DNP 830 Working With Inferential Statistics
Grand Canyon University DNP 830 Working With Inferential Statistics – Step-By-Step Guide
This guide will demonstrate how to complete the Grand Canyon University DNP 830 Working With Inferential Statistics 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 DNP 830 Working With Inferential Statistics
Whether one passes or fails an academic assignment such as the Grand Canyon University NUR 550 Benchmark – Evidence-Based Practice Project: Literature Review 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 DNP 830 Working With Inferential Statistics
The introduction for the Grand Canyon University DNP 830 Working With Inferential Statistics 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.
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How to Write the Body for DNP 830 Working With Inferential Statistics
After the introduction, move into the main part of the DNP 830 Working With Inferential Statistics 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 DNP 830 Working With Inferential Statistics
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 DNP 830 Working With Inferential Statistics
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.
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Sample Answer for DNP 830 Working With Inferential Statistics
Analysis of collected data is key in determining the impacts of an intervention in a project. One of the approaches used is inferential statistics. In inferential statistics, various analytical tools are used to help draw conclusions by examining random samples in a population. The analysis also offers further and finer details about the sampled data, such as the relationship between variables that exist in a dataset (Oh &Pyrczak, 2023). Therefore, inferential statistics help in making generalizations regarding a population by using various approaches, such as t-tests and analysis of variance (ANOVA), among other statistical approaches. As such, the purpose of this assignment is to use the SPSS program to analyze specific tests for a given data set and communicate the results.
Mean, Standard Deviation, and Range
The data on the number of discharges by year was analyzed. The mean of the discharges in 2019 is 720,811.89. This analysis shows an increase in the number of discharges as compared to 2017 and 2018. The analysis also showed that the standard deviation from the calculated mean is 823,479.26. The standard deviation value is not deviating a lot from the population (Mishra et al., 2019). The implication is that the recorded discharges from the states do not vary by wider margins. The analysis also shows that the range of the discharge summaries recorded in the same year is 3,770,020.
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ANOVA
The number of discharges recorded in the year 2019 is of significance since it shows that each state had increased cases of discharge. It was also important to include data from the previous years and the years after to show trends in the number of discharges and enable comparisons. For example, it is conceivable that there was a further variance in 2020. The enhanced rates of discharge are a reflection of measures put in place in the previous year to help affect the rates of discharge. As shown in the appendix, the variance between groups and within groups of the other years with the data collected in 2019 is significant. The western and mid-western states have a small variance, while southern and northeastern states have a fairly large variance. However, the variance recorded in all these groups is significant because the p<0,000, which is significant for all the states (Amrhein et al., 2019).
T-Test
Another analysis performed is the t-test. The paired sampled t-test shows a bivariate Pearson correlation coefficient that gives hypothesized results. The paired samples between 2012 and 2019 have a t= 0.493. The implication is that there are significantly more discharges in 2019 as compared to 2012. The paired sampled t-test assumes that the mean of the patients discharged in 2012 and 2019 are not the same (Stapor, 2020). Therefore, the mean of patients discharged in 2012 is low, while the mean of those discharged in 2019 is high. As such, the analysis is an indication that there was an increase in the number of patients discharged in the year 2019 as compared to 2012. Another insight that can be obtained from the analysis is that while the number of discharges recorded in the year 2012 was significantly lower, this value steadily increased as the year progressed.
Conclusion
The analysis has been performed on the discharge summaries of the provided data. T-tests and ANOVA have both been performed on the data to help generalize the discharge summaries recorded for the year 2019. In addition, it was also noted that the number of discharges recorded in 2019 was significantly higher as compared to those in 2012. The number of discharges also steadily increased from 2012.
References
Amrhein, V., Trafimow, D., & Greenland, S. (2019). Inferential statistics as descriptive statistics: There is no replication crisis if we don’t expect replication. The American Statistician, 73(sup1), 262-270. https://doi.org/10.1080/00031305.2018.1543137
Mishra, P., Pandey, C. M., Singh, U., Gupta, A., Sahu, C., & Keshri, A. (2019). Descriptive statistics and normality tests for statistical data. Annals of Cardiac Anaesthesia, 22(1), 67. https://doi.org/10.4103%2Faca.ACA_157_18
Oh, D. M., & Pyrczak, F. (2023). Making sense of statistics: A conceptual overview.
Stapor, K. (2020). Descriptive and inferential statistics. In Introduction to Probabilistic and Statistical Methods with Examples in R (pp. 63-131). Springer, Cham. DOI: 10.1007/978-3-030-45799-0_2
DNP 830 Topic 4 DQ 1 Sample Answer
Power analysis is used to determine if a sample size is adequate to evaluate project outcomes prior to initiating any project. This is also called a priori. Justify the project sample size for your DPI Project by using the sample size calculator to conduct a power analysis. Discuss how your sample size may affect the validity of your project. Determine the sampling method used in your project. Compare the convenience sample to other sampling methods. Provide evidence supporting your response.
KRISTEN
The direct immersion project (DPI) will examine the impact that electronic medication monitoring (EEM) has on medication adherence in veterans with chronic obstructive pulmonary disease (COPD). The goal of the quality improvement project is to increase medication adherence in people with COPD. The participants of the DPI project will come from a pulmonary veteran clinic and have been diagnosed with COPD. Power analyses are performed to establish a minimum level of participants to obtain statical significance (Sylvia, 2018). Power analyses help to bring validity to results and outcomes. A sample calculator was used to determine the ideal sample size for the DPI project. A power analysis was conducted to establish the minimum range of participants needed for the DPI project. The pulmonary clinic currently has 48 patients on the inhaled medication regimen. With a confidence level of 95, a population size of 48, and a margin of error of 5%, the ideal sample size is 43 (Qualtrics, 2020). All eligible patients will be able to participate. The sample size could impact on the project’s validity.
A convenience sample will be used. A convenience sample is when participants are available to the researcher (Sylvia, 2018). A convenience sample is not explicitly selected by the researcher (Sylvia, 2018). The sample will be people who fit the criteria and are available while attending pulmonary appointments. A convenience sample will be used over randomized because this is a quality improvement project and is not research. The purpose of the DPI project is to improve the outcomes of participants with COPD. Adherence to inhaled medication regain is essential to the health of COPD patients.
The sampling method that will be used impacts the outcome of any quality improvement or research project. Additionally, the correct performance of a power analysis is vital to quality improvement and research project results and outcomes. It is essential for the doctoral-prepared nurse needs to process a solid foundation on power analysis. The sample impact multiple aspects of the DPI project. A set approach to sample selection and data analysis needs to be fully developed prior to the implementation of the DPI project.
References
Qualtrics. (2020, May 21). Calculating sample size: A quick guide (calculator included). https://www.qualtrics.com/blog/calculating-sample-size/
Sylvia, M. L. (2018). Clinical analytics and data management for the DNP (2nd ed.). Springer Publishing Company.