DNP 830 Data Errors and Cleaning Methods Essay

DNP 830 Data Errors and Cleaning Methods Essay

DNP 830 Data Errors and Cleaning Methods Essay

DNP 830 Data Errors and Cleaning Methods Sample Essay

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.

ORDER A PLAGIARISM-FREE PAPER HERE ON; DNP 830 Data Errors and Cleaning Methods Essay

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

BUY A CUSTOM-PAPER HERE ON; DNP 830 Data Errors and Cleaning Methods Essay

Topic 2 DQ 1

Assessment Description

Describe the survey, instrument, or tool you plan to use in your DPI Project and explain why this is the best for your project. Describe the tool in terms of name, the number of items, how it is answered (e.g., multiple choice, Likert scale, yes/no, open answer), the total score, and the level of measurement. Describe the reliability and validity of the instrument including the applicable psychometric data. Provide evidence supporting your response

Topic 2 DQ 2

Assessment Description

There are many types of errors that can be found in data. Depending on the source of data, the cleansing process can take a substantial amount of time, and the attention to detail should not be underestimated. After reading Chapter 12 in Clinical Analytics and Data Management for the DNP, compare four common types of data errors and identify a method for cleaning each error. Provide evidence supporting your response.