Data Cleaning Workflow for Health Studies
2026-05-03 • 7 min
Quick Answer
Data cleaning in health research requires standardized coding, missing-data strategy, outlier review, and reproducible audit trails. A structured pipeline improves model stability, prevents analytical errors, and ensures that published findings reflect true patterns instead of preventable data-quality artifacts.
Build a Data Dictionary First
Define variable names, units, allowed values, and transformations before analysis. This reduces ambiguity across collaborators.
Missingness and Outliers
Profile missingness mechanisms and identify implausible observations using domain-aware thresholds rather than automatic deletion.
Auditability and Reproducibility
Maintain versioned scripts and cleaning logs. Reproducible pipelines are easier to validate during peer review and regulatory checks.
Frequently Asked Questions
Is deleting missing records acceptable?
Only when missingness is minimal and plausibly random; otherwise consider imputation or model-based handling.
Why keep a cleaning log?
Logs preserve transparency, support QA review, and allow exact replication of analytical datasets.