Linking by Translation: the key to comparable codesets Ben Hickman Local Government Analysis &...
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Transcript of Linking by Translation: the key to comparable codesets Ben Hickman Local Government Analysis &...
Linking by Translation:the key to comparable codesets
Ben HickmanLocal Government Analysis & Research19th 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
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
Issues with multiple-sources
» Data sharing protocols» Lack of consistent definitions» Different census dates» Reliability of methodologies» Lack of a comparable occupational
classification
Linkage by Translation
Dataset 1 Dataset 2
Dataset 3
Key-Codes
Linkage by Translation
» Key-codes links each dataset» Able to aggregate to lowest
common denominator» Enables comparisons across
multiple datasets without continual remapping
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
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
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
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
Local Government Analysis & Research
» Numbers and pay research» Covers all local government staff» Approx. 100 classifications» Less detailed classifications» Not education specific
Office of National Statistics
» Labour Force Survey» Provides details for whole economy» Occupational classification = SOC2000» Also not education specific» enables national and international
comparisons
Schools Workforce Census
DfES Classification LGAR Pay ResearchAll LG classification
Labour Force SurveySOC2000
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
So, for Schools Workforce key-codes...
Post x Role = Var1
Advanced Skills
Teacher
Excellent
Teacher
Support Staff
Caretaker XClassroom Teacher
X X
Cleaner X
Var1 x School = Var2 Nursery and
Primary Schools
Secondary Schools
Special
Schools
Classroom Teacher (Advanced Skills)
X X X
Classroom Teacher (Excellent Teacher)
X X X
Cleaner X X X
Var2 x SOC2000 = Schools Key-codes 2314 2315 231
6
Classroom Teacher (Advanced Skills) (Nursery and Primary Schools)
X
Classroom Teacher (Excellent Teacher) (Nursery and Primary Schools)
X
Classroom Teacher (Excellent Teacher) (Secondary Schools)
X
LGAR Pay Research
higher level teaching assistant n.e.c.
higher level teaching assistant (special schools)
higher level teaching assistant (secondary schools)
higher level teaching assistant (primary and nursery schools)
teaching assistant n.e.c.
teaching assistant (special schools)
teaching assistant (secondary schools)
teaching assistant (primary and nursery schools)
literacy workers
minority ethnic support
learning support assistant (for SEN pupils)
learning mentor
language support
bilingual support assistant
Higher level Teaching Assistant
Teaching Assistant Educational assistant n.e.c.
6124 Educational assistant
Data Linkages Demonstration
Without translation
DfES Classification Pay classification
SOC2000
NMDS-SCChild services
mapping
Soulbury Committee
Simplify through translation...
Key-codes
DfES Classification Pay classification
SOC2000
NMDS-SCChild services
mapping
Soulbury Committee
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
Disadvantages
» Mapping key-codes takes time and knowledge of each dataset
» If one detailed dataset changes it can require major amendments to key-codes
Thanks for listening
Any questions?