Statistical Analyses in Nursing

Grand Canyon University Statistical Analyses in Nursing-Step-By-Step Guide

This guide will demonstrate how to complete the Grand Canyon University Statistical Analyses in Nursing assignment based on general principles of academic writing. Here, we will show you the A, B, Cs of completing an academic paper, irrespective of the instructions. After guiding you through what to do, the guide will leave one or two sample essays at the end to highlight the various sections discussed below.

How to Research and Prepare for Statistical Analyses in Nursing

Whether one passes or fails an academic assignment such as the Grand Canyon University Statistical Analyses in Nursing depends on the preparation done beforehand. The first thing to do once you receive an assignment is to quickly skim through the requirements. Once that is done, start going through the instructions one by one to clearly understand what the instructor wants. The most important thing here is to understand the required format—whether it is APA, MLA, Chicago, etc.

After understanding the requirements of the paper, the next phase is to gather relevant materials. The first place to start the research process is the weekly resources. Go through the resources provided in the instructions to determine which ones fit the assignment. After reviewing the provided resources, use the university library to search for additional resources. After gathering sufficient and necessary resources, you are now ready to start drafting your paper.

How to Write the Introduction for Statistical Analyses in Nursing

The introduction for the Grand Canyon University Statistical Analyses in Nursing is where you tell the instructor what your paper will encompass. In three to four statements, highlight the important points that will form the basis of your paper. Here, you can include statistics to show the importance of the topic you will be discussing. At the end of the introduction, write a clear purpose statement outlining what exactly will be contained in the paper. This statement will start with “The purpose of this paper…” and then proceed to outline the various sections of the instructions.

How to Write the Body for Statistical Analyses in Nursing

After the introduction, move into the main part of the Statistical Analyses in Nursing assignment, which is the body. Given that the paper you will be writing is not experimental, the way you organize the headings and subheadings of your paper is critically important. In some cases, you might have to use more subheadings to properly organize the assignment. The organization will depend on the rubric provided. Carefully examine the rubric, as it will contain all the detailed requirements of the assignment. Sometimes, the rubric will have information that the normal instructions lack.

Another important factor to consider at this point is how to do citations. In-text citations are fundamental as they support the arguments and points you make in the paper. At this point, the resources gathered at the beginning will come in handy. Integrating the ideas of the authors with your own will ensure that you produce a comprehensive paper. Also, follow the given citation format. In most cases, APA 7 is the preferred format for nursing assignments.

How to Write the Conclusion for Statistical Analyses in Nursing

After completing the main sections, write the conclusion of your paper. The conclusion is a summary of the main points you made in your paper. However, you need to rewrite the points and not simply copy and paste them. By restating the points from each subheading, you will provide a nuanced overview of the assignment to the reader.

How to Format the References List for Statistical Analyses in Nursing

The very last part of your paper involves listing the sources used in your paper. These sources should be listed in alphabetical order and double-spaced. Additionally, use a hanging indent for each source that appears in this list. Lastly, only the sources cited within the body of the paper should appear here.

Stuck? Let Us Help You

Completing assignments can sometimes be overwhelming, especially with the multitude of academic and personal responsibilities you may have. If you find yourself stuck or unsure at any point in the process, don’t hesitate to reach out for professional assistance. Our assignment writing services are designed to help you achieve your academic goals with ease.

Our team of experienced writers is well-versed in academic writing and familiar with the specific requirements of the Statistical Analyses in Nursing assignment. We can provide you with personalized support, ensuring your assignment is well-researched, properly formatted, and thoroughly edited. Get a feel of the quality we guarantee – ORDER NOW.

