Stata
How to Translate Stata Value Labels Without Breaking Codes
Stata value labels are where survey categories become readable. The data may store a 3, but the label tells you whether that means little trust, somewhat agree, or not at all satisfied.
That makes value label translation more than a cosmetic cleanup. If the code-to-label mapping drifts, your recodes and interpretation can become wrong without triggering an error.
Why Value Labels Matter
Value labels connect numeric codes to human-readable categories. A translated file should keep the numeric values intact while changing only the readable text.
This is especially important for Likert scales, yes-no variables, and special missing categories such as don't know or refused.
A Reliable Workflow
- Inspect value label definitions with `labelbook` or `codebook`.
- Translate repeated label sets once and reuse them consistently.
- Review special missing categories before translating the rest.
- Preview translated labels before exporting the full file.
- Export a translated `.dta` that preserves codes and label assignments.
What to Avoid
- Do not sort label spreadsheets manually and reattach them later.
- Do not flatten ordinal distinctions such as agree versus strongly agree.
- Do not translate don't know, refused, and missing as if they were interchangeable.
Suggested Internal Links
FAQ
Can I translate Stata value labels without changing numeric codes?
Yes. Proper value label translation changes only the readable category text while keeping the original codes intact.
What happens to categories like 98 and 99?
They should stay attached to their original codes and be translated carefully because don't know, refused, and missing often mean different things.
Does this change the response data?
No. Response data remain untouched. Only the labels attached to coded responses change.
Preview Your Own Dataset
Upload a `.dta` file and preview the first three translated label rows before exporting the full translated dataset.
Upload a dataset