Workflow
How to Work With Multilingual Codebooks and Dataset Labels
A translated codebook and a partially translated dataset can quickly fall out of sync. Once that happens, researchers start wondering which wording is authoritative.
The cleanest pattern is to keep the translated labels in the data and use codebooks for notes, definitions, and supporting context.
Where Drift Starts
- A codebook is updated but the dataset labels are not.
- Multiple translated documents circulate with slightly different wording.
- Repeated variables use different terms in the workbook and the analysis file.
A Better Documentation Model
- Translate labels inside the dataset or workbook output.
- Use codebooks for notes and definitions rather than duplicated label maintenance.
- Keep one authoritative translated label version for the team.
Why This Helps
A clearer division of labor between datasets and codebooks reduces drift and makes multilingual projects easier to maintain.
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FAQ
Do codebooks still matter?
Yes. They remain useful for notes, definitions, and contextual explanations.
Can translated datasets reduce drift?
Often yes, because they keep the authoritative labels inside the analysis file.
Is documentation still needed?
Yes. Cleaner labels help, but documentation still matters.
Preview Your Own Dataset
Upload a survey file and preview translated labels before rebuilding a drifting multilingual codebook by hand.
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