Clinical decision is of utmost importance in the provision of quality health outcomes. Contingent upon this premise, the two articles establish decision-making procedures and practice guidelines relevant for clinical practice. The first study assesses the feasibility of decision-making processes by nurses stationed at the emergency department of a care facility (Fisher, Orkin & Frazer, 2010). On the other hand, the work of Tjia et al. (2010) purposes to develop guidelines required to monitor the dispensation of high-risk medications while at the same time establish the prevalence of existing laboratory testing concerning these medications. In order to draw clinical evidence on a factor in decision making, the article by Fisher, Orkin and Frazer (2010) employed the usage of nonparametric tests comprising Fisher’s exact tests and chi-square.

The study relied on conjoint analysis to reflect upon the decision-making patterns. The results of this study provided quality outcomes by demonstrating that nurses depended on the functional status of patients, future health status, and family input to undertake decisions on healthcare delivery for their clients. The article by Tjia et al. (2010) utilized t-test and Likert-type scale to formulate guidelines for the utilization of high-risk drugs and to monitor the frequency of dispensing them. The non-parametric test was instrumental in developing medication dispensing guidelines in terms of drug classes, the frequency of medication, monitoring and laboratory testing for efficacy.

According to numerous empirical studies, parametric parameters receive useful application in the testing of study group means. Nevertheless, the effectiveness of the methodology remains debatable within the context of the present articles. For instance, the use of t-test and ANOVA requires normal distribution of the applicable data regarding the research. Since data from the two articles were not distributed, it became paramount for the authors to consider skewing of non-normal distribution to produce the results (Gibbons & Chakraborti, 2011). Therefore, the approach remains embedded on assumptions and as such it has a high vulnerability to error. However, the assertion receives higher applicability in the second article. Nonetheless, the application of ANOVA and t-test requires studies that have a broad distribution of sample sizes, a threshold that neither of the two articles met.

Click here to ORDER an A++ paper from our MASTERS and DOCTORATE WRITERS: Statistical Analyses in Nursing

Despite providing results on the clinical decision and high-risk drug dispensing techniques, certain strengths and weakness characterized the studies. The first article used conjoint analysis techniques to design a workable mathematics model required for clinical decision-making process for nurses in the emergency department (Fisher, Orkin & Frazer, 2010). However, the technique involving proxy decision-making for this study is complex considering the premise that it does not uniformly address the responses of all nurses. As such, the study could be subject to speculation hence casting doubt on the accuracy of information obtained from the first study. In the article by Tjia et al. (2010), the selected study design captured a multispecialty population and therefore provided a reflection of clinical practice in the United States of America. However, utilization of the Likert-type scale could subject the study outcomes to errors due to a lack of consensus on the questions administered to participants. Considerably, findings and recommendations in the work of Fisher, Orkin and Frazer (2010) provide the need for aligning clinical decisions as per the patients in the emergency department for purposes of improving the quality of care. Correspondingly, the other article offers guidelines for safe administration of high-risk medications to establish an evidence-based practice in a healthcare setting.

In the entire coursework, the present author discovers nonparametric tests as commonly applied to the processes of analyzing data. Specifically, chi-square dominates most of the literature review in clinical research. Evidently, the adoption of this test has demonstrated effectiveness in the analysis of nominal data. Furthermore, the technique has a high level of accuracy since it has received comparison with observed frequencies obtained from null hypotheses. Nevertheless, the adoption of other nonparametric tests such as the Wilcoxon matched-pairs test, Mann-Whitney U and Kruskal-Wallis tests does not readily occur since they measure rank-ordered data. According to Gibbons and Chakraborti (2011), the application of the above-mentioned non-parametric tests in multifarious clinical studies does not normally occur since outliers have the capacity to obscure the outcomes. Moreover, the outliers have minimal impact on the chi-square tests.

Reference

Fisher, K., Orkin, F., & Frazer, C. (2010). Utilizing conjoint analysis to explicate health care decision making by emergency department nurses: a feasibility study. Applied Nursing Research, 23(1), 30-35.

Gibbons, J. D., & Chakraborti, S. (2011). Nonparametric statistical inference. In International encyclopedia of statistical science (pp. 977-979). Springer, Berlin, Heidelberg.

