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Linking by Translation: the key to comparable codesets

Linking by Translation: the key to comparable codesets. Ben Hickman Local Government Analysis & Research 19th March 2007. Setting the scene. Require workforce data for all local government Approx 2.2 million employees Varied workforce Multiple sources of data No definitive source of data.

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Linking by Translation: the key to comparable codesets

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  1. Linking by Translation:the key to comparable codesets Ben Hickman Local Government Analysis & Research 19th March 2007

  2. Setting the scene • Require workforce data for all local government • Approx 2.2 million employees • Varied workforce • Multiple sources of data • No definitive source of data

  3. Local Government Data Flows Project • Systematic evaluation of current methods • Development of data framework and quality assurance measures • Occupational Classification for Local Government • Multi-sourced database • New collections targeted to cover specific gaps

  4. Issues with multiple-sources • Data sharing protocols • Lack of consistent definitions • Different census dates • Reliability of methodologies • Lack of a comparable occupational classification

  5. Linkage by Translation Dataset 1 Dataset 2 Key-Codes Dataset 3

  6. Linkage by Translation • Key-codes links each dataset • Able to aggregate to lowest common denominator • Enables comparisons across multiple datasets without continual remapping

  7. Multi variable mapping • Use matrices to map all possible combinations of classification variables i.e. Role x Post x Service • Use common classifier (e.g. SOC2000) • Map all possible variables against the common classifier • Creates key-code for every possible classification value in the dataset

  8. Multi-sourced key-codes • Map each dataset to the common classifier (SOC2000) • Use the common classifier as a basis for creating a single, unified list • Within each unit group identify overlaps and recode into a single variable where necessary – maintaining all available mapping data against the code

  9. Schools workforce • Approx. 800,000 workforce • Workforce collections undertaken by: • Department for Education and Skills • Chartered Institute of Public Finance and Accounting • Local Government Analysis and Research • Office of Manpower Economics • Office of National Statistics • Institute of Education

  10. Department for Education & Skills • Schools Workforce Census • Detailed information relating to all school staff • Broken down by: • Type of School – 4 variables • 9 role variables • 66 post variables • Total of 304 possible variables

  11. Local Government Analysis & Research • Numbers and pay research • Covers all local government staff • Approx. 100 classifications • Less detailed classifications • Not education specific

  12. Office of National Statistics • Labour Force Survey • Provides details for whole economy • Occupational classification = SOC2000 • Also not education specific • enables national and international comparisons

  13. Schools Workforce Census DfES Classification LGAR Pay Research All LG classification Labour Force Survey SOC2000

  14. Educational Assistants • DfES has 8 post x role identifiers • But with types of schools could be as many as 32 different variables (i.e. 8 posts by the 4 school types) • 3 LGAR Pay Research categories • 1 SOC2000 Code

  15. So, for Schools Workforce key-codes... Post x Role = Var1

  16. Var1 x School = Var2

  17. Var2 x SOC2000 = Schools Key-codes

  18. bilingual support assistant language support learning mentor learning support assistant (for SEN pupils) minority ethnic support literacy workers LGAR Pay Research higher level teaching assistant (primary and nursery schools) teaching assistant (primary and nursery schools) teaching assistant (secondary schools) higher level teaching assistant (secondary schools) teaching assistant (special schools) higher level teaching assistant (special schools) teaching assistant n.e.c. higher level teaching assistant n.e.c. Higher level Teaching Assistant Teaching Assistant Educational assistant n.e.c. 6124 Educational assistant

  19. Pay classification DfES Classification NMDS-SC Child services mapping SOC2000 Without translation Soulbury Committee

  20. Pay classification DfES Classification Soulbury Committee NMDS-SC Child services mapping SOC2000 Simplify through translation... Key-codes

  21. Benefits • Easy to add in additional datasets • Addition or amendment of one dataset does not affect any others • Enables datasets to be analysed to varying degrees of detail • Ensures all possible classification scenarios are accounted for • Vehicle for maintaining coding information

  22. Disadvantages • Mapping key-codes takes time and knowledge of each dataset • If one detailed dataset changes it can require major amendments to key-codes

  23. Thanks for listening Any questions?

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