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by Anders Norberg, Karin Lindgren and Can Tongur Statistics Sweden

Work Session on Statistical Data Editing Paris , France, 28-30 April 2014 Topic ( i ): Selective editing / macro editing Experiences from Selective Editing at Statistics Sweden. by Anders Norberg, Karin Lindgren and Can Tongur Statistics Sweden. Purpose of SE.

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by Anders Norberg, Karin Lindgren and Can Tongur Statistics Sweden

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  1. Work Session on Statistical Data EditingParis, France, 28-30 April 2014Topic (i): Selective editing / macro editingExperiences from Selective Editing at Statistics Sweden by Anders Norberg, Karin Lindgren and Can TongurStatistics Sweden

  2. Purposeof SE • Reduce costs for the manual work at the NSI without losing substantially in precision in estimates • Reduce the response burden for enterprises

  3. History from a Swedish perspective • Granquist, L. and Kovar, J.G. (1997). Editing of Survey Data: How Much is Enough? • Foreign Trade with Goods (2005) • Case studies of the potential use of SE (2007) • SELEKT comprises of a well-structured set of open-coded SAS™ macros and programmes

  4. SE in eleven surveys that had large spending on micro editing • Foreign Trade with Goods (Intrastat) • Commodity Flow Survey • International Trade in Services • Wage and Salary Structures in the Private Sector • STS, Wages and Salaries, Private Sector • STS, Employment, Private Sector • STS, Business Activity Indicators • Rents for dwellings • Revenues and Expenditure Survey for Multi-Dwelling Buildings • Energy Use in Manufacturing Industry • Consumer Price Index (CPI)

  5. Survey design • Annual / Short term • Sample / Census • One-stage / Multi-stage • Errors in classification variables • Little / voluminous output

  6. IT systems • Error lists on paper or Excel • Bespoke system inter­faces for the editing staff in VB6 or VB.net and data stored in SQL-databases • Triton, is a general production system for the collection and editing of micro data, under construc­tion at SCB. Here SE is a “service”

  7. Macro Editing • Decreased substantially, transferred to micro editing

  8. Quality • Improved edit rules on the way • Re-contacts closer to the data delivery • Less risk for mishaps when units are prioritised

  9. Editing staff opinions • More efficient, • More interesting • Less stressful • Focus on fewer units • “Now we know that the manual review is important and that our work really matters”

  10. Confidence of the respondents • Wonder why merely some of the cluster elements (products / employees) were flagged as erro­neously when the respondent could identify all elements having the same (systematic) error? • No thorough survey has been done to explore the respondent´s confidence of SE

  11. Resources needed • 300 – 600 hours of work

  12. Resources saved • 10 – 60 per cent cost savings for editing

  13. Maintenance, Process data • New Statistical Classification of Economic Activities (NACE 2007) • New International Standard Classification of Occupations (ISCO 2014) • Now considering a “five-year evaluation” for update of thresholds • Sampling under the global threshold for estimation of remaining measurement errors

  14. Merci pour votre attention

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