Research Survey Validation and Replication
How validation reports, audit trails, codebooks, and replication bundles make survey research easier to review.
Quick Answer
Research survey validation means showing that the software behaves as expected, that randomization and exports can be checked, and that other researchers can understand how the final analysis file was produced.
Validation evidence
Known-effects tests check whether the analysis path recovers effects that were built into synthetic data. Adversarial tests check whether common workflow failures are caught or documented.
Replication materials
A useful replication bundle contains raw exports, clean analysis files, a codebook, methods notes, scripts, and a record of exclusions or survey versions.
Privacy and retention
Replication should not override privacy obligations. Researchers still need to review identifiers, URL parameters, sensitive fields, retention plans, and access to exported files.
FAQ
Do synthetic respondents use an LLM?
No. Synthetic validation and path checks use deterministic rules, known effects, random seeds, and local scoring code rather than OpenAI or Claude APIs.
What should be in a replication bundle?
Include raw data, clean analysis data, codebook, methods text, exclusion rules, design summaries, and scripts needed to reproduce estimates.