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A systematic and strategic system for data collection management at Statistics Norway

Explore the systematic utilization of action plans and Lean philosophy in managing data collection systematically and strategically. Learn how these tools enhance quality, efficiency, and product improvement in data statistics.

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A systematic and strategic system for data collection management at Statistics Norway

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  1. A systematic and strategic system for data collection management at Statistics Norway Seminar on New Frontiers for Statistical Data Collection Geneva, Switzerland, 31 October-2 November 2012 Bengt Oscar Lagerstrøm, bol@ssb.no, Statistics Norway

  2. Outline • Background • What is a action plan? • Use of action plans • Results • Conclusion/Further works

  3. Background • Increasing emphasis on quality management • Total quality management, continuous quality improving, six sigma, and so forth • At the same time, our industry have experienced • Increased non response rates, and • (much) higher survey costs

  4. Respons rates, Norway

  5. Quality in data collection • ”Number of ton produced” • Number of interview started • Item non-response • ……. • Optimal planning • How many interviewers and when? • Reduction of non-scheduled stops • Organization of good flow

  6. Lean philosophy • Is about • structuring, • operating, • controlling, • managing, and • continuously improving (our data collection)

  7. Action plans and Lean • Purpose: Make a environment for • reducing costs • increase efficiencies, and • improve our products/statistics • How? • Reducing “waste” • “Only one time” • Organize and standardizes

  8. Why action plans? • A tool for designing and manage data collection within an allocated resource limit • A tool to monitor that actions leads to better quality given a set of resources • Documentation • But most of all, a tool for making a (different) culture around a data collection • Improved competence through more systematically and strategic approaches

  9. What is a action plan? • A description of the planning and implementation of the set of process that make a data collection • Quality indicators • Demming’s quality wheel (PDCA) • A preparation for both anticipated and unexpected challenges • A documentation • Of actions and results • A communication strategy • Who is responsible for what and when? • A monitoring system • indicators • A guidance • For next wave or a different survey

  10. Action plans • Goals and key figures • Actions • Before and during the field period • What to do, when and who? • Indicators • response rate, • contact rate, • recruitment rate, • r-indicator etc.

  11. Goal Mål 60% intervju (anslag på ca 25 avganger) , utvalg 2000, 16-79 år. Antall intervju 1200 Antall minutter:  11 minutter i gjennomsnitt.   

  12. Actions before the field period starts

  13. Actions during the field period, part 1

  14. Actions during the field period, part 2

  15. Indicators

  16. Results, Omnibus • Shorter field period • from 6-7 weeks to 5 • Less interviewer hours • Standardized procedures • Steady state • No jumping!

  17. Results, LFS • Shift in ‘mode’ between 2. and 3 quarter 2011 (from decentralized CATI to centralized CATI) • New challenges (questionnaire, contact strategies) • Standardized procedures • Steady state • No jumping!

  18. Conclusions • Standards for planning, communication, reporting, correction, documentation and evaluation of the field period • Especially surveys with • less complexes structures, • few involvements (persons), or • high degree of repetitive actions • Further work • Look for more similar iterative processes in different surveys • Work more with the teams (communication and standardization) • Not only steady state, but also continuous improvement • Higher response rate • Lower bias • Lower cost

  19. Thank you for your attention! Bengt Oscar Lagerstrøm, bol@ssb.no, Statistics Norway

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