Tjia, J., Field, T. S., Garber, L. D., Donovan, J. L., Kanaan, A. O., Raebel, M. A., … & Gurwitz, J. H. (2010). Development and pilot testing of guidelines to monitor high-risk medications in the ambulatory setting. The American journal of managed care, 16(7), 489-496.

Clinical decision is of utmost importance to provide quality health outcomes. Based on this premise, the two articles establish decision-making procedures and practice guidelines relevant for clinical practice as illustrated below.

This first study assesses the feasibility of decision-making process by the nurses stationed at the emergency department of a care facility for clients with intellectual disability (Fisher, Orkin & Frazer, 2010). In order to draw clinical evidence on a factor in decision making, this article used nonparametric tests comprising of Fisher’s exact tests and chi-square. The study relied upon conjoint analysis to reflect on the decision-making patterns among nurses in the emergency department. The results of this study provided quality outcomes by indicating that nurses depended on the functional status of patients, future health status and family input to make decisions on healthcare delivery for their clients. Even though parametric tests are utilized to test for study group means, their effectiveness remain debatable in this study. For instance, the use of t-test and ANOVA require that data for the research to undergo normal distribution. Since data from this article was not distributed, it meant that skewing of non-normal distribution was to be taken into account to yield the result (Gibbons & Chakraborti, 2011). Therefore, it implies that the approach is based on assumption and as such is subject to error when used for this article.

Despite providing results on the clinical decision, this study had strengths and weakness. The article used conjoint analysis techniques to design a workable mathematic model required for clinical decision-making process for nurses in the emergency department (Fisher, Orkin & Frazer, 2010). However, the technique involving proxy decision making for this study is complex considering the premise that it does not uniformly address the responses of all nurses. As such, the study could be subject to speculation hence casting doubt on the accuracy of information obtained from the first study. Considerably, findings and recommendations in the first study provide the need for aligning clinical decisions as per the patients in the emergency department to improve quality of care (Fisher, Orkin & Frazer, 2010).

The article purposes to develop guidelines required to monitor the dispensing of high-risk medications while at the same time establish the prevalence of laboratory testing of these medications. T-test and Likert-type scale were used to develop guidelines for the use of high-risk drugs and to monitor the frequency of dispensing (Tjia et al., 2010). The non-parametric test was instrumental in developing medication dispensing guidelines in terms of drug classes, the frequency of medication, monitoring and laboratory testing for efficacy (Gibbons & Chakraborti, 2011). However, the use of ANOVA and t-test requires studies that have a broad distribution of sample sizes which was contrary in this article. Besides, t-test are used to analyze randomized trials hence not applicable for this second study.

In this reading, the study design selected captured a multispecialty population and therefore provided a reflection of clinical practice in the United States of America (Tjia et al., 2010). However, use of the Likert-type scale could subject the study outcomes to errors due to a lack of consensus on the questions administered to participants. Apparently, this article provide guidelines for safe administration of high-risk medications to establish an evidence-based practice in a healthcare setting.

Statistical Analysis Most Frequently used in Research Literature

In the entire coursework, I find nonparametric tests commonly used to analyze data. Specifically, chi-square dominates most of the literature review in clinical research. It is evident that the use of this test is effective in analyzing nominal data. Besides, the technique is accurate as it is usually compared with observed frequencies obtained from null hypothesis. Other nonparametric tests such as the Wilcoxon matched-pairs test, Mann-Whitney U and Kruskal-Wallis test are not readily used because they measure rank-ordered data which is not the case in many clinical research where outliers can complicate the outcomes (Gibbons & Chakraborti, 2011). Moreover, chi-square tests are not readily affected by outliers.

Reference

Gibbons, J. D., & Chakraborti, S. (2011). Nonparametric statistical inference. In International encyclopedia of statistical science (pp. 977-979). Springer, Berlin, Heidelberg.