Survey Data Quality Screen
VerifiedPsychometrics (legacy hub)
Run this test straight away on a free built-in teaching dataset — no data of your own needed — or bring your own. Either opens the guided workspace: variable setup, assumption diagnostics, results with effect sizes and confidence intervals, figures, and APA-ready reporting.
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Or use your own dataset
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Pre-analysis integrity check for survey-response data.
Identifies responses that are likely INVALID and should be excluded or flagged before any substantive analysis. Six diagnostics: (1) missing-data pattern (overall + per-respondent + per-item); (2) Little's MCAR test; (3) straightlining (respondent checks same option for most items); (4) pattern responding (zigzag / repeating sequences); (5) speeders (completion time < 1/3 median); (6) careless-response flags summary. Recommended exclusion list + per-flag counts.
Worked example
Is the dataset clean enough to analyse?
A data-quality scan reviews missing values, duplicates, out-of-range and disguised-missing codes before any test is run.
The data were clean: 0.4% missing (consistent with MCAR), no duplicates, and no out-of-range values.
Data-quality screening found the dataset analysis-ready (0.4% missing, no duplicates or out-of-range values).
When to use it
- Online-survey pre-analysis integrity screenOnline personality survey on n=500 Prolific respondents.
- Longitudinal-survey attrition + missing-pattern check4-wave panel (n=800 at baseline) with decreasing n across waves.
When NOT to — use instead
- Post-hoc explanation of unexpected resultsData-quality screening should be PRE-SPECIFIED. → Survey Data Quality Screen
- Small sample where exclusion is costlyWith small n, exclusion may not be feasible. → Survey Data Quality Screen
- Single-administration face-to-face interviewsCareless-response indicators (straightlining, speeders) are tuned for online surveys. → Survey Data Quality Screen
- Item-text quality assessmentData-quality is about respondent behaviour. → Item Text Quality Audit
Assumptions (and what to do if they fail)
Check: See the assumption diagnostics in the workspace.
If violated: The workspace flags this and suggests a robust or nonparametric alternative.
Check: See the assumption diagnostics in the workspace.
If violated: The workspace flags this and suggests a robust or nonparametric alternative.
Check: See the assumption diagnostics in the workspace.
If violated: The workspace flags this and suggests a robust or nonparametric alternative.
Ready to run a Survey Data Quality Screen on your own data?
Guided setup, automatic assumption checks, effect sizes, figures and an APA write-up.
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