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1A_3-A geodemographic classification of london primary schools
1A_3-A geodemographic classification of london primary schools
1A_3-A geodemographic classification of london primary schools
1A_3-A geodemographic classification of london primary schools
1A_3-A geodemographic classification of london primary schools
1A_3-A geodemographic classification of london primary schools
1A_3-A geodemographic classification of london primary schools
1A_3-A geodemographic classification of london primary schools
1A_3-A geodemographic classification of london primary schools
1A_3-A geodemographic classification of london primary schools
1A_3-A geodemographic classification of london primary schools
1A_3-A geodemographic classification of london primary schools
1A_3-A geodemographic classification of london primary schools
1A_3-A geodemographic classification of london primary schools
1A_3-A geodemographic classification of london primary schools
1A_3-A geodemographic classification of london primary schools
1A_3-A geodemographic classification of london primary schools
1A_3-A geodemographic classification of london primary schools
1A_3-A geodemographic classification of london primary schools
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

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GISRUK 2010 Session 1A, Presentation 3

GISRUK 2010 Session 1A, Presentation 3

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

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