Recoding Categorical Variables
Learning Outcomes
Outcomes
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Learning Outcomes
- Explain the purpose of data cleaning and preparation in clinical research data management.
- Distinguish between raw data, cleaned data, analysis-ready data, derived variables, and query outputs.
- Describe a reproducible R workflow for importing REDCap exports and preparing datasets for review.
- Explain the role of the REDCap API in automated data export and why API use must be governed carefully.
- Identify and classify missing data using study-specific definitions and documentation.
- Recode categorical variables transparently while preserving traceability to original values.
- Create derived variables in R using protocol-defined rules.
- Write cleaning scripts that are readable, rerunnable, and suitable for review by another data manager.
- Produce simple cleaning logs and outputs that support query management, monitoring, and analysis preparation.