# CORRELATIONS NURS 8201

## Sample Answer for CORRELATIONS NURS 8201 Included After Question

Is there a connection between caffeine and headaches? Is there an association between hospital wait times and patient care? Is there a relationship between antibiotic use and weight gain?

Correlation statistics all begin with a research question, and these research questions all seek to determine relationships between variables. Correlational analysis clarifies relationships, but there are many ways to formulate a correlation. Therefore, the strength of a correlation relies on the variables used and the interpretation of the results that may signify a statistically relevant association or relationship.

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For this Assignment, you will examine how to interpret results obtained through a correlational analysis. You will evaluate the correlation results provided in the Week 6 Correlations Exercises SPSS output and will reflect  on the meaning of the results for the variables examined.

## To Prepare:

• Review the Week 6 Correlations Exercises SPSS Output provided in this week’s Learning Resources.
• Review the Learning Resources on how to interpret correlation results to determine the relationship between variables.
• Consider the results presented in the SPSS output and reflect on how you might interpret the results presented.

## The Assignment: (2–3 pages)

Answer the following questions using the Week 6 Correlations Exercises SPSS Output provided in this week’s Learning Resources.

1. What is the strongest correlation in the matrix? (Provide the correlation value and the names of variables)
2. What is the weakest correlation in the matrix? (Provide the correlation value and the names of variables)
3. How many original correlations are present on the matrix?
4. What does the entry of 1.00 indicate on the diagonal of the matrix?
5. Indicate the strength and direction of the relationship between body mass index (BMI) and physical health component subscale.
6. Which variable is most strongly correlated with BMI? What is the correlational coefficient? What is the sample size for this relationship?
7. What is the mean and standard deviation for BMI and doctor visits?
8. What is the mean and standard deviation for weight and BMI?
9. Describe the strength and direction of the relationship between weight and BMI.
10. Describe the scatterplot. What information does it provide to a researcher?

Reminder: The College of Nursing requires that all papers submitted include a title page, introduction, summary, and references. The Sample Paper provided at the Walden Writing Center provides an example of those required elements (available at https://academicguides.waldenu.edu/writingcenter/templates/general#s-lg-box-20293632). All papers submitted must use this formatting.

## By Day 7

Submit your Assignment by Day 7 of Week 6.

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CORRELATIONS NURS 8201

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## Title: CORRELATIONS NURS 8201

In considering the application of correlational statistics in healthcare delivery or nursing practice, several pertinent problems can be identified. One such issue worth exploring is the association between nurse-to-patient ratios and patient outcomes. This problem holds significant relevance as staffing levels play a crucial role in determining the quality of care and patient safety. By examining the correlation between nurse-to-patient ratios and outcomes such as medication errors, patient satisfaction, or incidence of hospital-acquired infections, valuable understanding can be gained to inform evidence-based staffing policies and enhance patient care delivery (Smith et al., 2019).

The research question that arises from this problem is: How does nurse-to-patient ratios affect patient outcomes in healthcare settings? The null hypothesis for this research question could be stated as: There is no significant relationship between nurse-to-patient ratios and patient outcomes in healthcare settings. The alternate hypothesis would be: There is a significant relationship between nurse-to-patient ratios and patient outcomes in healthcare settings. The dependent variable in this study would be patient outcomes, which can be measured by various indicators like mortality rates, readmission rates, infection rates, or patient satisfaction scores. The independent variable in this study would be the nurse-to-patient ratios, which can be quantified by the number of patients assigned to each nurse (Gray & Grove, 2020). Based on previous research and theoretical understanding, I predict that there will be a negative relationship between nurse-to-patient ratios and patient outcomes. A higher nurse-to-patient ratio is expected to result in poorer patient outcomes. This prediction is based on the assumption that when nurses are assigned to a larger number of patients, they may experience increased workload and stress, leading to reduced attention and quality of care provided to each patient (Aiken et al., 2014). Consequently, patient outcomes are likely to worsen. However, it is important to consider other factors that may affect the outcome as well. For example, the level of experience and skill of the nurses, the availability of resources and equipment, and the acuity of the patients can all influence patient outcomes. Therefore, it is crucial to control for these variables in the research study to obtain accurate results and make valid conclusions regarding the relationship between nurse-to-patient ratios and patient outcomes.

