1. THE MAIN INNOVATIONS OF DATA EDITING AND IMPUTATION FOR THE 2010 ITALIAN AGRICULTURAL CENSUS G. Bianchi, R. M. Lipsi, P. Francescangeli, G. Ruocco, A. M. Salvatore, F. Scalfati (giruocco@istat.it) Work Session on Statistical Data Editing Ljubljana, Slovenia, 9-11 May 2011 UNECE - CONFERENCE OF EUROPEAN STATISTICIANS

2. Outline Introduction E&I strategy guidelines strategy and census stages during data collection stages after data capturing E&IS tools and innovations Conclusions References

3. Introduction  For the 6 th Italian Agricultural Census, a new Editing and Imputation System (E&IS) has been implemented in order to reduce the total census error  The main purpose of the E&IS is to identify and treat the non sampling errors, in order to provide a complete and consistent set of data

4. E&I strategy guidelines (1)  Quality oriented approach by performing the E&I process from data collection to the final figures  Data editing and detection of outliers and influential errors (selective editing) during data collection  After data capturing, scheduling of two main correction stages, centrally managed by Istat

5. E&I strategy guidelines (2)  Use of techniques that minimize the number of changes especially for the treatment of not influential random errors  Quality indicators to monitor the main steps of E&I  Ad-hoc documentation to evaluate the outcome of the procedures, paying particular attention to changes due to the E&I process

6. E&I strategy and census stages (1)  According to the E&I strategy, all variables are separated into different related subsets to identify the most appropriate treatment for each of them  The E&I process will feature three main stages: 1.E&I during data collection 2.Provisional figures dissemination (primary variables) 3.Final results dissemination

7. E&I strategy and census stages (2)

8. E&I during data collection (1) In order to prevent and correct fatal errors and missing values during data capturing Questionnaire editing Holdings/enumerators A subset of 220 checking rules (fatal and query) has been implemented in the web based data entry System Automatic check Data collection staff Before the final release of data to the census DB, to localize potential errors slipped during data gathering Census Data Collection System

9. E&I during data collection (2) Before the end of field enumeration operations, and while data collection network is still in force, two distinct procedures have been implemented and launched by Istat to detect influential errors and outlier values Outliers detection -Forward Search Technique -manual review of anomalous values by data collection staff Micro-editing check Underlines inconsistent data by analyzing at unit level the coherence between the answers referring to related topics E&I SYSTEM

10. E&I during data collection (3) Forward Search Technique: outliers detection among strata, defined according to the crop type and the farm size Census Administrative Register -Regression line Y=aX+b -Parameters estimation a and b, with and without outliers -Statistical significance and goodness of fit of the regression model (R2)

11. E&I stages after data capturing (1)  In order to achieve maximum coherence between provisional and final data at regional level, the strategy adopted is firstly to correct all the primary variables and then the secondary ones  After data collection, two main correction stages are scheduled. In the first stage, all the variables for the dissemination of provisional figures (primary variables) are corrected  In each E&I stage, the following steps are repeated: automatic error detection and treatment of errors

12. E&I stages after data capturing (2) First step of each E&I stage Automatic error detection Macro level editing - Uses all (or large part) of data to identify errors - Enables to evaluate the accuracy of preliminary estimates such as totals (or subgroups main figures) - Outliers detection Micro level editing Erroneous values in individual records are automatically identified by means of edit rules

13. E&I stages after data capturing (3) Second step of each E&I stage Treatment of errors Selective editing Treatment of the outliers and influential errors, having substantial impact on data dissemination is based on manual review Random errors Treatment of not influential random errors is based on minimum change approaches Imputation Model based techniques or nearest neighbour donor will be used for the imputation of not influential random errors

14. E&IS tools and innovations (1)  Inclusion of a subset of edit rules in the data capture stage  Use of Forward Search methods for the outliers detection  Use of administrative sources for micro and macro data checks  Use of score functions to prioritize records to be manually reviewed  Use of minimum change based model or nearest neighbour approach for localizing residual random errors  Mix of different imputation methods as nearest neighbour approach or model based imputation

15. E&IS tools and innovations (2)  The core of E&IS is the software DIESIS (Data Imputation Editing System – Italian Software), used for dealing with non influential errors in quantitative variables  DIESIS was developed in 2001 by ISTAT and academic researchers of the University of Rome “ Sapienza”  In DIESIS, optimization techniques were implemented for the simultaneous treatment of qualitative and quantitative variables  Joint use of data driven and minimum change approaches  DIESIS localization performance has been compared with the performance of the Canadian software BANFF

16. E&IS tools and innovations (3)  The scheduling and the monitoring of all procedures and the interactive corrections will be managed by CONCERT, a Java web application  To test the E&IS while implementing the scheduled procedures, an Oracle database was implemented  The whole process of E&I will be documented by a set of quality indicators both, on the data collected and on the results of the different editing steps

17. E&IS tools and innovations (4)

18. E&IS tools and innovations (5) Some simulation studies have been carried out for:  identifying for each section of the questionnaire, the most appropriate correction approach  evaluating the imputation of missing non linearly dependent data through conditional Copula functions (developed by ISTAT and the University of Bologna)  assessing the use of Forward Search techniques (robust statistical methods) for outliers detection (developed by ISTAT and the University of Parma)

19. Conclusions  The innovative E&I strategy will reduce the efforts of coping with timeliness constraints and will increase data consistency and accuracy  The results of the procedures implemented in the E&IS are very encouraging and allow to trust in a good improvement of census data quality

20. Thank you!!!

21. References Bianchi G., Di Lascio F. M. L., Giannerini S., Manzari A., Reale A., Ruocco G. (2009-a) Exploring copulas for the imputation of missing nonlinearly dependent data, Seventh Scientific Meeting of the CLAssification and Data Analysis Group of the Italian Statistical Society Università di Catania (Italy). September 9-11, 2009. Bianchi G., Francescangeli P., Manzari A., Reale A., Ruocco G., Salvi S. (2009-b) An overview of Editing and Imputation System of 2010 Italian Agriculture Census. Round. Roundtable Meeting on Programme for the 2010 Round Census of Agriculture. Budapest 23-27 november 2009. Bianchi G., Manzari A., Reale A., Salvi S. (2009-c) Valutazione dell’idoneità del software DIESIS all’individuazione dei valori errati in variabili quantitative. Istat - Collana Contributi Istat – n. 1 – 2009. Cotton C. (1991) Functional description of the generalized edit and imputation system. Business Survey Methods Division - July 25 Statistics Canada. Kovar J.G., MacMillian J.H., and Whitridge P. (1988) Overview and strategy for the generalized edit and imputation system. Report, Methodology Branch - April 1988 (updated February 1991) Statistics Canada. Luzi et al. (2007). EDIMBUS. Recommended Practices for Editing and Imputation in Cross-Sectional Business Surveys, August 2007. Riani M., Atkinson A. C. (2000). Robust Diagnostic Data Analysis: Trasformations in Regression. TECHNOMETRICS. vol. 42, pp. 384-394 ISSN: 0040-1706. With discussion.