# NURS 8201 WEEK 6 ASSIGNMENT: CORRELATIONS

## Sample Answer for NURS 8201 WEEK 6 ASSIGNMENT: CORRELATIONSIncluded After Question

### 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-20293632Links to an external site.). All papers submitted must use this formatting.

## Description of the Healthcare Problem

Medication mistakes and nurse staffing are the healthcare issues I choose to explore in my correlation analysis. In the US, medication errors are still a major source of stress for the healthcare system. It is among the main reasons why people die that may be avoided. Finding the root causes of medical errors and putting strong remedies in place to address them have proven difficult. To enhance patient safety and quality care, medication mistakes have been recognized as a quality improvement gap that has to be filled (Gray & Grove, 2020). Healthcare professionals are said to be in violation of the five rights of drug prescription: proper patient, medication, time, dosage, and route. To guarantee a safe medication procedure, advance practice nurses should incorporate the five rights into drug prescriptions. They must guarantee that the prescription contains the appropriate action, form, paperwork, and reaction.

A persistent issue for many healthcare organizations is a lack of staff. An increase in medical mishaps has been linked to a scarcity of nurses. Keeping a very healthy environment that honors the values of nurses is one of the measures for addressing medication errors. Medical errors are seen as areas for quality improvement in a very healthy work environment, and they need to be addressed. Medical errors are important basic indications of high-quality care that healthcare organizations should consider. Healthcare professionals make mistakes when trying to treat the large number of patients. Burnout makes a nurse more likely to make mistakes in both commission and omission. Adequate staffing is advised to prevent such mistakes and make sure that practice obligations do not overwhelm nurses and cause fatigue. “What effect does increasing nurse staffing have on medication errors reported?” is my study question.

## Null Hypothesis and AlternativeHypothesis

The two types of hypotheses used in research are null hypothesis and alternative

hypothesis. The null hypothesis is a general mathematical statement that implies that there is no correlation between the two variables being compared. On the other hand, the alternative hypothesis states that there is a difference between the two variables being compared (Travers etal., 2017). After conducting research the researcher can test, verify or reject the null hypothesis being investigated. The principle of both null and alternative hypotheses is collecting data and evaluating the probability of the collected data proving a null or alternative hypothesis is true or false (Travers et al., 2017). In formulating the two hypotheses of my research on staffing and medication errors I will document the following hypotheses.

The Research Problem: “What effect does increasing nurse staffing have on medication errors reported?

Null hypothesis H1: The number of medication errors reported in healthcare organizations does not decrease with an increase in the number of nurses on staff.

Alternative hypothesis H2: Medication errors recorded in healthcare organizations are decreased when there is an increase in the number of nurses on staff.

## My Expected Relationship on Nurse Staffing and Medication Errors

In my opinion as an advanced practice nurse, there should be a direct link between medical errors and nurse staffing. More hours are spent interacting with patients than any other healthcare worker, with nurses serving as primary care providers. Reasons doctors are important decision makers in therapy even though they may only see patients for 30 to 60 minutes a day.Their capacity to view patients’ symptoms and medical state is so limited. Nurses play a variety of functions, such as keeping an eye on patients’ symptoms, identifying medication errors, providing patient care, and carrying out additional tasks to guarantee better patient outcomes (Driscoll et al., 2018).

Nurses are constantly by their patients’ bedsides, arranging their care and keeping in touch with the patients, their families, and the doctors. It stands to reason that more nurses will be able to deliver safe care that is marked by fewer prescription errors. There wouldn’t be many nurses who couldn’t comfortably service an expanding number of nurses. They’ll be under pressure, suffer from severe burnout, and have an excessive workload. Regarding the two hypotheses, I anticipate that the null hypothesis—that is, the idea that “increasing nurse staffing does not reduce the number of medication errors reported in healthcare organizations”—will be disproved. I anticipate that the data’s p-value will be below the test’s significant level. As a result, the null hypothesis will be rejected by the researcher, suggesting that the research findings are meaningful.

