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Management Tools for Enhancing the Composition of Survey Response

Management Tools for Enhancing the Composition of Survey Response. Q2008 European Conference on Quality in Official Statistics Rome, July 2008 Anne Sundvoll, Bengt Oscar Lagerstrøm Øyvin Kleven, Tora L ö fgren, Statistics Norway. Outline.

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Management Tools for Enhancing the Composition of Survey Response

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  1. Management Tools for Enhancing the Composition of Survey Response Q2008 European Conference on Quality in Official Statistics Rome, July 2008 Anne Sundvoll, Bengt Oscar Lagerstrøm Øyvin Kleven, Tora Löfgren, Statistics Norway

  2. Outline • A growing focus on how to improve response rates, minimize response bias and monitor fieldwork costs during the fieldwork period • Combination of easily retrieved administrative data and process data make continuous monitoring of survey variable of interest possible • The task is to maximize the result, given certain constraints as time or costs

  3. SIV – A system for managing multi mode data collection • Co-ordinates CATI, CAPI, CAWI and Paper Questionnaires • Master Management system • Updates, reports and graphs • Quick and continuous re-scheduling of actions

  4. Architecture example CATI In House Case Management Blaise CATI Service Deliver Cases CAWI Blaise Input Data Store Blaise Data Server Progress Report CASI In House Data Store Extract Data CAPI Third Party Data CADI 4

  5. Examples of Administrative Data • Mode(s) • Data collection period and dates of actions • Budget • Number of Interviewers • Interviewer characteristics

  6. Examples of Process Data • Response rate • Composition of nonresponse • Refusal rate • Non-contact rate • Telephone coverage • Workload • Cost

  7. Management Tools - description • Snapshots of the data collection process • Intervention must be possible • Transparent and predictable management system

  8. Data we would like to monitor During the data collection process, we would like to monitor the composition of….. • Process data • Auxiliary data • Target variables

  9. Examples of Quality Indicators • Response rate • Non-response rate • Response bias • Response burden • Revisions • Errors

  10. Monitoring of Process Variables

  11. Monitoring of Auxiliary Variables

  12. Monitoring of Target Variables

  13. Post stratification by background variables

  14. Benchmarking by data from previous rounds

  15. Examination of multiple bias

  16. Management Tools - Challenges • Get used to communicate through data displays • High skilled project managers • Demand of quick re-sceduling procedures

  17. Management Tool - Limitations • May draw attention away from serious measurement problems • May indicate more than one issue of concern • May add new bias

  18. Further work • Development of SIV • Experiences from real-time data collections • Development of more sophisticated management tools

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