DNP 830 Examining Methods and Designs 

Grand Canyon University DNP 830 Examining Methods and Designs  – Step-By-Step Guide

This guide will demonstrate how to complete the Grand Canyon University DNP 830 Examining Methods and Designs  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 Examining Methods and Designs     

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 Examining Methods and Designs     

The introduction for the Grand Canyon University DNP 830 Examining Methods and Designs 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 DNP 830 Examining Methods and Designs     

After the introduction, move into the main part of the DNP 830 Examining Methods and Designs      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 Examining Methods and Designs     

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 Examining Methods and Designs     

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 Examining Methods and Designs 

Healthcare issues are extensive, and researchers focus on various items at different times. Broadly, research studies aim to explore a concept in-depth to advance knowledge through ideas and theories. Due to their critical role in advancing healthcare, nurses should actively engage in research to improve patient care and address practice problems and other goals. Successful research requires nurses to understand research essentials such as methods, designs, data collection, and statistical analyses. The three articles demonstrate how researchers use different methods and designs. The purpose of this paper is to examine the methods and designs to understand better what they entail and their significance.

Characteristics Associated with the Methods of Each Article

Research methods vary with the studied variables, the aim of the research, and other factors. In the research context, methods are primarily about the tools that researchers use in a study. They could be quantitative, qualitative, or mixed methods. In the first article, Cheng et al. (2018) conducted a quantitative study using observations and focused group interviews. The study’s aim was to explore the impact of a mental health home visit partnership on patient satisfaction. In a quantitative study, researchers examine numerical data and often use statistical tools in data analysis. The other key characteristic of the quantitative method is allowing researchers to measure variables and relationships.

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In the second article, Gilad et al. (2020) conducted qualitative research based on the grounded theory approach. The study examined patients’ and staff’s attitudes and feelings on medication use. Interviews are commonly used in qualitative research. When using the qualitative method, researchers collect non-numerical data and primarily focus on establishing patterns. In the third article, Zimmerman et al. (2020) conducted a mixed-methods study. As the name suggests, mixed-method research combines (mixes) quantitative and qualitative techniques in data collection and analysis. It also allows researchers to explain unexpected results.

Characteristics Associated with the Design of Each Article

Research designs vary with the methods. Generally, a research design is an overall strategy a researcher uses to investigate the research problem thoroughly. Cheng et al. (2018) used a quasi-experimental quantitative design. The main characteristic of this design is demonstrating a causal relationship between an intervention and outcome. Quasi-experiments do not use randomization. In this article, participants were classified into the experimental and control groups. Participants received the partnership intervention in the experimental group, while the control group continued with routine home visits. To examine the causality between intervention and outcomes, researchers measured patient satisfaction pre-intervention, after six months, and after one year. 

Research designs vary in the other articles, further demonstrating how research methods and designs vary depending on the type of study. Gilad et al. (2020) utilized a grounded theory approach using semi-structured interviews. Grounded theory represents the systematic collection and analysis of data to construct a hypothesis. As a result, researchers determine the relationship between the study variables through deductive reasoning. In the other article, Zimmerman et al. (2020) used the descriptive design. In this design, researchers obtain information systematically to describe a phenomenon. This is evident through the use of semi-structured interviews and multiple-choice questions to assess students’ perceptions.

Statistical Analysis Associated with the Method and Design of the Articles

Researchers collect massive amounts of data when analyzing the relationship between variables. Statistical analysis aims to make data meaningful, hence breathing life into it (Grove & Gray, 2022). The articles demonstrate how statistical analysis varies with research methods and designs. In the quasi-experimental quantitative study, Cheng et al. (2018) analyzed data using SPSS Version 20.0. This is a common statistical analysis tool for examining statistical significance. The p-value validates a hypothesis against the collected data. In the qualitative research based on the grounded theory approach, Gilad et al. (2020) analyzed the data using thematic analysis. This systematic approach is common in qualitative research, where researchers identify major themes and relationships between concepts. In the descriptive, mixed-methods study, Zimmerman et al. (2020) subjected the data to inferential and descriptive statistical analysis. Chi-square statistics identified the differences in students’ ability to answer questions correctly. Overall, the statistical analysis demonstrates how different data sets are analyzed to make them more meaningful.

Reliability and Validity Issues Associated with Each Method and Design

Research studies should be highly valid and reliable regardless of their aims and scope. Validity and reliability differentiate credible and bad research. The three articles demonstrate a high regard for internal reliability. A lack of internal reliability implies that procedures do not yield similar results on repeated trials (Grove & Gray, 2022). In the first article, Cheng et al. (2018) confirmed that the scale used was a reliable measurement by a Cronbach’s Alpha of 0.93. Gilad et al. (2020) ensured consistency between findings by conducting interviews using three interviewers. Zimmerman et al. (2020) ensured high reliability by using multiple-choice questions to achieve scores consistency. Studies that lack validity cannot be generalized since results deviate from the truth. To avoid this issue, researchers in the three articles ensured content validity through different techniques. Cheng et al. (2018) used questions with high content validity indexes, while Gilad et al. (2020) used questions with alternating topics. Zimmerman et al. (2020) promoted validity by exploring the study material more broadly (mixed methods) in terms of perceptions and feelings.

