A Framework for Data Quality: Case Studies

Details

• Creators:
  Mirel, Lisa B ; Singpurwalla, Darius ; Hoppe, Travis ; Schmitt, Rolf ; Weber, Julie ; Liliedahl, Erika
• Corporate Creators:
  United States, Federal Committee on Statistical Methodology. Data Quality Framework Implementation Subcommittee
• Subject/TRT Terms:
  • Data collection
  • Data analysis
  • Data quality
  • Machine learning
  • Case studies
• DOI:
  https://doi.org/10.21949/1529869
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  United States. Federal Committee on Statistical Methodology
• Abstract:
  The Federal Committee on Statistical Methodology (FCSM) released A Framework for Data Quality in September 2020 to provide a common foundation upon which federal agencies can make informed decisions about the quality of data products and their management throughout their life cycle. The framework introduces data program managers and analysts—including statisticians, chief data officers, and evaluation officers—to a broad range of quality considerations and provides a common, systematic language for communicating data quality issues and methods for resolving or accepting those issues. The framework includes quality issues beyond bias and accuracy, the traditional focus of statisticians, and encompasses data from sources including surveys, administrative records, monitors, and blended data.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/72804/dot_72804_DS1.pdf
• File Type:
  [PDF - 787.19 KB]
• Collection(s):
  FCSM
• Main Document Checksum:
  urn:sha-512:8a117d2ef1e3c8156b6e925602634e484bc6c1c58d816c24cc40bddd4c234d123de8caf9ecd36a9013ec77077291f9682fec0a54ea8cfa5dc1f949b724bacce2