DNP 830 Compare four common types of data errors and identify a method for cleaning each error

Grand Canyon University DNP 830 Compare four common types of data errors and identify a method for cleaning each error-Step-By-Step Guide

This guide will demonstrate how to complete the Grand Canyon University DNP 830 Compare four common types of data errors and identify a method for cleaning each error  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 Compare four common types of data errors and identify a method for cleaning each error                                                                   

Whether one passes or fails an academic assignment such as the Grand Canyon University DNP 830 Compare four common types of data errors and identify a method for cleaning each error  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 Compare four common types of data errors and identify a method for cleaning each error                                                                 

The introduction for the Grand Canyon University DNP 830 Compare four common types of data errors and identify a method for cleaning each error 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 Compare four common types of data errors and identify a method for cleaning each error                                                                 

After the introduction, move into the main part of the DNP 830 Compare four common types of data errors and identify a method for cleaning each error  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 Compare four common types of data errors and identify a method for cleaning each error                                                                 

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 Compare four common types of data errors and identify a method for cleaning each error                                                                   

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 Compare four common types of data errors and identify a method for cleaning each error

DNP 830 Topic 2 DQ 2

Nursing research is extensive and requires researchers to collect massive amounts of data to answer the research question. This data is further analyzed statistically using techniques that vary with the scope and type of the research. Errors are common in data and affect the research’s critical attributes, like validity and reliability (Abu-Bader, 2021). As a result, nursing researchers should be aware of these errors, their sources, and cleaning methods.

A common error during data collection that hampers data quality is wrong entries. A suitable example in this category is a lexical error, such as classifying a participant’s health status as their age. The other potential data-related error is missing data/attributes. Their impact is huge since they have the potential to reduce a study’s statistical power and make the researcher make invalid conclusions, that is, misinterpret the data (Aboumatar et al., 2021). Missing data represent omissions. The third common error is data duplication. Here, an entry is repeated and compromises the analysis. The fourth common error is outliers. Safaei et al. (2020) described outliers as extreme values deviating from other data points. Outliers have huge impacts on the statistical analysis since they skew results.

Data cleaning is crucial in research and DNP projects to avoid skewed analysis. The process involves fixing or removing erroneous data (Kotronoulas et al., 2023). Wrong entries can be fixed as structural errors. They are irrelevant information that should be removed from the dataset or corrected since they do not fit into the analyzed variables. If the value of the missing data is low, the researcher can overlook the data. The other intervention is to forget the variable if it is not too critical to the analysis. Duplicates are cleansed through de-duplication, which involves deleting the repeated entries. Outliers should be thoroughly checked to determine whether they are erroneous. Next, the unwanted outliers should be filtered or removed if it is irrelevant for analysis.

References

Aboumatar, H., Thompson, C., Garcia-Morales, E., Gurses, A. P., Naqibuddin, M., Saunders, J., Kim, S. W., & AWise, R. (2021). Perspective on reducing errors in research. Contemporary Clinical Trials Communications, 23, 100838. https://doi.org/10.1016/j.conctc.2021.100838

Abu-Bader, S. H. (2021). Using statistical methods in social science research: With a complete SPSS guide. Oxford University Press, USA.

Kotronoulas, G., Miguel, S., Dowling, M., Fernández-Ortega, P., Colomer-Lahiguera, S., Bağçivan, G., … & Papadopoulou, C. (2023). An overview of the fundamentals of data management, analysis, and interpretation in quantitative research.  Seminars in Oncology Nursing, 39 (2). https://doi.org/10.1016/j.soncn.2023.151398.

Safaei, M., Asadi, S., Driss, M., Boulila, W., Alsaeedi, A., Chizari, H., … & Safaei, M. (2020). A systematic literature review on outlier detection in wireless sensor networks. Symmetry, 12(3), 328. https://doi.org/10.3390/sym12030328