stata-data-cleaning

What problem does it solve?

Stata researchers often struggle with messy, inconsistent datasets that contain missing-value codes, duplicates, outliers, and poorly labeled variables, which prevents reliable analysis.

Core Features & Use Cases

• Reproducible cleaning pipeline: Generates a Stata do-file structure with setup, logging, and documented transformations so results can be replicated.
• Common data-quality fixes: Handles duplicates, missing value decoding, string normalization, derived variables, and basic integrity checks using assertions.
• Analysis-ready outputs: Produces a validated, labeled dataset plus a codebook for downstream modeling and reporting.
• Use Case: Turn raw survey or administrative data with coded missing values and inconsistent text fields into a clean, analysis-ready dataset suitable for regression or panel construction.

Quick Start

Ask the AI to create a Stata cleaning do-file for my raw dataset raw_survey_data.dta, documenting each transformation, validating assumptions with asserts, and saving cleaned_analysis_data.dta with a codebook.