Statistics service 07
Data Cleaning & Validation
Start every analysis with data you can defend
Audit, clean and validate your dataset before any model is fitted, with a documented trail of every decision made along the way.
- Raw data auditing: missingness patterns, duplicate detection, logic checks
- Missing data handling with multiple imputation (MICE, Amelia) and sensitivity analysis
- Outlier identification using statistical and visual diagnostics
- Standardisation, variable coding, transformation and full cleaning documentation
Software: R (tidyverse, mice), SPSS, Python (pandas)