Quality Assurance

1. Quality Assurance

3. “mechanisms [that] are designed to prevent the introduction of errors into a data set, a process known as data contamination” QA/QC from Brunt 2000 • Commission: Incorrect or inaccurate data are entered into a dataset • – Can be easy to find • – Malfunctioning instrumentation • • Sensor drift • • Low batteries • • Damage • • Animal mischief • – Data entry errors • Omission: Data or metadata are not recorded • – Difficult or impossible to find • – Inadequate documentation of data values, sampling methods, anomalies in field, human errors

4. Verification and Validation • Verification – has data been entered correctly? • Validation – does data make sense ecologically? • Handout 5-1: Example of data verification request • Handout 5-2: Response from person responsible for data entry • Handout 5-3: Example of a data sheet with quality assurance samples • Handout 5-4: Example of a data sheet with a missing value

5. Verification and Validation • Handout 5-6: Total carbon duplicates - to test precision of measurements • Handout 5-7: Total carbon duplicates – to establish measurement quality objectives • Handout 5-8: % plant cover duplicates – comparison of results from routine field crews with an independent QA crew

6. Verification Process Types • Visual review at data entry (2nd person check) • Visual review after data entry (print out and review) • Duplicate data entry (enter random data in a testing DB) • Project Leaders are fully responsible for data (both verification and validation) • 100% of records checked by data entry staff • >= 10% random records checked by Project Leader for verification

7. NPS Director’s Order 11BEnsuring Quality of Information Disseminated by the NPS Defines quality as three key components Objectivity Utility Integrity You Passed!