Stata Data Cleaning

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

Loading and Inspecting Data

* ============================================
* Data Cleaning Script: [Project Name]
* Author: [Name]
* Date: [Date]
* Input: raw/survey_data_raw.dta
* Output: processed/survey_data_clean.dta
* ============================================

clear all
set more off
log using "logs/cleaning_log.smcl", replace

* Load raw data
use "raw/survey_data_raw.dta", clear

* Basic inspection
describe
summarize
codebook, compact

* Check dimensions
display "Observations: " _N
display "Variables: " c(k)

* Check for duplicates on ID variable
duplicates report respondent_id
duplicates list respondent_id if duplicates(respondent_id) > 0

Data Quality Report

* Generate a data quality summary
foreach var of varlist _all {
    quietly {
        count if missing(`var')
        local nmiss = r(N)
        local pctmiss = (`nmiss' / _N) * 100
    }
    if `pctmiss' > 0 {
        display "`var': `nmiss' missing (`pctmiss'%)"
    }
}

* Check value ranges for numeric variables
foreach var of varlist age income years_education {
    summarize `var', detail
    * Flag implausible values
    count if `var' < 0 & !missing(`var')
    count if `var' > 150 & !missing(`var')
}

String Cleaning

Standardizing Text Variables

* Trim whitespace
replace name = strtrim(name)
replace name = stritrim(name)  // Remove internal multiple spaces

* Standardize case
replace city = proper(city)        // Title case
replace country = upper(country)   // Upper case
replace email = lower(email)       // Lower case

* Remove special characters
replace phone = ustrregexra(phone, "[^0-9]", "")

* Fix encoding issues
replace name = ustrfix(name)

* Standardize common variations
replace department = "Computer Science" if ///
    inlist(department, "CS", "Comp Sci", "Comp. Sci.", "CompSci")

replace gender = "Female" if inlist(gender, "F", "f", "female", "FEMALE")
replace gender = "Male" if inlist(gender, "M", "m", "male", "MALE")

Parsing Complex Strings

* Split full name into first and last
gen first_name = word(full_name, 1)
gen last_name = word(full_name, -1)

* Extract year from date string "March 15, 2024"
gen year = real(word(date_string, -1))

* Parse numeric values from strings like "$1,234.56"
gen income_clean = real(subinstr(subinstr(income_str, "$", "", .), ",", "", .))

Missing Data Handling

Identifying Missing Data Patterns

* Install missing data analysis tools
ssc install mdesc
ssc install misstable

* Summary of missing data
mdesc

* Missing data patterns
misstable summarize
misstable patterns

* Create missing indicator variables
foreach var of varlist income education occupation {
    gen mi_`var' = missing(`var')
}

* Test whether missing is random (Little's MCAR test approximation)
* Compare means of observed variables by missing status
foreach var of varlist income education {
    ttest age, by(mi_`var')
    ttest gender_numeric, by(mi_`var')
}

Recoding Missing Values

* Common survey codes for missing
* -99 = refused, -88 = don't know, -77 = not applicable
foreach var of varlist income satisfaction trust_score {
    replace `var' = .r if `var' == -99  // .r = refused
    replace `var' = .d if `var' == -88  // .d = don't know
    replace `var' = .n if `var' == -77  // .n = not applicable
}

* Extended missing values preserve the reason for missingness
* while still being treated as missing in analyses

Variable Construction

Recoding and Categorization

* Create age groups
recode age (18/29 = 1 "18-29") (30/44 = 2 "30-44") ///
           (45/59 = 3 "45-59") (60/max = 4 "60+"), gen(age_group)

* Create binary indicator
gen high_income = (income > 75000) if !missing(income)

* Create composite scale (e.g., Likert items)
alpha item1 item2 item3 item4 item5, gen(scale_score) item
* Cronbach's alpha is reported; scale_score is the mean

* Standardize continuous variables
foreach var of varlist income education_years age {
    egen z_`var' = std(`var')
}

* Winsorize extreme values
winsor2 income, cuts(1 99) replace

Date Variables

* Parse date strings
gen interview_date = date(date_string, "MDY")
format interview_date %td

* Extract components
gen interview_year = year(interview_date)
gen interview_month = month(interview_date)
gen interview_dow = dow(interview_date)  // 0=Sunday

* Calculate durations
gen days_since_treatment = interview_date - treatment_date
gen months_since = (interview_date - treatment_date) / 30.44

Data Validation

Assertion-Based Validation

* These assertions halt execution if violated
assert _N == 5000  // Expected sample size
assert !missing(respondent_id)  // No missing IDs
assert age >= 18 & age <= 120 if !missing(age)  // Plausible age range
assert inlist(gender, "Male", "Female", "Other", "") | missing(gender)

* Cross-variable consistency checks
assert education_years >= 0 if !missing(education_years)
assert income >= 0 if !missing(income)
assert end_date >= start_date if !missing(end_date) & !missing(start_date)

Duplicate Detection and Resolution

* Identify duplicates
duplicates tag respondent_id, gen(dup_flag)
list respondent_id survey_date if dup_flag > 0, sepby(respondent_id)

* Keep most recent observation per respondent
bysort respondent_id (survey_date): keep if _n == _N

* Or keep first observation
bysort respondent_id (survey_date): keep if _n == 1

Saving and Documentation

* Label all variables
label variable age "Age at time of interview (years)"
label variable income "Annual household income (USD)"
label variable education_years "Total years of formal education"

* Save cleaned dataset
compress  // Reduce file size
save "processed/survey_data_clean.dta", replace

* Export codebook
codebook, compact
describe, short

* Close log
log close

Best Practices

1. Never modify raw data files: Always read raw data and write to a separate processed file.
2. Log everything: Use log using to capture all output for audit trails.
3. Use assert statements: Validate assumptions about the data at each stage.
4. Document decisions: Comment every recode, drop, or imputation with the rationale.
5. Version your cleaning scripts: Use git to track changes to .do files.
6. Produce a data dictionary: Label every variable and value label in the final dataset.

References

• Stata Data Management Reference Manual: https://www.stata.com/manuals/d.pdf
• DIME Analytics Data Management Wiki: https://dimewiki.worldbank.org/Data_Management
• J-PAL Research Resources: https://www.povertyactionlab.org/resource/data-cleaning
• Long, J.S. (2009), The Workflow of Data Analysis Using Stata, Stata Press