A Primer of Data Cleaning in Quantitative Research: Handling Missing Values and Outliers

Abstract

Aims

This paper discusses data errors and offers guidance on data cleaning techniques, with a particular focus on handling missing values and outliers in quantitative datasets.

Design and Methods

Methodological discussion.

Results

This paper provides an overview of various techniques for identifying and addressing data anomalies, which can arise from incomplete, noisy, and inconsistent data. These anomalies can significantly affect data quality, leading to biased model parameter estimates and evidence-based decisions. Data cleaning, particularly the appropriate handling of missing values and outliers, is essential to improving data quality before analysis. Data cleaning includes screening for anomalies, diagnosing errors, and applying appropriate corrective measures.

Conclusion

Proper handling of missing values and the identification and correction of outliers are crucial aspects of data cleaning in ensuring data quality and the reliability of statistical analyses. Effective data cleaning enhances the validity and accuracy of research findings for evidence-based decision making that leads to optimal patient outcomes.

Implications for the Profession

The quality of study results depends on how a dataset and its complexities are processed or handled before the analysis. Nursing researchers must use a framework to identify and address important data anomalies and produce reliable results.

Impact

This paper describes data cleaning, often overlooked during the data mining process, as a crucial step before conducting data analysis. By addressing missing values and outliers, identifying and fixing data anomalies, and enhancing data quality prior to analysis, data cleaning techniques can produce precise research findings for evidence-based decision making.

Reporting Method

In this methodological paper, no new data were generated.

Patient or Public Contribution

No patient or public contribution.