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Quality Control & Quality Assurance Carly Strasser DataONE, California Digital Library
Defini.ons Mechanisms for preven0ng errors from entering a data set
Quality assurance • Ac.vi.es to ensure data quality before collec.on
Quality control • Monitoring & maintaining data quality during the study
Types of Errors
Errors of Commission • Incorrect or inaccurate data entered • Examples: malfunc.oning instrument, mistyped data
Errors of Omission • Data or metadata not recorded • Examples: inadequate documenta.on, human error, anomalies in the field
QA/QC Before Collec.on
• Define & enforce standards Formats Codes Measurement units Metadata
• Assign responsibility for data quality Be sure assigned person is educated in QA/QC
QA/QC During Data Entry
• Double entry Data keyed in by two independent people Check for agreement with computer verifica0on
• Use text-‐to-‐speech program to read data back
• Use a database Minimize number of 0mes items must be entered repeatedly Use consistent terminology Atomize data: one cell per piece of informa0on
• Document changes Avoids duplicate error checking Allows undo if necessary
QA/QC ASer Data Entry
• Ensure data line up in columns
• No missing, impossible, or anomalous values sort by fields to highlight discrepancies
• Perform sta.s.cal summaries If data transforma0on performed, compare summaries before and aGer to assure no mistakes during transforma0on
QA/QC ASer Data Entry
Use illegal data filter • Illegal data: values that are impossible or unlikely • Filter using computer program
Inputs: possible ranges & values; raw data file Output: list of illegal data & error report
QA/QC Practices
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QA/QC ASer Data Entry Look for outliers • Outliers: extreme values for a variable given the sta.s.cal model being used
• Goal is not to eliminate outliers but to iden.fy poten.al data contamina.on
QA/QC ASer Data Entry
Methods to look for outliers • Graphical
Normal probability plots Regression ScaNer plots Maps
• Sta.s.cal
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