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Surveys

Survey Data Quality Checks

Use screeners, attention checks, validation, reCAPTCHA, pilot testing, and exports to review data quality.

Plan Data Quality Before Launch

Data quality is part of survey design. In academic survey software, quality checks should be built into the instrument, flow, publishing process, and export review before live collection begins.

Preview: Respondent View

Run The Quality Pass

  1. Step 1: Review Block Builder. Check wording, required questions, validation rules, attention checks, and any respondent-facing instructions.

    Step 1: Review Block Builder

  2. Step 2: Review Flow. Confirm screeners, experimental paths, quota-full routes, and end screens work as intended.

    Step 2: Review Flow

  3. Step 3: Review Deploy. Check public link settings, quotas, reCAPTCHA, and launch readiness.

    Step 3: Review Deploy

  4. Step 4: Use Preview. Complete the survey with realistic answers for every major path using the pilot testing workflow.

    Step 4: Use Preview

  5. Step 5: Inspect Export. Submit test responses and confirm variables and recodes, conditions, and randomized order fields are present.

    Step 5: Inspect Export

  6. Step 6: Fix and repeat. Re-run the quality pass after any change to wording, Flow, quotas, validation, or response options.

    Step 6: Fix and Repeat

Common Quality Checks

  • Eligibility screeners: keep the target population clear.
  • Required questions: prevent missing values where an answer is essential.
  • Validation rules: keep numeric and text answers in the expected format.
  • Attention checks: identify careless responses when your protocol allows them.
  • reCAPTCHA: reduce automated responses on public survey links.
  • Pilot testing: catch confusing wording and broken paths before launch.

For experimental studies, add manipulation checks and comprehension checks that match the treatment design. See Attention and Manipulation Checks.

Block Builder: Required Toggle

Use Flow And Quotas Carefully

Broken routing can damage data quality as much as bad wording. Preview every path, especially eligibility exits, experimental conditions, and quota-full screens. If quotas affect routing, confirm the quota status variable behaves as expected.

Flow: Conditions and Routing

Review Exported Data

Submit test responses and inspect the export before publishing. Confirm that variables, recodes, condition assignments, and randomized order fields are present when needed.

Export: Data Output

Related Help

  • Preview and Pilot Test a Survey
  • reCAPTCHA Survey Verification
  • Short Answer Survey Questions
  • Export Research Survey Responses