Structured quality controls for cleaner research evidence.
MIU reviews response behaviour, source metadata, open text, survey logic and fieldwork signals to identify and reduce low-quality or suspicious data before analysis.
Multiple signals, reviewed together.
No single rule proves fraud. MIU combines project-specific rules and context to assess data quality.
Duplicates & identity
Respondent IDs, source records, repeated patterns and available device/IP indicators.
Speed & engagement
Completion time, page timing, dropouts, item non-response and low-effort behaviour.
Pattern checks
Straightlining, contradictory answers, improbable combinations and quota consistency.
Open-end quality
Gibberish, copied text, irrelevance, repetition, translation concerns and AI-like similarity signals.
Define, review, flag, decide and document.
- Agree rulesProject thresholds, exclusions, warnings and client decision rights.
- Run checksAutomated and manual review across records and sources.
- InvestigateReview suspicious clusters, supplier patterns and open text.
- CleanRemove, retain or flag according to agreed criteria.
- ReportProvide accepted data and transparent cleaning notes.
Files clients can audit.
- Flagged dataset with reason codes.
- Cleaned analysis-ready dataset.
- Source- or supplier-level quality summary where data is available.
- Open-end review output and coding notes.
- Removal and retention counts.
- Validation methodology and caveats.
SPSS, R, Excel and AI-assisted review.
Tool selection depends on file structure, sample size, validation rules, reproducibility and client requirements. AI is used to accelerate review, not to make untraceable exclusion decisions.
We do not promise zero fraud.
MIU applies structured controls to identify and reduce suspicious or low-quality responses. Final confidence depends on recruitment source, respondent verification, questionnaire design, available metadata and agreed rules.
See how raw responses become a cleaner research dataset.
Understand how rules, statistical signals, AI-assisted review and human judgement work together.
Open the enquiry form with this service selected, review the prefilled details and click Send enquiry for review.
Start with context, not only a file
The questionnaire, source information, fieldwork notes and client rules shape the validation plan.
- Questionnaire and raw data
- Supplier and source metadata
- Agreed thresholds and exclusions
Flag suspicious records across multiple signals
Automated checks surface duplicates, speeders, patterned answers, contradictions and low-quality open text.
- Time and engagement
- Straightlining and logic
- Open-end similarity and relevance
Review source, answers and open text before decision.
Let analysts investigate the evidence
Human reviewers inspect records and clusters before important exclusions are made.
- Record-level reason codes
- Source and quota patterns
- AI signals verified by people
Hand over data clients can audit
The client receives accepted data, flagged records, cleaning notes and a transparent summary.
- Cleaned analysis file
- Flag file with reasons
- Removal counts and methodology
Send the questionnaire, raw file and known quality concerns.
MIU will propose checks, decision rules, outputs and turnaround before work begins.