When the null hypothesis is rejected, the alternative theory is accepted. By hiring more nurse practitioners, the healthcare facility can ensure that each patient will have enough time spent with a nurse practitioner. They’ll be able to spot patients who could make medication mistakes and take action to prevent them before they have a chance to affect them. Increasing the staffing ratio will also have an effect on the amount of contact hours, which is a contributing factor to medical errors and nursing burnout (Rachel & Francesco, 2018). The nurse will have enough time to calm down before starting their shifts again when there are more providers on duty.

## My Expected Relationship on Nurse Staffing and Medication Errors

The number of nurses on staff is just one aspect that influences patient safety and high-quality care. Patient outcomes may be impacted by other factors. One element that may have an impact on the standard of care is nursing expertise. The quality of care is closely tied to the nursing training and abilities that nurses possess. Low mortality and medical error rates have been linked to well-educated healthcare professionals (Rachel & Francesco, 2018). The quality of treatment provided can also be influenced by the educational backgrounds of nurses. The conditions in which nurses work are another influence. A very well-being work atmosphere has been linked to an improvement in the quality of care, whereas unfavorable working circumstances have been linked to higher workloads, nurse burnout, and subpar patient outcomes. More evidence-based study is required by nursing researchers to ascertain the relationship between medical errors and sufficient staffing.

## References

Driscoll, A., Grant, M. J., Carroll, D., Dalton, S., Deaton, C., Jones, I., … & Astin, F. (2018). The

effect of nurse-to-patient ratios on nurse-sensitive patient outcomes in acute specialist

units: a systematic review and meta-analysis.European Journal of Cardiovascular Nursing,17(1), 6-22.

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.

Rachel, H., & Francesco, S. (2018). Factors associated with and impact of burnout in nursing and

residential home care workers for the elderly. Acta Bio Medica: Atenei Parmensis,89(Suppl 7), 60.

Randolph, C. (2020). Remember the null hypothesis? Journal of Neurology, Neurosurgery & Psychiatry,91(6), 571-571.

Travers, J. C., Cook, B. G., & Cook, L. (2017). Null hypothesis significance testing and p-values.Learning Disabilities Research & Practice,32 (4), 208-215

## A Sample Answer For the Assignment: NURS 8201 WEEK 6 ASSIGNMENT: CORRELATIONS

### Title: NURS 8201 WEEK 6 ASSIGNMENT: CORRELATIONS

Correlational studies or research plays a crucial role in helping researchers gain insight into how particular study variables are related. Through correlational statistics or studies, individuals get to know the strength of a correlation between the variables, and through careful interpretation, a researcher can have an idea if there is a statistically relevant relationship or association (Janse et al.,2021). Therefore, the purpose of this assignment is to explore how to interpret results obtained through a correlational analysis. As such, a correlation SPSS output will be evaluated, and various questions will answered.

## The Strongest Correlation In the Matrix

In the provided output, the strongest correlation is between Body Mass Index and weight pounds. It is evident that the Pearson correlation coefficient for the relationship between BMI and Weight-pounds is 0.937. It is important to note that this relationship is significant as a two-tailored significance has been pegged at 0.01 (Makowski et al.,2020).

## The Weakest Correlation In the Matrix

It is also important to explore the weakest correlation in the matrix. From the output, the weakest correlation is the correlation between the Body Mass Index and SF12: Mental Health Component score, standardized. The correlation value is -0.078, which indicates a weak correlation.

## The Number of Original Correlations In the Matrix

From the provided output, there are a total of nine correlations. The correlation includes Number of doctor visits, past 12 months and Body Mass Index, Number of doctor visits, past 12 months, and SF12: physical health component score. The next is the Number of doctor visits, past 12 months, and SF12: Mental Health Component Score, standardized; the BMI and SF12: Physical Health Component Score standardized, and Body Mass Index and Weight-pounds. The next correlations are BMI and Weight, SF12: Physical Health Component Score, standardized, and SF12: Mental Health Component Score, standardized. The other includes SF12:Physical Health Component Score, standardized and Body Mass Index, SF12: Mental Health Component Score, standardized, and Number of doctor visits, past 12 months.