Summary of the Articles’ Methods and Designs

Different healthcare concepts are studied differently. Cheng et al. (2018) conducted a quasi-experimental quantitative design with an experimental and control group. The main characteristic of this approach is the lack of randomization. Gilad et al. (2020) conducted a qualitative study using the grounded theory approach. In such studies, data is collected to construct a hypothesis. In the third article, Zimmerman et al. (2020) conducted a descriptive, mixed-methods study. As a result, they combined quantitative and qualitative techniques to study the relationship between variables.

Conclusion

Nursing research is extensive due to the expansive nature of patients’ needs and healthcare issues. Methodologies also need continuous improvement, necessitating rigorous research. The DNP nurse should be aware of the differences between research methods and designs and their application. A comparative analysis of the three articles has demonstrated the differences in research techniques and their suitability. It has also shown how statistical analysis methods vary with the research design.

References

Cheng, J. F., Huang, X. Y., Lin, M. C., Wang, Y. H., & Yeh, T. P. (2018). A mental health home visit service partnership intervention on improving patients’ satisfaction. Archives of psychiatric nursing, 32(4), 610-616. https://doi.org/10.1016/j.apnu.2018.03.010

Gilad, L., Haviv, Y. S., Cohen-Glickman, I., Chinitz, D., & Cohen, M. J. (2020). Chronic drug treatment among hemodialysis patients: a qualitative study of patients, nursing and medical staff attitudes and approaches. BMC Nephrology, 21(1), 1-9. https://doi.org/10.1186/s12882-020-01900-y

Grove, S. K., & Gray, J. R. (2022). Understanding nursing research e-book: Building an evidence-based practice. Elsevier health sciences.

Zimmerman, P. A. P., Sladdin, I., Shaban, R. Z., Gilbert, J., & Brown, L. (2020). Factors influencing hand hygiene practice of nursing students: a descriptive, mixed-methods study. Nurse Education in Practice, 44, 102746. https://doi.org/10.1016/j.nepr.2020.102746

DNP 830 Benchmark – What Are the Data Saying Sample Answer

What Are the Data Saying? 

Data analysis and interpretation form a critical part of research since it leads to the fulfillment of the project objectives. Through data interpretation, the researchers are capable of fully understanding the results in relation to the research questions and objectives since the raw data may not provide useful insights and meaning in connection to the project. The implication is that the data should carefully be interpreted following the laid down principles to get the full meaning. Upon collecting the raw data, the data is then taken through statistical analysis and then represented in graphs, charts, and percentages as a way of data visualization (Kim et al.,2020). As such, the purpose of this assignment is to carry run appropriate statistics in SPSS, provide the results for the analyzed data, and compose an analysis explaining the procedure used in the analysis of the non-parametric and parametric variables. 

The Statistical Tests Used 

Independent Sample T-test  

This is a parametric test usually applied to explore if two groups or populations have a similar mean based on a particular variable. The independent T-test is used when the data to be analyzed has a continuous dependent variable not related within the groups, a categorical independent variable of at least two groups, while the data should have a normal distribution and must be a random sample (Gerald, 2018). In addition, the data to be analyzed should possess an equal variance across the group with no outliers. Besides, every group must also have at least six study subjects, with each group having an equal number of study participants. As such, in the cases of the data provided, this test was chosen to help in exploring and determining the means between the two groups provided. 

Paired Sample T-test  

This is a statistical test usually used in comparing means from the same data sample to find out if the compared means are significantly different. Therefore, paired sample T-test is usually used in research designs such as control experiments, pre-test and post-test, and experimental designs. It is particularly applied when a researcher needs to make comparisons between two points, measurements, conditions, or matched pair (Afifah et al.,2022). It is worth noting that the tests are not applicable in cases where there are unpaired samples with no normal distribution around the mean and have more than two units. This test has been chosen since there is a need to compare the weight at baseline and the intervention weight with the main focus of determining if there is a significant difference. As such, through the test, it will be possible to determine if there has been a change in weight.  

McNemar  

This is another test that has been applied to the provided data sample. The McNemar test is used as a way of checking the marginal homogeneity of two different dichotomous variables.  As such, it is used for two groups having similar participants and when the data is paired. The data to be analyzed should have an independent variable with two related groups, and the groups to be considered should be mutually exclusive and with a random sample (Pembury Smith & Ruxton, 2020). Therefore, the McNemar test was used in this case for comparing the compliance of the baseline to that of the intervention for the research subjects’ data.  

Chi-square 

This statistical test is usually applied in determining how two variables are associated; hence it is also referred to as the chi-square test of association. While it is key to identifying associations between variables, it cannot be used to draw inferences. For the Chi-Square test to be used, the data under consideration should have two categorical variables at least two categories in every variable (Connelly, 2019). The subjects should also be of a large sample and unrelated. As such, this test was also chosen in this case since there was a need to explore the intervention readmission and baseline readmission associations. 

