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UNECE - CONFERENCE OF EUROPEAN STATISTICIANS. Work Session on Statistical Data Editing Ljubljana, Slovenia, 9-11 May 2011. THE MAIN INNOVATIONS OF DATA EDITING AND IMPUTATION FOR THE 2010 ITALIAN AGRICULTURAL CENSUS G. Bianchi, R. M. Lipsi, P. Francescangeli, G. Ruocco,
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Work Session on Statistical Data Editing
Ljubljana, Slovenia, 9-11 May 2011
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
strategy and census stages
during data collection
stages after data capturing
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
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
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&Iprocess will feature three main stages:
E&I during data collection
Provisional figures dissemination (primary variables)
Final results dissemination
In order to prevent and correct fatal errors and missing values during data capturing
Census Data Collection System
Data collection staff
A subset of 220 checking
rules (fatal and query) has
been implemented in the web based data entry System
Before the final release of data to the census DB, to localize potential errors slipped during data gathering
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
Underlines inconsistent data by analyzing at unit level the coherence between the answers referring to related topics
Forward Search Technique: outliers detection among strata, defined according to the crop type and the farm size
-Parameters estimation a and b, with and without outliers
-Statistical significance and goodness of fit of the regression model (R2)
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
First step of each E&I stage
Automatic error detection
Macro level editing
Micro 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
Erroneous values in individual records are automatically identified by means of edit rules
Second step of each E&I stage
Treatment of errors
Treatment of the outliers and influential errors, having substantial impact on data dissemination is based on manual review
Model based techniques or nearest neighbour donor will be used for the imputation of not influential random errors
Treatment of not influential random errors is based on minimum change approaches
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
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
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
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 Copulafunctions (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)
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.