ArticleJournal of advanced nursing2026
A Primer of Data Cleaning in Quantitative Research: Handling Missing Values and Outliers.
Article in Journal of advanced nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Development of an Explainable Machine Learning Model to Predict Mortality Risk in Sepsis Patients: Insights From a Real-World Clinical Data.Shock (Augusta, Ga.) · 2026Article
- Machine learning-based prediction of postoperative continuous renal replacement therapy initiation after open thoracoabdominal aortic repair.BMC nephrology · 2026Article
- Data-driven design of hybrid graphene oxide-MXene polymeric membranes for water purification.iScience · 2026Article
- A Hybrid ISSA-XGBoost Model for Predicting Wellbore Leakage.Sensors (Basel, Switzerland) · 2026Article
- Factors associated with sleep quality in community-dwelling older adults: a latent class analysis.BMC geriatrics · 2026Article
- Examining the Association Between Perceived Quality of Care and Experienced Coercion Among Patients in Acute Mental Health Units in Spain: A National Cross-Sectional Study.Journal of clinical nursing · 2026Article
- Towards precision medicine in Tourette syndrome: a perspective on AI-driven predictive modelling and personalised care.Frontiers in computational neuroscience · 2026Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsThis 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
methodsMethodological discussion.
resultsThis 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.
conclusionProper 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
methodIn this methodological paper, no new data were generated. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.