Assignment: Correlations
Introduction
From the onset, it would be prudent to note that statistics knowledge has been applied to various research fields, namely the health sciences, social sciences, and physical sciences. The current paper seeks to assess the relationship and associations between the variables under investigation. Therefore, the current paper aims at investigating the connection/relationship between the usage of caffeine and headaches. Also, the current paper aims at assessing the association between hospital wait times and patient care. Lastly, this particular paper will also investigate if there is a relationship between antibiotic use and weight gain.
Methodology
This section of the paper aims at providing the procedure of the study. The study employed a quantitative method in analyzing the study’s results through summary statistics and inferential statistical analysis. Descriptive statistics aims at describing the study’s variables in a meaningful and/or simple way/manner (Rio, Tenthorey, & Ramelet, 2021). The analysis will be done on the primary data on caffeine use, headache, hospital wait times, patient care, antibiotic usage and weight gain. Correlation analysis will be used in testing for statistical significance. A correlational design was used to assess the relationship between the study’s variables, use of caffeine, headache, hospital wait times, patient care, antibiotic usage and weight gain (Benton, 2021). Therefore, the current study aims to explore the relationship between caffeine use, headache, hospital wait times, patient care, antibiotic usage and weight gain. In conducting the study’s analysis, all the data sets were first entered into Excel, and later imported into SPSS version 20 for further statistical analysis. An alpha value of 5% was used to evaluate the significance of the results.
Results and Summary
This part of the paper aims to summarize the study’s main findings and discussions highlighted in the research’s objectives under investigation – which aims to summarize the results that seeks to determine the relationship between use of caffeine, headache, hospital wait times, patient care, antibiotic usage and weight gain. The results were analyzed through descriptive statistics, enabling and describing the study’s data and presenting them in a more straightforward and meaningful manner. Inferential statistics aim to infer the population using the sample drawn (Gray and Groove, 2020).
Descriptive Statistics
The table on the descriptive statistics presented on week six outputs summarizes the study’s variables, including doctor’s visits, body weight, physical health, and mental health. The results show that they had means of 6.80,29.22,45.11, and 46.83, respectively. Their standard deviations were 12.72, 7.38,10.84, and 10.81, respectively, which measures how the variables are dispersed from their means.
Statistical Inferences
Correlational Analysis
A primary goal of correlation research is to describe and evaluate the relationships and associations between variables. A correlational research design is a way to investigate relationships between variables without any researcher interference. However, it can disallow researchers efforts to make conclusions about causal links or which variable has more impact on other variables. Instead, correlational research aims to understand and assess the statistical relationship between two variables (Salkind, 2017).
From the correlations table, the strongest correlation in the matrix provided was between the body mass index and weight in pounds (r=0.937, p<0.001), while the weakest correlations in the matrix provided were between the body mass index and the physical health component, (r=-0.078, p=0.022). It should also be noted that from the outputs provided, the original correlations on the matrix were six correlations. The entry of 1.00 indicates a correlation of a variable by itself; if you run a correlation between one variable and itself, you will get 1.00 (Woo & Kim, 2021). The results show that there was a weak significant negative relationship between the body mass index (BMI) and physical health component subscale (r=-0.134, p<0.001). The most strongly correlated with BMI is pounds, with a correlation coefficient of N=970, r=0.937. The means for weight and BMI’s are 171.46 and 29.22, respectively. Their standard deviations are 45.44 and 7.38, respectively. A strong positive relationship exists between weight and BMI (r=0.937, p<0.001).
From the scatterplot presented, the chart depicts a positive relationship between the BMI and the weight, indicating that as the BMI increases, the weight also increases proportionately.
First of all, when presenting a case for evaluation, one looks at the commitment of the government. In the case of Kuwait, in 1986, the government was spending 5.2% of its GNP on health care, and ten years later, in 1996, with more than double the national budget, the government was allocating 2.7% of its GNP on health care (Burney, 1999). Today, according to the World Health Organization, 3% of GNP goes to health-related services. That is outstanding for any nation. The commitment of the Kuwaiti government is also shown in the growth of expenditures: for example, since 1970, the public expenditure on health care has increased nearly 18-fold. Between the years 1970 and 1996, the ratio of population to doctors declined from 958 to 597; in those same years, the ratio of population to nurses shrunk from 262 to 226. Other positives for Kuwait in the health delivery field? 1) The average life expectancy at birth (LEAB): in the aforementioned years, life expectancy increased from 69 years to 74.8 years. 2) The crude death rate (CDR): it declined from 5 per 1,000 population to 2.9 per 1,000 population in the 26 years between 1970 and 1996. 3) The infant mortality rate (IMEL): it declined from 51.7 per 1,000 live births, to 11.4 per 1,000. And the share of communicable diseases resulting in deaths dropped from 33% to 8% in those same years. All those data are courtesy Nadeem A. Burney, et al., in the International social Science Review.
Data supplied by the World Health Organization (WHO) provides dramatic evidence that there are many more “strong points” than “weak points” in Kuwait’s health care system. For example, comparing Kuwait to nearby nations in terms of “government expenditures on health, as a percentage of all money spent on health” – Kuwait’s government spends 87.2% of all health-related spending in the country. Oman is next, with 82.9%; Saudi Arabia is 79.1; Tunisia is 78.2; United Arab Emirates is 72.7; and way down at the bottom of the scale in neighboring nations to Kuwait is Sudan, at 21.2%.
Total government spending on health care, as a percentage of GNP, shows Kuwait at 3%, far below Lebanon’s 11.8%, but one has to factor in the huge amount of money Kuwait has to spend, versus the relatively small GNP in Lebanon. As to immunization of children against diseases, Kuwait would appear to be doing everything correctly. For instance, there were zero reported cases of Diphtheria from 1990 to 2001, according to World Health Organization data, and only 206 cases of measles in that same time period.
Kuwait certainly faced a health care crises when Iraqi forces invaded Kuwait on August 2, 1990. According to a report by the Physicians for Human Rights (PHR) (Brennan, 1992), there were “mass arrests, torture during interrogation, summary executions…and the plundering of Kuwaiti institutions…[and] claims were made that medical care institutions had been abused, specifically that there had been stationing of military personnel [from Iraq] in and around hospitals.” These invading forces, according to PHR, “interfered with the delivery of health care services, assaulted health professions, removed medicines, equipment and supplies from hospitals.” Several doctors told PHR researchers they were prohibited from entering their hospitals by the soldiers. Some health care workers were reported killed for providing emergency medical care to Kuwaitis injured by bombs and gunfire. And many other health care personnel fled the country.
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References
Benton, C. P. (2021). Sexual health attitudes and beliefs among nursing faculty: A correlational study. Nurse Education Today, 98. Doi: 10.1016/j.nedt.2020.104665
Gray, J. R., & Grove, S. K. (2020). Burns and Grove are the practice of nursing research: Appraisal, synthesis, and generation of evidence (9th ed.). Elsevier.
Rio, L., Tenthorey, C., & Ramelet, A.-S. (2021). Unplanned post-discharge healthcare utilization, discharge readiness, and perceived quality of teaching in mothers of neonates hospitalized in a neonatal intensive care unit: A descriptive and correlational study. Australian Critical Care, 34(1), 914. DOI: 10.1016/j.aucc.2020.07.001
Salkind, N. J. (2017).Exploring research(9th ed.). Pearson.
Woo, M.-J., & Kim, D.-H. (2021). Factors associated with secondary traumatic stress among nurses in regional trauma centres in South Korea: A descriptive correlational study. Journal of Emergency Nursing, 47(3), 400411. DOI: 10.1016/j.jen.2020.08.006
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