The Purpose of Data Cleaning and Preparation
Learning Outcomes
Outcomes
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Learning Outcomes
- Explain why data cleaning begins before the end of data collection.
- Distinguish data cleaning from broader data preparation activities.
- Differentiate raw data, cleaned data, analysis-ready data, derived variables, query outputs, and cleaning logs.
- Describe why raw exports should be preserved and not manually overwritten.
- Explain how R can support reproducible checks, query listings, and analysis preparation.