Screen duplicates, speeders, straight-liners, and logic failures. Track completion, missingness, retention, and cleaning impact instantly. Prepare cleaner datasets for trustworthy modeling, reporting, and inference.
Enter survey collection, exclusion, and missing-data values. The calculator will estimate the usable sample and summarize cleaning quality below the header.
| Input or Output | Example Value | Notes |
|---|---|---|
| Total Responses | 1,200 | All submissions collected from the survey platform. |
| Completed Responses | 1,080 | Respondents meeting the completion rule. |
| Total Removed | 208 | Combined duplicates, incompletes, speeders, logic failures, outliers, and other exclusions. |
| Cleaned Responses | 992 | Usable sample after removing flagged records. |
| Retention Rate | 82.67% | Cleaned responses divided by total responses. |
| Missing-Data Rate | 1.75% | 520 missing cells across 29,760 reviewed cells. |
| Quality Score | 93.38 | Weighted average of retention, completion, missingness, and integrity subscores. |
1. Total Removed
Total Removed = Duplicates + Incomplete + Speeders + Straight-Liners + Logic Failures + Outliers + Other
2. Cleaned Responses
Cleaned Responses = Total Responses - Total Removed
3. Retention Rate
Retention Rate = (Cleaned Responses / Total Responses) × 100
4. Exclusion Rate
Exclusion Rate = (Total Removed / Total Responses) × 100
5. Missing-Data Rate
Missing-Data Rate = (Missing Cells / (Cleaned Responses × Variables Per Record)) × 100
6. Integrity Score
Integrity Score = 100 - ((Duplicates + Speeders + Straight-Liners + Logic Failures + Outliers) / Total Responses × 100)
7. Quality Score
Quality Score = Weighted average of Retention, Completion Subscore, Missing Subscore, and Integrity Score.
This score is a practical benchmark for internal review, not a universal statistical standard.
It estimates how many survey records remain usable after cleaning. It also summarizes completion, exclusions, missingness, integrity, and a practical overall quality score for reporting.
Not always. Some studies keep partial responses when the missing pattern is minor or analytically manageable. This calculator supports either approach by letting you decide how many incomplete records to exclude.
They indicate different quality risks. Speeders suggest low engagement, while straight-liners may signal inattentive behavior on repeated scales. Separating them helps document cleaning logic more clearly.
Higher is better, but acceptable ranges depend on your study design. In this calculator, scores above 90 indicate strong cleaning outcomes, while lower scores suggest more review is needed.
Yes. The weight inputs let you emphasize retention, completion, missing-data control, or integrity. This is helpful when stakeholder priorities differ across dashboards, audits, or formal analyses.
No. It is a decision-support tool for summarizing cleaning results. You should still review skip logic, coding rules, scale reliability, open-text quality, and any unusual respondent behavior.
The calculator divides missing cells by total reviewed cells in the cleaned dataset. Total reviewed cells equal cleaned responses multiplied by variables per record.
Export after your cleaning rules are finalized. CSV works well for audit trails and spreadsheets, while PDF is useful for stakeholder summaries, documentation packs, or project reports.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.