stata-data-cleaning

• SKILL.mdcore · 8,599 bytes

# Stata Data Cleaning

Clean, transform, and validate messy research datasets in Stata. This skill covers the complete data preparation pipeline from raw survey or administrative data to analysis-ready datasets, with emphasis on documentation, reproducibility, and handling the common data quality issues encountered in social science, economics, and health research.

## Overview

Data cleaning typically consumes 60-80% of research time in empirical studies, yet it is often under-documented and poorly reproducible. Stata provides a powerful set of commands for data manipulation, but knowing which commands to use and in what order requires experience with common data quality issues: inconsistent coding, duplicate observations, string formatting problems, implausible values, and complex missing data patterns.

This skill provides a systematic, step-by-step data cleaning workflow in Stata. Each step produces a log of changes made, enabling full reproducibility and audit trails. The workflow is organized around the principle that raw data should never be modified in place -- instead, cleaning scripts transform raw data into processed datasets while preserving the original.

The approach follows best practices from the World Bank's DIME Analytics team and the J-PAL research transparency guidelines, making it suitable for projects that require rigorous data documentation for peer review, replication packages, or regulatory compliance.

## Initial Data Assessment