Data Cleaning
Automated validation and quality assurance
Data quality is critical for meaningful insights. Our platform implements multiple layers of validation and cleaning, both during data entry and after collection, to ensure the highest quality data reaches your analysis systems.
Real-time Field Validation
As data is entered, the platform performs immediate checks to prevent common errors.
- Required field enforcement
- Format constraints (dates, numbers, text)
- Skip logic enforcement
- Range validation
- On-spot error prompts
Automated Cleaning Rules
Post-collection rules that detect and flag data quality issues.
- Inconsistency detection
- Outlier identification
- Cross-field validation
- Sequence verification
- Duplicate detection
EGRA/EGMA Standard Practices
We work closely with statisticians to document and formalize standard EGRA and EGMA data-cleaning practices typically applied by researchers, systematically encoding these as automated rules within the platform.
Assessment Sequence Checks
Flags when "last word read" is selected before "last error marked" in oral reading fluency tests.
Score Consistency
Validates that calculated scores match the marked responses and timing data.
Timing Anomalies
Detects unusually fast or slow assessment completions for review.
Configurable Rules
Administrators can define custom cleaning rules based on assessment design.
Administrator Review Workflow
Flag
System identifies problematic records
Queue
Flagged items added to review queue
Review
Admin reviews with full context
Action
Correct, exclude, or approve