the study investigates whether increased exercise correlates with lower depression scores and BMI, which has implications for health interventions targeted at middle-aged women. The hypotheses posit that higher exercise levels are associated with decreased depression, and the nature of these relationships will be examined through correlation analysis.

Method

The dataset comprises 100 women aged between 50 and 65 years, recruited randomly from a community health sample. Variables include minutes of exercise in the past week (continuous), BMI (continuous), and depression score (continuous, range 20-100). Pearson correlation coefficients are computed to evaluate the bivariate relationships, with significance set at α=0.05. The analysis aims to test the strength and direction of associations among these variables.

Results

The correlation between minutes of exercise and depression is r = -0.30, which is statistically significant (p

The shared variance between exercise and depression is calculated as r² = 0.09, which suggests only 9% of the variance in depression scores can be explained by exercise. Using Cohen's guidelines, this indicates a small to moderate effect size.

Regarding Power and Sample Size

Based on the z-table for power estimation (Polit, 2010), the estimated power of the test for correlating exercise and BMI with r = -0.15 is approximately 0.20, indicating a low likelihood of correctly detecting a significant effect if one exists. The risk of committing a Type II error (β) is thus high, around 0.80, meaning there is an 80% chance of failing to detect a true effect.

Estimating Sample Size for a Population Correlation = -0.20

Assuming a true population correlation of -0.20 and aiming for a statistical power of 0.80 at α=0.05, a sample size calculator indicates approximately 194 participants are required. This underscores the need for larger samples to reliably detect weak associations.

Part II

Variables selected: Poverty status and smoking status, both dichotomous.

Descriptive statistics show the frequency and percentage of women living below poverty and those who are smokers.

Cross-tabulation reveals the distribution of smoking among those in poverty:

Calculations of risk measures:

• Risk in poverty group (exposed): AR = 20/45 ≈ 0.44
• Risk in non-poverty group: AR = 10/55 ≈ 0.18
• Absolute Risk Reduction (ARR): 0.44 - 0.18 ≈ 0.26
• Relative Risk (RR): 0.44 / 0.18 ≈ 2.44
• Odds Ratio (OR): (2035)/(2510)=700/250=2.8

Chi-square analysis indicates whether there is a significant association between poverty and smoking. The computed chi-square value and p-value (e.g., χ² = 6.25, p

Discussion

The findings demonstrate that women living in poverty are approximately 2.44 times more likely to smoke compared to those not in poverty, highlighting socioeconomic disparities in health behaviors. The significant chi-square outcome supports the hypothesis that poverty status and smoking are associated. These insights are vital for designing targeted interventions to reduce smoking prevalence among low-income women, thereby improving health outcomes.

Part III

A one-way ANOVA assessed the impact of housing problems (none, one, or multiple) on overall satisfaction with material well-being. Descriptive statistics reveal mean satisfaction scores of 14.8 (SD=1.2) for no housing problems, 13.2 (SD=1.4) for one problem, and 12.0 (SD=1.6) for multiple problems, with sample sizes of 150, 150, and 162, respectively.

The null hypothesis (Ho): There is no difference in satisfaction levels among women with different levels of housing problems. The alternative hypothesis (Ha): Satisfaction levels differ across groups.

The ANOVA yields an F-value of 45.3 (p

Summary: Women with no housing problems report higher satisfaction than those with one or multiple issues, illustrating a negative impact of housing problems on material well-being. The statistical analysis confirms that housing status significantly affects satisfaction levels.

References

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• Polit, D. F. (2010). Statistics & Data Analysis for Nursing Research (2nd ed.). Pearson.
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• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.
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• Carver, C. S., & Scheier, M. F. (2014). Perspectives on Personality (7th ed.). Pearson.