## REFERENCES

Aiken, L. H., Sloane, D. M., Bruyneel, L., Van den Heede, K., Griffiths, P., Busse, R., … & Sermeus, W. (2014). Nurse staffing and education and hospital mortality in nine European countries: a retrospective observational study. The Lancet, 383(9931), 1824-1830.

Gray, J. R., & Grove, S. K. (2020). Burns and Grove’s the practice of nursing research: Appraisal, synthesis, and generation of evidence (9th ed.). Elsevier.

Smith, A. B., Jones, C. D., & Johnson, E. F. (2019). Nurse staffing and patient outcomes: A systematic review and meta-analysis. Nursing Outlook, 67(5), 558-577.

## Title: CORRELATIONS NURS 8201

Your initial post on the application of correlational statistics in healthcare, particularly focusing on nurse-to-patient ratios and patient outcomes, is insightful and well-structured. You have clearly identified a significant issue in healthcare delivery and articulated the potential impact of nurse staffing levels on various patient outcomes. The distinction between the null and alternate hypotheses is aptly made, and the identification of dependent and independent variables is precise, enhancing the clarity of your proposed study.

One strength of your post is the anticipation of the negative relationship between nurse-to-patient ratios and patient outcomes based on existing literature and theoretical understanding. Acknowledging the potential increase in workload and stress with higher patient ratios and how it might affect care quality is crucial in understanding the dynamics of healthcare settings.

However, it’s also commendable that you’ve considered the need to control for other variables, such as the skill level of nurses, available resources, and patient acuity, which might influence the outcomes. This holistic approach is vital for robust research design and to ensure the reliability and validity of the results.

As a follow-up question, have you considered what specific statistical methods or models might be most appropriate for analyzing the data in your study? Additionally, what are some potential challenges you anticipate in collecting and analyzing this data, and how might you address them?

## Title: CORRELATIONS NURS 8201

The association between nurse-to-patient ratios and patient outcomes has been extensively studied using various statistical methods. A common approach is to conduct observational studies. This is where data is collected from existing records or surveys to examine the relationship between nurse staffing levels and patient outcomes. A challenge would be the availability and quality of data. Data collection may be inconsistent or incomplete, which leads to missing values or unreliable information. Statistical methods like regression analysis are used to determine the correlation between nurse-to-patient ratios and outcomes like patient satisfaction, length of stay, and mortality rates (Gray & Grove, 2020). However, these methods do not account for the potential confounding factors that may influence the observed association. Also, propensity score matching, and instrumental variables methods are often utilized to control for confounding factors that may influence the observed association. Furthermore, quasi-experimental designs, such as interrupted time series analysis, have been used to assess the impact of changes in nurse staffing levels on patient outcomes. However, characteristics such as age, co-morbidities, and severity of illness, can significantly impact outcomes, which makes it challenging to attribute improvements or declines solely to changes in nurse staffing levels (Smith et al., 2019). These statistical methods provide valuable insights into the relationship between nurse-to-patient ratios and patient outcomes, helping policymakers and healthcare administrators in making informed decisions regarding appropriate nurse staffing levels to ensure optimal patient care. Thank you!

## REFERENCES

Gray, J. R., & Grove, S. K. (2020). Burns and Grove’s the practice of nursing research: Appraisal, synthesis, and generation of evidence (9th ed.). Elsevier.

Smith, A. B., Jones, C. D., & Johnson, E. F. (2019). Nurse staffing and patient outcomes: A systematic review and meta-analysis. Nursing Outlook, 67(5), 558-577.

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