1. Statistics Portugal/ Metadata Unit Monica Isfan (monica.isfan@ine.pt)monica.isfan@ine.pt « Joint UNECE/ EUROSTAT/ OECD Work Session on Statistical Metadata (METIS) 11 –13 March 2009 Variables Subsystem

2.  Variables subsystem  Relationship with other systems  Search and management applications  Statistical indicators  Normalization and harmonization  Benefits Overview

3. Variables Subsystem ISO/ IEC 11179 + IDMB Statistics Canada  Statistical Survey Design  Automatic Questionnaire Generations  Statistical Dissemination  Facilitate Standardization  Identify Duplicates  Facilitate Data Sharing

4. Variables Subsystem Production System Dissemination System Variable subsystem

5. Variables Subsystem Family/ Theme Conceptual Variable Variable Statistical Indicator Object Class Value Domain Unit of Measure Property Representation Class

6. Variables Subsystem Property Object class (population or statistical unit) Representation class Value domain Variables Statistical indicators Variables Subsystem defined

7. Variables Subsystem Personal information Filter 1 All persons of the household

8. Variables Subsystem Labour force questionnaire/ personnel data (persons - members of the household) Property Object class Representation class Value domain

9. Variables Subsystem Property Marital status Object Class Person Representation class Code Value domain Enumerated (classification + level of classification) Concept “Marital status”- 174 A person's legal situation consisting of the qualities defining his or her personal status in terms of family relations figuring in the register. It comprises the following situations: a) single, b) married, c) widow(er), d) divorced. No concept LevelCodeName 11Single 12Married or cohabiting 13Widowed 14Divorced or separated Marital status_Person_Code_Table of marital status/ level 1

10. Variables Subsystem  Formal name not user friendly;  Formal name very long;  Variables must supply both production systems and dissemination systems;  Variables effectively searchable;

11.  External name General Rule: Property + (Qualifier term) + Object Class Example: Marital status of person Legal reserves (€) of enterprise  Abbreviate name General Rule: Property + (Qualifier term) Example: Marital status Legal reserves (€) Qualifier term: A word or words which help define and differentiate a name within the database Variables Subsystem

12. Conceptual variables Variables Subsystem Relationship with other systems Concepts Subsystem Bidirectional View Concepts

13. Value domain of variable Variables Subsystem Relationship with other systems Classification Subsystem Bidirectional Views Version (level) Bidirectional Views

14. Variables Variables Subsystem Relationship with other systems Methodological Documents Subsystem Version

15. Variables Variables Subsystem Relationship with other systems Data Collection Instruments Subsystem Questionnaire

16. Variables Variables Subsystem Relationship with other systems Questionnaire Data base Question Observation: Not yet developed

17. Statistical indicators Variables Subsystem Relationship with other systems Dissemination Data base Statistical indicators view

18. Search and management application Search application Management application

19. Statistical Indicators Data element that represents statistical data for a specified time, place, and other characteristics. (“Terminology on Statistical Metadata, Conference of European Statisticians – Statistical Standards and Studies – Nº 53”). Statistical Indicator

20. Variables subsystem Statistical indicator defined Variables Aggregate VariablesDimensions +  D1 = Time  D2 = Geography  …….  Dn = Other characteristics Statistical Indicators

21. Aggregate variable D2 = Dimension (geography) Dn = Other dimensions, by and …, Dn-1 = Other dimensions Name definition

22. Sex Statistical Indicators Aggregate variable Dimension (geography) Other dimensions Resident population Place of residence, by and Age group

23. Statistical Indicators Step 1. Analyse of data and metadata Step 2. Variables and statistical indicators proposal Step 3. Register and approval of variables Step 4. Register and approval of statistical indicators Step 5. Transmission of metadata and data

24. Variables Subsystem Statistical Indicators (view) Metadata DataWarehouse Data Base Statistical Indicators DB Metadata Data Internet Statistical Indicators

25. Why ????? 1. Sex: Masculine………1  Feminine………..2  2. Gender: ………………………. 3. Sex of person: Male………….1  Female………2  Normalization and harmonization

26. “ A theory is more impressive the greater is the simplicity of its premises, the more different are the things it relates, and the more extended its range of applicability…” Albert Einstein Basic steps:  Conceptual analysis;  Normalization;  Harmonization.

27. Normalization and harmonization  Selection of variables;  Identification and documentation of potential incompatibilities;  Compiling the existent documentation, determining variables availability and use;  Classification in chapters by main concept;  Preparation of the proposed variable;  Documentation for the future normalization scheme, etc. Conceptual analyses

28. The normalization process consists in:  If the variable is already registered in the Variables System, it is equivalent to be normalized and ready to harmonization (if it’s the case).  If the variable is not in the Variables System, then we most follow: Normalization and harmonization 1.Comparison of proposed variable with the normalized variables 2. Definition of all basic attributes of variables 3. Definition of formal, external and short names for variables 4. Process of registry, verification and approval

29. Harmonization Reinforce the contextual study of variables  Production System (Methodological Documentation, Questionnaires, Administrative Sources, etc);  Dissemination System ;  Data Warehouse. Use/ reuse of the same variable in different contexts Normalization and harmonization

30. Harmonization Proposal Consulting Group (Production Division, Dissemination Unit and Methodological Unit). Preferred variable for use in data interchange and in new or updated applications. Normalization and harmonization

31. Chapter  Statistical area of use:  Main Concept or Main Definition:  Observations:  Filter:  Statistical Unit:  Classification:  Normalized variables registered in Variables System proposed for harmonization  Coding process:  Questionnaire:  Example of a questionnaire module which meets the requirements documented in this proposal.  Operational issue:  Dissemination requirements:  Good practices: Normalization and harmonization

32. Benefits  Increased chances of sharing data and metadata with other agencies;  Single point of reference for data harmonization;  Reduce redundancies and anomalies;  Central reference for survey re-engineering and re- design;  Reduce ongoing production costs;  Reduce statistical burdens;  Improvement of quality and understandability of disseminated data

33. Thank you for your attention Variables Subsystem