Abstract
Quantitative data quality assurance is the systematic process and procedures used to ensure the accuracy, consistency, reliability, and integrity of data throughout the research process. Effective quality assurance helps identify and correct errors, reduce biases, and ensure the data meets the standards needed for analysis and reporting. This paper provides an overview of key issues to consider when working with data and reporting findings.
| Original language | English |
|---|---|
| Pages (from-to) | 723-727 |
| Number of pages | 5 |
| Journal | Journal of Psychiatric and Mental Health Nursing |
| Volume | 32 |
| Issue number | 3 |
| Early online date | 19 Dec 2024 |
| DOIs | |
| Publication status | Published (in print/issue) - 30 Jun 2025 |
Bibliographical note
Publisher Copyright:© 2024 The Author(s). Journal of Psychiatric and Mental Health Nursing published by John Wiley & Sons Ltd.
Data Access Statement
The authors have nothing to report.Funding
The authors received no specific funding for this work.
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Quantitative methods
- Hierarchy of evidence
- Data analysis
- data analysis
- data management
- quantitative
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