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Quality in Official Statistics: Critical Issues

Explore the evolution of quality in statistics from historical milestones to modern frameworks and standards, essential for ensuring accurate data insights. Learn about quality reporting, management, indicators, and more at this week's conference.

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Quality in Official Statistics: Critical Issues

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  1. Quality in Official Statistics: Some Critical Issues Lars Lyberg Statistics Sweden Q, May 6, 2010 Email: lars.lyberg@scb.se

  2. Looking Back • Statistical Process Control (30’s and 40’s, Shewhart) • Neyman’s landmark paper 1934 • Fitness for use, fitness for purpose (Juran, Deming) QM science building up, customer recognition • Small errors indicate usefulness (Kendall, Jessen, Palmer, Deming, Stephan, Hansen, Hurwitz, Tepping, Mahalanobis), nonsampling errors recognized 1944-1953. • Decomposition of MSE around 1960 • Data quality (Kish, Zarkovich 1965)

  3. Administrative applications of SPC (late 60’s) • Quality frameworks 70’s • CASM movement 80’s • Quality and users 80’s • Business Excellence Models • ISO, TQM, Six Sigma, Kaizen, Lean, PDCA, BPR • Quality assurance and quality control • Standards and Quality Guidelines

  4. This Week’s Conference • Quality reporting and metadata • Quality management • Quality indicators, paradata, performance • Frameworks and standards • Errors and users • Design and harmonization • Data collection

  5. Back to the Future: What’s Critical? • Standard errors and confidence intervals do not include all error sources • Design, risk assessment and allocation of resources • Specifications, operations and control • Building capacity • Benchmarking, efficient production, processes and paradata

  6. Standards and guidelines, best practices, theories • International surveys • Users’ perceptions of quality • Do not leave key processes behind • Global coordination, benchmarking, training, conferences and workshops • The element of continuous improvement

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