1. Improving the efficiency of editing in ONS business surveys
Rachel Skentelbery
Office for National Statistics, UK

2. Overview Introduction to Eden Project
Selective Editing Methodology – Short term surveys
Annual Business Survey
Overview of SELEKT
Results
Collaborations and next steps

3. Eden Project
New editing strategy for the ONS following visit in 2007 from Australian Bureau of Statistics
Aim to improve the efficiency of editing process for business surveys, based on Australian model
Deliver efficiencies with little impact on quality
Better balance between micro and macro editing
Implementation of new selective editing for RSI and MBS

4. Eden Principles
1. Holistic Approach
2. Sound Methods
3. Maximise Impact of Resources
4. Process Quality
5. Informed Data
6. Continuous Quality Improvement

5. Selective editing methodology - STS
Targets potential errors which have a significant impact on key estimates
Selective editing works by:
assigning a score to each business;
the score reflects the impact that editing the response would have on the estimates;
businesses with a score above their domain threshold are validated;
those businesses with a score below the threshold are not validated.

6. Quality Measures - ARB
Absolute relative bias aims to control the residual bias left in the domain estimates after editing

7. Savings
Savings measure the change in the number of units that will be manually micro edited

8. Results – Short Terms Surveys
The number of forms failing has reduced by approximately 20% on both RSI and MBS
Quality – ARB below 1% for all domains
Monitoring a number of ‘quality indicators’

9. Annual Business Survey (ABS)
Large business survey split by different sectors of the economy
Many questions asked from each business
Large number of edit rules applied to identify suspect values in returned data

10. Alternative approach needed for ABS
Current selective editing is unsuitable for ABS due to
Large number of (key) survey variables
Lack of sufficient predictors needed to calculate scores
Collaboration between ONS, Statistics Sweden and Pedro Silva (University of Southampton) to investigate use of SELEKT

11. SELEKT Tool Generic software to perform Selective Editing
Developed at Stats Sweden by A. Nordberg and team
Set of SAS macros
Driven by large set of user-specified parameters
Can deal with large, complex surveys like ABS

12. SELEKT Scoring Method Local scores defined for each combination of:
Domain of interest (d)
Variable (j)
Record / unit (i)
Local score used in SELEKT considers three components:
Potential impact
Suspicion
Importance
Global score is obtained by aggregating local scores

13. Results from testing Production & Construction Sector Catering Sector
Total number of records: 4,164
Records flagged by original edits: 2,923
Catering Sector
Total number of records: 784
Records flagged by original edits: 620
Global threshold
Records flagged - SELEKT
Savings %
Average Global Bias (all) %
Average Global Bias (key) %
0.0005
2259
22.7
0.7
0.3
Global threshold
Records flagged - SELEKT
Savings %
Average Global Bias (all) %
Average Global Bias (key) %
0.0001
483
22.1
0.8
0.2

14. Work in progress Completed feasibility study – looking to implement
Fine-tuning parameters for P&C and Catering
Also testing Statistics Sweden LAB to help choose best performing parameters
Set up parameter table for all other sectors
Plans for use in ABS, 2012 dispatch