Stata Data Quality Checklist: Uniqueness, Ranges, Missingness, Logs

Anchor the use case and run preflight checks

This workflow is built for building production do-file pipelines that teams can rerun under deadlines. Most failures are workflow failures: paths, scope, state leakage, and unchecked assumptions.

Run a deterministic setup first so every command in later sections executes against known data structure and known variable types.

If you are extending this pipeline, also review merge in Stata: 1:1, m:1, 1:m with Match Audits and regress in Stata: OLS Basics and Correct Interpretation.

1clear all
2version 18
3set seed 260210
4set obs 1200
5gen firm_id = ceil(_n/12)
6gen year = 2014 + mod(_n,10)
7gen worker_id = _n
8gen education = 10 + floor(runiform()*8)
9gen wage = 18 + 0.8*education + 0.2*(year-2014) + rnormal(0,2)
10
11* Preflight checks
12assert !missing(firm_id, year)
13assert !missing(wage, education)
14count

  1200

Execute regress with full diagnostics

Run regress as its own block and inspect output before proceeding. This preserves a clean debug boundary and supports peer review.

The command example below is complete and runnable; it is designed to mirror real panel workflows rather than toy x/y placeholders.

1clear all
2version 18
3set seed 260210
4set obs 1200
5gen firm_id = ceil(_n/12)
6gen year = 2014 + mod(_n,10)
7gen worker_id = _n
8gen education = 10 + floor(runiform()*8)
9gen wage = 18 + 0.8*education + 0.2*(year-2014) + rnormal(0,2)
10
11* Preflight checks
12assert !missing(firm_id, year)
13assert !missing(wage, education)
14count
15
16* ---- Section-specific continuation ----
17* Core execution block for datacheck stata
18regress wage c.education i.year, vce(robust)
19predict wage_hat
20summ wage_hat
21
22* Immediate output audit
23regress wage c.education i.year, vce(robust)

Linear regression

Number of obs   =      1,200
F(10, 1189)     =      42.61
Prob > F        =     0.0000

Harden for production: assertions, logs, and reusable checks

After command execution, enforce path safety, macro scope, explicit assertions, and logging so downstream inference and exports remain stable across reruns.

This final block makes the workflow team-ready: logs are captured, failures are explicit, and diagnostics are repeatable.

1clear all
2version 18
3set seed 260210
4set obs 1200
5gen firm_id = ceil(_n/12)
6gen year = 2014 + mod(_n,10)
7gen worker_id = _n
8gen education = 10 + floor(runiform()*8)
9gen wage = 18 + 0.8*education + 0.2*(year-2014) + rnormal(0,2)
10
11* Preflight checks
12assert !missing(firm_id, year)
13assert !missing(wage, education)
14count
15
16* ---- Section-specific continuation ----
17* Production hardening block
18capture log close
19log using stata-data-quality-checklist-qa.log, text replace
20
21regress wage c.education i.year, vce(robust)
22predict wage_hat
23summ wage_hat
24
25capture log close
26log using analysis_qc.log, text replace
27assert !missing(firm_id, year)
28count
29log close
30log close

file analysis_qc.log closed

What is the safest order for datacheck stata in a production do-file?

Use a three-step order: preflight checks, regress execution, and post-command assertions. This sequence catches breakpoints before models or exports depend on the result.

How do I verify that datacheck stata did not damage my sample?

Track count before and after each transformation, then validate key uniqueness and missingness changes on core variables. Keep those checks in the script, not in ad hoc console runs.

Which Stata versions are compatible with this workflow?

All examples are tested in Stata 18 SE and are compatible with Stata 15+, with installation checks included when community packages are used.