Michael Bruneforth, UNESCO Institute for Statistics Expert Group on SDMX m.bruneforth@uis.unesco.org May 10, 2007, UN, G

1. Building SDMX Data Structure Definitions based on a generic conceptual model for contentsExperience with the joint Eurostat-Unesco-OECD education statistics questionnaire Michael Bruneforth, UNESCO Institute for Statistics Expert Group on SDMX m.bruneforth@uis.unesco.org May 10, 2007, UN, Geneva

2. Overview • The world of international education data collections • Why building a conceptual model • Steps to build the model • The model • From the model towards a SDMX data structure definition

3. The world of international education data collections

4. Instruments used in the system of international education data collections • The UNESCO-UIS / OECD / EUROSTAT (UOE) Data Collection on Education Statistics • EXCEL based questionnaire, organized in 31 work sheets • 47 countries, 14,000+ data points • Changes: 2003 • The World Education Indicators Project (WEI) • Based on UOE Instruments, extended by 10 work sheets • 16 countries , >15,000+ data points • Examples at www.uis.unesco.org/publications/wei2006 • The UIS Survey • Pdf based E-Questionnaire infrastructure, plus paper form • All remaining countries, 5,000+ data points • Examples at www.uis.unesco.org -> current surveys

5. Instruments used in the system of international education data collections (II) Can be transformed Can be transformed UOE WEI i ii i i i i UIS

6. Education Questionnaires: ever changing • 1998 • Tables were introduced after ISCED 97 was adopted. • 2000 • Redesign of Finance tables. • 2001 – 2005: ??? • 2005 • Major redesign: Tables redesigned, some tables spilt or combined. • 2006 • In ENRL8a; ENRL8b and ENRL8c: the Caribbean countries are now included with Latin America instead of Northern America. • 2007 • In table ENRL-7, three new sub-categories, “unknown residence”, “unknown prior education”, and “unknown citizenship” have been added.. • In ENTR-2 a new row has been added to collect typical age of entry. • In GRAD-1 and GRAD-3 a new row has been added to collect typical graduation age.

7. Why building a conceptual model? • Meta data • Theoretical basis for describing data • Visualization of data • Validation of codes • Questionnaire design • Improving internal consistency in questionnaires • Maintaining the coding schemes: • Avoiding random or ad-hoc data descriptions leading to inconsistent, incomprehensible systems • (we need discipline as much as a model!)

8. Why using a conceptual model as basis for SDMX? • A model describes a universe of questionnaires • Consistency across questionnaires • Consistency across tables • Consistency across statistical units • Facilitates adaptation of SDMX to changes to tables • Typically no/few keys need to be changed, most new data can be defined using existing keys • A model can be used to describe indicators and derived data • SDMX exchange of results (->WorldBank, MDG) • A model can be transformed into/from data base definitions • Use of existing meta data (efficiency) • Avoid redundant information (less error prone) • Basis to match national data to international SDMX definitions

9. Building the model • Step 1: Bo Sundgren’s analysis of the UIS Questionnaire • Step 2: Analysis of the relational data base at UIS • Step 3: Correction / Expansion of Bo’s model • Step 4: Model verification1: review of UOE questionnaires • Step 5a: Model verification2: Transformation of UIS database model to conceptual model, automated creation of full code list • Step 5b: Model verification2: Analysis of the relational data base at OECD • Step 6: Creation of data structure definition based on existing meta data

11. Example 1: Students and Repeater

12. Count of lower secondary general students

13. Count of male lower secondary general repeater at grade 2

14. Example 2: Students and Classes

15. Count of lower secondary general classes and students, class size

16. Example 2: Students and Classes

17. Count of new entrants to tertiary 5B with previous tertiary education

20. Principles for the generation the detailed model for individual data points • Use existing meta data • Avoid multiple capturing of questionnaire information • Ensure consistency with existing systems

21. Generate the detailed model for individual data points

22. The basis: the UIS meta data (relational database description)

23. Example: UIS meta data (relational database codes, XML version)

24. What is needed beyond the model to get a complete data structure definition? • The data structure definition has to cope with data points collected twice. • Total number of primary students is collected in ENRL1a, ENRL1, ENRL3, ENRL4, CLASS1 • The data structure definition has to cope with adjustements to data concerning coverage of data. • The count of student is collected with coverage adjusted to expenditure data.

25. Thanks Questions, comments? Education content: Michael Bruneforth (m.bruneforth@uis.unesco.org) IT: Brian Buffett (b.buffett@uis.unesco.org)