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Guidelines on the quality of multisource statistics

Guidelines on the quality of multisource statistics. Giovanna Brancato, Italian National Statistical Institute (Istat), brancato@istat.it Gabriele Ascari, Italian National Statistical Institute (Istat), gabascari@istat.it. 29 June 2018. Session 37. Purpose of the work.

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Guidelines on the quality of multisource statistics

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  1. Guidelines on the quality of multisource statistics Giovanna Brancato, Italian National Statistical Institute (Istat), brancato@istat.it Gabriele Ascari, Italian National Statistical Institute (Istat), gabascari@istat.it 29 June 2018 Session 37

  2. Purpose of the work To develop Quality Guidelines for Multisource Statistics (GQMSS) • Multiple administrative sources or administrative sources with survey data (excluding big data) • Eurostat quality dimensions & errors’ framework • Following the statistical process chain (GSBPM) • Considering the basic data configurations • Reference to Guidelines of Output Quality measures and indicators & Applications WP3 Guidelines practical and applicable in the ESS

  3. Uses of the guidelines The guidelines are oriented to: • Process managers of the NSIs of ESS Member States They can be used: • As a reference, to guide in the production of multisource statistics at the highest quality standards • To increase the awareness of quality issues in multisource statistics • To support quality assessment Increase the quality of the statistical production in the ESS

  4. Quality guidelines* • Generally accepted principles for the production of statistics. • They also provide guidance as to what is considered important and less important regarding effects on the product quality. • The process manager and his/her team is free to make the final choices. • Usually, they are a short and agile documents. *Leadership Expert Group on Quality, Leg on Quality (2001)

  5. Content of the QGMSS Introduction Part I. The quality framework • Output quality and error sources • Quality management principles Part II. Principles and Guidelines • Relevance • Accuracy (and reliability) • Timeliness and punctuality • Coherence and comparability • Accessibility and clarity

  6. Principles and guidelines Principles • Prevention of errors mainly influencing the output quality dimension • Monitoring/correcting/adjusting the errors • Assessment Guidelines • Practical activities • They mention some Standard Quality indicators • Reference to the guidelines of output quality measures and indicators (WP3) • From the simpler to the most complex and costly Applications • From WP3, classified on the bases of the quality dimension and error source

  7. The quality framework Principles and guidelines Principles and guidelines Assessment of the expected impact on estimates Error prevention and adjustment * Survey lifecycle from a quality perspective (Groves et. al, 2004); Zhang, 2012

  8. Output quality / sources of errors / process phases / survey vs. administrative data Relevance

  9. The Quality Framework Accuracy – error categories • Frame and source errors (coverage, misclassification in variables) • Selectivity (sampling, unit nonresponse, missing in the admin data set) • Measurement and item missingness • Processing errors (coding, mapping, linkage, E&I, … errors) • Model assumption errors

  10. The Quality Framework: Accuracy - Selectivity Survey component Administrative data component Prevention Monitoring/Indicators Prevention Monitoring/Indicators Selectivity errors REPRESENTATION LINE Methods for Coverage Assessment

  11. The Quality Framework: Accuracy – Measurement and item missingness Survey component Administrative data component Prevention Monitoring/Correction Prevention Monitoring/Correction Measurement and item missingness REPRESENTATION LINE Methods for Coverage Assessment MEASUREMENT LINE Methods for Measurement Error Assessment

  12. The Quality Framework: Accuracy - Model Assumptions STATISTICAL MODELING • Objective and Data • Hypotheses • Selection / Identifiability • Estimation • Fitting Model assumption errors

  13. State of the art Introduction Part I. The quality framework • Output quality and error sources • Quality management principles Part II. Principles and Guidelines • Relevance • Accuracy (and reliability) • Timeliness and punctuality • Coherence and comparability • Accessibility and clarity

  14. Guidelines on the quality of multisource statistics Thank you Giovanna Brancato, Istat, brancato@istat.it

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