## What the Entry of 1.00 Indicates on the Diagonal of the Matrix

The entry of 1.00 on the diagonal matrix indicates that each variable is in perfect correlation with itself (Pandey, 2020). It is easily observable as it is indicated from the top left to the bottom right of the main diagonal.

## The Strength and Direction of The Relationship Between BMI and Physical Health

### Component Subscale

The strength of the correlation between body mass index and the physical health component subscale is -0.134. In terms of direction, it is negative, which implies that when the BMI increases, the physical health component subscale decreases. It implies that the two variables are inversely related. In addition, it shows a weak relationship.

## The Variable That Is Most Strongly Correlated With BMI, Coefficient, and Sample Size

From the SPSS output, the variable that is most strongly correlated with Body Mass Index is the Weigh-pounds. The correlational coefficient between the two variables is 0.937. In addition, the sample size for the relationship between Body Mass Index and Weight-pounds is 970. The correlation indicates a very strong positive relationship. The direction is positive, which shows that when the Body Mass Index is high, there is a substantial increase in the weight in pounds. In addition, the strong positive correlation is an indication that a positive and close connection exists between weight in pounds and body mass index.

## The Mean and Standard Deviation for BMI and Doctor Visits

From the output, the mean for Body Mass Index is 29.222, with a standard deviation of 7.379. In addition, the mean for the Number of Doctor Visits in the past 12 months is 6.80, with a standard deviation of 12.720.

## The Mean and Standard Deviation for Weight and BMI

From the provided output, the mean for BMI is 29.22, with a standard deviation of 7.38. besides, the mean of weight-pounds is 171.462, with a standard deviation of 7.38.

## The Strength and Direction of the Relationship Between Weight and BMI

The relationship between weight and BMI is positive and very strong, as the correlation coefficient is 0.937. The positive sign is an indication that when BMI increases, the weight also increases notably.

## Description of Scatterplot and the Information It Provides to the Researcher

Scatterplots are applied to help show the connection between variables. The scatterplot provided in the output displays a relationship between weight and Body Mass Index. The dots in the scatter plot show particular data points, and they can be used to determine patterns. In instances where the horizontal values are given, it becomes easier to predict the vertical value (Ali & Younas, 2021). In the output offered, the distribution of the scatter plots is concentrated in one region. Besides, the distance between the dots is negligible. There is a positive correlation between the variables. There is also a BMI outlier point, which shows that weight may have a higher effect on BMI.

## Conclusion

This assignment has entailed an exploration of an SPSS output showing correlational analysis. Therefore, various aspects have been explored, including mean, standard deviation, and the magnitude of the relationships. In addition, the direction of relationships has also been explored and discussed.

## References

Ali, P., & Younas, A. (2021). Understanding and interpreting regression analysis. Evidence-Based Nursing. https://doi.org/10.1136/ebnurs-2021-103425

Janse, R. J., Hoekstra, T., Jager, K. J., Zoccali, C., Tripepi, G., Dekker, F. W., & van Diepen, M. (2021). Conducting correlation analysis: important limitations and pitfalls. Clinical Kidney Journal14(11), 2332-2337. https://doi.org/10.1093/ckj/sfab085

Makowski, D., Ben-Shachar, M. S., Patil, I., & Lüdecke, D. (2020). Methods and algorithms for correlation analysis in R. Journal of Open Source Software5(51), 2306. https://joss.theoj.org/papers/10.21105/joss.02306.pdf

Pandey, S. (2020). Principles of correlation and regression analysis. Journal of the Practice of Cardiovascular Sciences6(1), 7-11. Doi: 10.4103/jpcs.jpcs_2_20