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1A_3_A geodemographic classification of london primary schools
 

1A_3_A geodemographic classification of london primary schools

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    1A_3_A geodemographic classification of london primary schools 1A_3_A geodemographic classification of london primary schools Presentation Transcript

    • A geodemographic classification of London primary schools
      Anne Gibbs, John Stillwell & Linda See
      14th April 2010
    • Structure of the presentation
      Aim of the research
      Research questions
      Methodology
      The Classification
      What it reveals: the performance of different types of schools
      Further research
    • Why Classify?
      There is a complex set of relationships between schools, neighbourhoods and performance.
      Pupil populations are particularly diverse in London.
      Classifying schools by their pupil populations:
      creates some order out of the chaos;
      enhances understanding by highlighting similarities and differences between schools;
      has potential to be used as a benchmarking tool by policy makers and managers;
      provides a framework for further research from both geographical and educational perspectives.
    • The Database
      Spring Census 2007
      All pupils in maintained primary schools in London Reception to Year 6
      Derive new variables e.g. % of mobile pupils in Year 6
      English & Maths Levels
      • low
      • medium
      • high
      GeoConvert
      • grid references
      • matching to Lower Layer Super Output Areas (LSOAs)
      The Database
      Key Stage 2 Results
      Edubase
      IDACI / LSOA Area Classification
    • Ethnicity of London Primary Pupils
      Base population = total number of pupils in the school in the statutory years of Reception to Year 6
    • Socio-economic variables 1
      % eligible for Free School Meals (FSM)
      % with English as an Additional Language (EAL)
      Base population = total number of pupils in the school in the statutory years of Reception to Year 6 (except for mobility rate, where base is Year 6 only)
    • % FSM Pupils (quartiles)
    • % EAL Pupils (quartiles)
    • Socio-economic variables 2
      % eligible for Free School Meals (FSM)
      % with English as an Additional Language (EAL)
      % with Special Educational Needs (SEN)
      % of 2006/7 Year 6 pupils who entered their school after the beginning of Year 5 (MOBILE)
      Base population = total number of pupils in the school in the statutory years of Reception to Year 6 (except for mobility rate, where base is Year 6 only)
    • Correlations between variables
      All correlations are significant at the 1% level of confidence
    • Deriving the Schools Classification
      k-means algorithm in SPSS;
      distribution of many variables skewed, so the data was range-standardised;
      ran clustering routine for n=4 to n=22 clusters giving 19 alternative classifications;
      alternatives assessed for homogeneity and evenness of cluster size, using standardised data;
      short listing of classifications with 7,10,14 & 16 clusters.
    • Final Selection
      More detailed assessment of ‘short listed’ solutions using 3 measures of cluster validity:
      Selection of 14-cluster solution
    • A
      5
      9
      2
      B
      13
      C
      14
      1
      8
      3
      10
      6
      11
      12
      7
      D
      4
      The Schools Classification: Visualisation
      White British Mixed ethnicity Non-white / EAL
      Well-off / stable Mobile Needy
      Source: After Harris et al. (2005) Figure 6.3, p.170.
    • Cluster profiles: Group D
    • Cluster profiles: Group A
    • Cluster profiles: Group B
    • Cluster profiles: Group C
    • Super Groups by LEA
    • Contextual Performance Ranking of Clusters(indexed to global average = 100)
    • Possible Future Research
      Classification as a framework for more detailed analysis of relationship between schools and their neighbourhoods.
      Classification as a framework for more work on mobility and its impact on educational attainment.
      Potential for use as a benchmarking tool or as a sampling frame for qualitative research.
      Feasibility of an online system.
      Updating the Classification.