Selective Editing Strategies for Efficient Data Editing Process

1. Topic (i): Selective editing / macro editing Discussants Orietta Luzi - Italian National Statistical Institute Rudi Seljak - Statistical Office of Slovenia Pedro Revilla - National Statistical Institute of Spain

2. Background • The adoption of selective editing / macro editing approaches can significantly improve the efficiency of the data editing process • This is especially true in the case of business surveys where usually a small amount of measurement errors have a decisive influence on the final results and therefore the appropriate usage of selective editing and macro editing techniques can significantly reduce the cost of the data editing process with no major impact on the quality of the final results • Developing tools implementing selective editing models/functions may validly support further harmonization and widening use of efficient editing strategies in NSIs Topic (i) Selective editing / macro editing

3. Topic contents • Practical implementation of local and global score functions • Integration of selective editing / macro editing techniques within the statistical process • Tools to support selective editing / macro editing, including visualization approaches • Validation of selective editing strategies, inference issues • Editing of unstructured data Topic (i) Selective editing / macro editing

4. Long Presentations • Italy - Use of administrative data for selective editing: the case of business investments • Spain - Score Functions under the Optimization Approach • United Kingdom – Maintenance of Selective Editing in ONS Business Surveys Topic (i) Selective editing / macro editing

5. Short Presentations • Sweden – Experiences from Selective Data Editing at Statistics Sweden • France – Selective Editing Techniques and Seasonal Adjustment of Short-Term Economic Indicators • France – Using R-indicators to monitor household surveys and prioritize data collection: an application to the 2010 Household Wealth survey in France • Japan – An Assessment of Automatic Editing via the Contamination Model and Multiple Imputation • United States – Text Analysis Tools for Editing and Verification • Sweden – Adjusting for Remaining Measurement Errors after Selective Editing Topic (i) Selective editing / macro editing

6. Short Presentations • Sweden – Adjusting for Remaining Measurement Errors after Selective Editing (T. Laitila, A. Norberg) Model-based approach for bias correction of estimators due to the remaining errors in selective edited data sets, where the observed measurement errors in the edited units are related to their scores. The estimated models are used to predict measurement errors in unedited units and their associated bias. Experimental application to data of the sample survey on establishments and their salary payments to employees carried on at Statistics Sweden. Topic (i) Selective editing / macro editing

7. Topic (i): Selective editing / macro editing Discussion

8. Overall summary Session papers provide a wide-ranging overview of current research and development activities in the area of selective / macro editing carried out at various NSIs with the aim of enhancing the efficiency of survey editing strategies • Theoretical formalizations of the selective editing paradigm are proposed, in a situation where no accepted theory has been developed in this area yet ; demanding modelling problems come out from presentations • The need of a common theoretical evaluation framework for quality assessment of selectively edited data is an area requiring further research at also an international perspective • Issues related to the development and dissemination inside NSIs of “generalized” IT-tools for selective editing are discussed, with specific attention to those relating to applicability, maintenance and optimization of the implemented methods/models • New application contexts where selective / macro editing principles can contribute to most efficient statistical production processes are explored

9. Points for discussion • Integrating selective / macro editing in existing E&I process • Advantages (increased efficiency, burden reduction,…) • Costs (re-design of processes, models/parameters maintenance/update due to changes of populations/information contexts, …) • Cultural issues (changes in attitude needed, importance of support by the survey staff, importance of constant training and education,…) • Countries’ experiences Papers from Italy, Japan, UK, Sweden (A. Norberg) Topic (i) Selective editing / macro editing

10. Points for discussion Formalization of selective editing Ad hoc methods VS theoretical frameworks How to estimate the model parameters? How do we apply the developed frameworks in practice? Papers from Italy and Spain UNECE Work session on SDE, Paris, 28-30 April 2014 Topic (i) Selective editing / macro editing 10

11. Points for discussion • Standard tools for selective editing • Advantages (costs savings, harmonization, easy to document and to update, …) • Drawbacks (specificity against generalization, validity of models assumptions, “black-box” effect, ….) • International co-operation, sharing solutions SeleMix (papers from Italy, Japan), Selekt (papers from Sweden, UK) Topic (i) Selective editing / macro editing

12. Points for discussion • Selective and macro editing and administrative data • Potentials (use of admin data as auxiliary information for error modeling/error detection/error correction) • Direct use of admin data for estimation purposes: feasibility, costs, role of selective / macro editing Paper from Italy Topic (i) Selective editing / macro editing

13. Points for discussion • Assessing the validity of a selective / macro editing strategy • Which indicators, quality measures, evaluation approaches • Inference and quality assessment under selectively edited data • Countries’ experiences Paper from Italy, Sweden (Laitila-Norberg) Topic (i) Selective editing / macro editing

14. Points for discussion • New, innovative approaches in selective / macro editing strategy • Editing of unstructured data by using text analyses tools • Using R-indicators for prioritization of follow-up • Countries’ experiences Paper from United States , France (Merly-Alpa) Topic (i) Selective editing / macro editing