<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Data editing is arguably one of the most resource-intensive processes at NSIs. Forced by everincreasing budget pressure, NSIs keep searching for more efficient forms of data editing. Efficiency gains can be obtained by selective editing, that is, limiting the manual editing to influential errors, and by automating the editing process as much as possible. In our view, an optimal mix of these two strategies should be aimed for. In this article we present a decomposition of the overall editing process into a number of different tasks and give an upto- date overview of all the possibilities of automatic editing in terms of these tasks. During the design of an editing process, this decomposition may be helpful in deciding which tasks can be done automatically and for which tasks (additional) manual editing is required. Such decisions can be made a priori, based on the specific nature of the task, or by empirical evaluation, which is illustrated by examples. The decomposition in tasks, or statistical functions, also naturally leads to reuseable components, resulting in efficiency gains in process design.</p></abstract>

Data editing is arguably one of the most resource-intensive processes at NSIs. Forced by everincreasing budget pressure, NSIs keep searching for more efficient forms of data editing. Efficiency gains can be obtained by selective editing, that is, limiting the manual editing to influential errors, and by automating the editing process as much as possible. In our view, an optimal mix of these two strategies should be aimed for. In this article we present a decomposition of the overall editing process into a number of different tasks and give an upto- date overview of all the possibilities of automatic editing in terms of these tasks. During the design of an editing process, this decomposition may be helpful in deciding which tasks can be done automatically and for which tasks (additional) manual editing is required. Such decisions can be made a priori, based on the specific nature of the task, or by empirical evaluation, which is illustrated by examples. The decomposition in tasks, or statistical functions, also naturally leads to reuseable components, resulting in efficiency gains in process design.

Automated and Manual Data Editing: A View on Process Design and Methodology

Statistics Netherlands, P.O. Box 24500, 2490 HA The Hague, The Netherlands

Journal of Official Statistics

{"article-title":"Automated and Manual Data Editing: A View on Process Design and Methodology"}

Data editing is arguably one of the most resource-intensive processes at NSIs. Forced by everincreasing budget pressure, NSIs keep searching for more efficient...

Automated and Manual Data Editing: A View on Process Design and Methodology

Publié en ligne: 09 nov. 2013

Pages: 511 - 537

DOI: https://doi.org/10.2478/jos-2013-0038

Mots clés
Automatic editing, selective editing, edit rules, process design, process evaluation

This content is open access.

Automated and Manual Data Editing: A View on Process Design and Methodology

Publié en ligne: 09 nov. 2013

Pages: 511 - 537

DOI: https://doi.org/10.2478/jos-2013-0038

Mots clésAutomatic editing, selective editing, edit rules, process design, process evaluation

This content is open access.

Mots clés
Automatic editing, selective editing, edit rules, process design, process evaluation