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An Evolutionary Schema for Using “it-is-what-it-is” Data in Official Statistics

Author

Listed:
  • Lothian Jack

    (360 Hinton Ave S, OttawaON K1Y1A5Canada.)

  • Holmberg Anders

    (Statistics Norway, Division for Methodology, Akersveien 26 Oslo, Norway.)

  • Seyb Allyson

    (Stats NZ, Statistical Methods, Private Bag 4741, Christchurch8011, New Zealand.)

Abstract

The linking of disparate data sets across time, space and sources is probably the foremost current issue facing Central Statistical Agencies (CSA). If one reviews the current literature looking for the prevalent challenges facing CSAs, three issues stand out: 1) using administrative data effectively; 2) big data and what it means for CSAs; and 3) integrating disparate data set (such as health, education and wealth) to provide measurable facts that can guide policy makers. CSAs are being challenged to explore the same kind of challenges faced by Google, Facebook, and Yahoo, which are using graphical/semantic web models for organizing, searching and analysing data. Additionally, time and space (geography) are becoming more important dimensions (domains) for CSAs as they start to explore new data sources and ways to integrate those to study relationships. Central agency methodologists are being pushed to include these new perspectives into their standard theories, practises and policies. Like most methodologists, the authors see surveys and the publications of their results as a process where estimation is the key tool to achieve the final goal of an accurate statistical output. Randomness and sampling exists to support this goal, and early on it was clear to us that the incoming “it-is-what-it-is” data sources were not randomly selected. These sources were obviously biased and thus would produce biased estimates. So, we set out to design a strategy to deal with this issue.This article presents a schema for integrating and linking traditional and non-traditional datasets. Like all survey methodologies, this schema addresses the fundamental issues of representativeness, estimation and total survey error measurement.

Suggested Citation

  • Lothian Jack & Holmberg Anders & Seyb Allyson, 2019. "An Evolutionary Schema for Using “it-is-what-it-is” Data in Official Statistics," Journal of Official Statistics, Sciendo, vol. 35(1), pages 137-165, March.
    • Handle: RePEc:vrs:offsta:v:35:y:2019:i:1:p:137-165:n:7
    DOI: 10.2478/jos-2019-0007
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    References listed on IDEAS

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    1. Jabine, Thomas B & Scheuren, Fritz, 1985. "Goals for Statistical Uses of Administrative Records: The Next 10 Years," Journal of Business & Economic Statistics, American Statistical Association, vol. 3(4), pages 380-391, October.
    2. Wu C. & Sitter R. R, 2001. "A Model-Calibration Approach to Using Complete Auxiliary Information From Survey Data," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 185-193, March.
    3. David J. Hand, 2018. "Statistical challenges of administrative and transaction data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 555-605, June.
    4. T Holt, 2000. "The future for Official Statistics," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(9), pages 1010-1019, September.
    5. Jabine, Thomas B, 1985. "Goals for Statistical Uses of Administrative Records: The Next 10 Years: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 3(4), pages 402-404, October.
    6. Li‐Chun Zhang, 2012. "Topics of statistical theory for register‐based statistics and data integration," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(1), pages 41-63, February.
    7. Alfio Ferrara & Andriy Nikolov & François Scharffe, 2011. "Data Linking for the Semantic Web," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 7(3), pages 46-76, July.
    Full references (including those not matched with items on IDEAS)

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