1. Theme (v): Managing change
UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE CONFERENCE OF EUROPEAN STATISTICIANS Work Session on Statistical Data Editing April 2017 The Hague, Netherlands
Theme (v): Managing change
Discussants: Agnes Andics, Sander Scholtus

2. Introduction to theme (v)
Many generic ideas and methods for efficient and effective data editing are now well-established, e.g.,
Selective editing
Automatic editing
The use of generalised data editing systems
Introduction of new data sources (administrative/big data) can also prompt new approaches to data editing
However, successful introduction of new methodology into statistical production processes is not trivial

3. Introduction to theme (v)
Important factors for successful changes:
Obtaining support and cooperation from top-level management and editing staff as soon as possible
Planning development work in achievable and measurable stages
Evaluating the quality of data editing processes and monitoring the effects of changes

4. Introduction to theme (v)
In this theme: presentations on
Evaluating data editing processes
The use of process data
Indicators for impact of different process steps on results
Developing a generic, metadata-driven data editing system
Developing data editing methodology for a new data source

5. Agenda for theme (v)
United Kingdom: Improvements in editing methods and processes for use of Value Added Tax data in UK National Accounts
Denmark: Usage of process data in data editing at Statistics Denmark
Switzerland: Data preparation process analysis of the structural survey of the Swiss population census
Finland: An information model for a metadata-driven editing and imputation system
General discussion

6. Summary: presentations in theme (v)
WP.24 Improvements in editing methods and processes for use of Value Added Tax data in UK National Accounts (UK)
Development of a system to process VAT data for short term statistics
Review and refinement of previously developed methods
Rules for detecting systematic errors (£1000 error, quarterly patterns), automatic editing of suspicious turnover values
Suggestion to introduce selective editing of suspicious turnover values
VAT units vs. reporting units
In principle, would like to edit as early in the process as possible (VAT units)
However, better auxiliary information available for reporting units
Investigation whether auxiliary information can be translated to VAT unit level
Development process: agile, four week sprint, continuous feedback

7. Summary: presentations in theme (v)
WP.25 Usage of process data in data editing at Statistics Denmark (Denmark)
Project to modernise data editing at Statistics Denmark
Construction of a common database (Data Archive) for storing and editing surveys; includes storage of process data
Feedback from process data to improve data editing process, e.g.,
Extend score function for selective editing with the probability that a suspicious value would lead to a correction
Investigate unusual changes in process indicators such as edit rate
Investigate systematic reporting problems
Application to survey on pig farmers: yearly feedback process

8. Summary: presentations in theme (v)
WP.26 Data preparation process analysis of the structural survey of the Swiss population census (Switzerland)
Evaluation of the data editing process of structural census survey
Computed indicators proposed in EDIMBUS manual, including
Item response rate / ratio
Imputation rate / ratio
Structural missingness rate
Compared indicators
at different stages of the editing process
for different years (evolution monitoring)
Implemented indicators in R package ‘sdap’ (available online)

9. Summary: presentations in theme (v)
WP.27 An information model for a metadata-driven editing and imputation system (Finland)
Possibility of developing a generic system for data editing and imputation, driven by metadata (no hard-coded parameters)
Metadata information model influenced by Statistics Canada’s Banff
Parameters (such as edit rules) defined as separate metadata objects
Facilitates, e.g., re-use of the same edit rule by different methods
Parameters are versioned so the outcome of an editing process is reproducible
‘Naïve’ data organisation model proposed; will be made more efficient

10. Questions / Points for discussion
Paper by Statistics Denmark: regular feedback (using process data) to improve data collection and data editing processes
Experiences of other statistical institutes?
Indicators in papers by Statistics Denmark and SFSO are based on properties of data before and after editing/imputation
Advantage: indicators can be computed directly from available data
Limitation: no direct information on the effects of editing/imputation on the accuracy of statistical output (in terms of bias, variance)
Could impact of editing/imputation on accuracy of statistical output also be measured during regular production? Any experiences?

11. Questions / Points for discussion
Selective editing of administrative data (e.g., VAT data)
Number of influential errors may be too large for manual follow-up
Previous work sessions: ‘probabilistic selective editing’ proposed
Select random sample of records for manual follow-up (e.g., probability proportional to score)
Estimate correction term for the residual errors in the non-selected records
Any experiences of statistical institutes in practice?