Wilcoxon Z  

Wilcoxon Z is a statistical test used to compare the means of related samples. As such, it is applied in the analysis of repeated measures without or with intervention. Therefore, this test can be used in cases where there are matched subjects without an intervention and another with an intervention (Kim et al.,2020). For it to be used appropriately, the pair should be from a random sample and also independent of other pairs. This test was chosen to help in comparing the mean ranks of the intervention weight pairs and those of the baseline weight. 

Mann Whitney U 

Mann Whitney U test is a statistical test used when comparing the difference between independent samples that do not possess normal distribution. For the test to be used, all the variables should be in ordinal or continuous scales. In most cases, this test is used in cases when one of the considered parameters does not allow the use of independent sample t-tests (Kim et al.,2020). As such, the statistical test was used as there was a need to evaluate if there was a difference in satisfaction between the intervention and baseline data. 

Parametric and Non-Parametric Tests 

Parametric and non-parametric tests are both used in data tests and analysis. The parametric tests are tests that make the assumption that sample data or population has a normal distribution around the mean. Examples of these tests include paired t-test, 2-sample test, 1-sample t-test, and one-way ANOVA. On the other hand, the non-parametric tests do not consider such an assumption; hence using such an assumption during the analysis may lead to inappropriate interpretations (Orcan, 2020). Some of the non-parametric tests include the 1-sample Wilcoxon test, Mann-Whitney Test, Signed-rank test, and Kruskal Wallis test. The non-parametric test is used in cases when meaningful interpretations can be drawn from the median with data samples not appearing normal and small sample. 

Summary of Results 

Paired Sample T-test  

Analysis was performed on the sample data provided. While the baseline weight mean was found to be 217.5 lbs, with an SD of 53.40, that of the intervention was found to be 178.3 lb, with an SD of 44.88. The results from the SPSS output show that t=7.188, df=29 t(df)=2.05, with a 95% confidence interval. The p-value is 0.000, and p<0.005. Therefore, the difference between the means was statistically significant.  

Independent Sample T-test  

This test shows that the mean weight for the intervention group is 218.3 lb with an SD of 54.8, a p-value of 0.934, and t (28) = 0,084 at a 95% confidence interval. The assumption made is that the variance is equal. The output t=0.084 is lower than 1.074, which is the critical value. Hence the result is insignificant. As such, the sampling variability affects the baseline weight and the intervention weight mean.   

McNemar  

The McNemar test shows that the frequency of events is 30 with a chi-square value of 1.639 and a p-value of 0.007. The implication is that it is statistically significant since it is lower than 0.05. As such, there is a difference between the intervention and baseline compliance is significant.  

Chi-square 

The analysis using Chi-Square shows a chi-square value of 1.639 with 1 df and a p-value of 0.008.  This value is lower than 3.84, which is the critical value. As such, the difference between the intervention and baseline readmissions is significant.  

Wilcoxon Z  

The analysis using Wilcoxon Z shows a mean difference of 11.5, Z=-4.307, and p=.000. The implication is that the mean ranking between baseline weight and intervention weight is statistically significant. 

Mann Whitney U 

The analysis using Mann Whitney U shows 18.8 and 12.2 as the baseline and intervention group mean, respectively. The Mann-Whitney test is 63.0 with a p-value of 0.035, a value lower than 0.05. The implication is that the result is that there is a statistical difference. The mean level of satisfaction is also lower in the intervention group in comparison to the baseline.  

Conclusion 

The SPSS software was used in analyzing the data to give results that can be interpreted to enhance an understanding of the collected data. Various statistical tests were used to obtain the required interpretation. Specifically, they offer appropriate information regarding the efficacy of the used intervention. For example, there was a significant difference in weight when an intervention was used. The groups also displayed a variance in both satisfaction and readmission. 

References 

Afifah, S., Mudzakir, A., & Nandiyanto, A. B. D. (2022). How to calculate paired sample t-test using SPSS software: From step-by-step processing for users to the practical examples in the analysis of the effect of application anti-fire bamboo teaching materials on student learning outcomes. Indonesian Journal of Teaching in Science, 2(1), 81-92. https://doi.org/10.17509/ijotis.v2i1.45895 

Connelly, L. (2019). Chi-square test. Medsurg Nursing, 28(2), 127–127. https://www.proquest.com/openview/04d2ff080887f9111b68eb7490a9630a/1?pq-origsite=gscholar&cbl=30764 

Gerald, B. (2018). A brief review of independent, dependent and one sample t-test. International Journal of Applied Mathematics and Theoretical Physics, 4(2), 50-54. Doi: 10.11648/j.ijamtp.20180402.13 

Kim, M., Mallory, C., & Valerio, T. (2020). Statistics for evidence-based practice in nursing. Jones & Bartlett Publishers. 

Orcan, F. (2020). Parametric or non-parametric: Skewness to test normality for mean comparison. International Journal of Assessment Tools in Education, 7(2), 255–265. https://doi.org/10.21449/ijate.656077 

Pembury Smith, M. Q., & Ruxton, G. D. (2020). Effective use of the McNemar test. Behavioral Ecology and Sociobiology, 74, 1–9. Doi: 10.1007/s00265-020-02916-y 

Appendices 

Table 1  

(Sylvia, 2018) 

Table 2