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The New View on Editing

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  • Leopold Granquist

Abstract

Numerous evaluation and other studies show that the heavy cost of editing cannot be justified by quality improvement. It is necessary to replace the old paradigm‐the more and tighter the checks and recontacts, the better the quality‐by a new‐focus the editing on identifying and collecting data on errors, problem areas, and error causes to provide a basis for a continuous improvement of the whole survey vehicle. The corner stone of the new view on editing is that the entire set of the query edits should be designed meticulously, be focused on errors influencing the estimates, and be targeted on existing error types which can be identified by edits, and finally that the effects of the edits should be continuously evaluated by analysis of performance measures and other diagnostics, which the process should be designed to produce. The paper is a short introduction to modern editing under the new view. It presents, also, some facts to the low efficiency of traditional editing, and explains it by giving a historical review of editing.

Suggested Citation

  • Leopold Granquist, 1997. "The New View on Editing," International Statistical Review, International Statistical Institute, vol. 65(3), pages 381-387, December.
    • Handle: RePEc:bla:istatr:v:65:y:1997:i:3:p:381-387
    DOI: 10.1111/j.1751-5823.1997.tb00315.x
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    Cited by:

    1. Di Zio Marco & Guarnera Ugo, 2013. "A Contamination Model for Selective Editing," Journal of Official Statistics, Sciendo, vol. 29(4), pages 539-555, December.
    2. George Petrakos & Claudio Conversano & Gregory Farmakis & Francesco Mola & Roberta Siciliano & Photis Stavropoulos, 2004. "New ways of specifying data edits," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(2), pages 249-274, May.
    3. de Waal Ton, 2013. "Selective Editing: A Quest for Efficiency and Data Quality," Journal of Official Statistics, Sciendo, vol. 29(4), pages 473-488, December.
    4. Valentin Todorov & Matthias Templ & Peter Filzmoser, 2011. "Detection of multivariate outliers in business survey data with incomplete information," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 5(1), pages 37-56, April.
    5. Arbue´s Ignacio & Revilla Pedro & Salgado David, 2013. "An Optimization Approach to Selective Editing," Journal of Official Statistics, Sciendo, vol. 29(4), pages 489-510, December.

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