Python

Metaforms' Data Validation module helps QA and data processing teams verify that survey data matches the questionnaire design. Rather than manually writing and running validation scripts, the platform uses AI to automatically generate checks that catch programming errors, data mismatches, and logic failures — at scale and with far less room for human error.

Important: Data Validation focuses on data correctness checks, not data cleaning. It verifies whether the survey was programmed correctly and whether the collected data is consistent with the questionnaire. Features like identifying speeders, straight-liners, or nonsensical open-ended responses are part of the product roadmap and are not included in the current release.

Data Validation with Python uses an uploaded .SAV file (SPSS format) containing both the data and metadata from the programmed survey. Scripts are generated and executed directly within the platform, and you can export a validation report.

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The 4-step workflow

1. Setting Up a Data Validation Project — Create the project, upload your questionnaire and SAV file, and let the AI generate the validation scripts.

2. Understanding the Interface — Get familiar with the Checks and Respondents views.

3. Reviewing and Editing Validation Scripts — Review the AI-generated logic, mark scripts as reviewed, and customise where needed.

4. Running Checks and Reviewing Results — Run all checks against the dataset, drill into flagged respondents, and export the validation report.

For a summary of the question types, sub-scenarios, and validation checks supported today (and what's on the roadmap), see Validation coverage.