Tammy thiele  june 2012
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  • Neighborhoods, Ethnicity and School Choice: Developing a Statistical Framework for Geodemographic Analysis (2007)Richard Harris Æ Ron Johnston Æ Simon Burgess Popul Res Policy Rev (2007) 26:553–579 DOI 10.1007/s11113-007-9042-9 Applying GIS Technology and Geodemographics to College and University Admissions Planning: Some Results from Ohio State UniversityAn example of the use of geodemographics in examining access to education in the United States. The article was written by Professor D Marble and Mr V Mora of the Ohio State University.Additional keywords: Academic USA Research Education Geodemographics PRIZM TIGERgis.esri.com/library/userconfAccess to Higher Education 1991-98: 'Using Geodemographics'A reproduction of an article published in the journal Widening Participation and Lifelong Learning by David Tonks of Lancaster University, in which UK university entrants are analysed by their geodemographic profiles.Additional keywords: Academic UK Research Education Geodemographicswww.staffs.ac.uk/journal/volonetwoCACI LimitedAcorn, the first geodemographic classification in the UK, is produced by market analysis company CACI. The company provides a combination of data, software and consultancy to help market products and services more effectively to the right consumers.Additional keywords: Commercial Biz UK ACORN Geodemographics Lifestyles Databaseswww.caci.co.ukPopul Res Policy Rev (2007) 26:553–579 DOI 10.1007/s11113-007-9042-9 Neighborhoods, Ethnicity and School Choice: Developing a Statistical Framework for Geodemographic Analysis Richard Harris Æ Ron Johnston Æ Simon Burgess Archer L, Hutchings M, 2000, ``'Bettering yourself '? Discourses of risk, cost and benefit in ethnically diverse, young working-class non-participants' constructions of higher education'' British Journal of Sociology of Education 21 555 ^ 574Archer, L, Hutchings (2000) Bettering yourself, discourse of risk, cost and benefit in ethnically diverse, young working class non participants. Conway, C., Coombes, M. and Raybould, S. (2002), Participation in Further and Higher Education across the North East Region: A Benchmark Analysis of Recent Patterns, Centre for Urban and Regional Development Studies, University of Newcastle, Newcastle upon Tyne. Croot, D. and Chalkley, B. (1999), “Student recruitment and the geography of undergraduate geographers in England and Wales”, Journal of Geography in Higher Education, Vol. 23 No. 1, pp. 21-47. • Clare Holdsworth (2006) 'Don't you think you're missing out, living at home?' Student experiences and residential transitions. Sociological Review,(),pp.495-519 • Jackie Patiniotis and Clare Holdsworth (2005) 'Seize that chance!': Leaving Home and Transitions to Higher Education. Journal of Youth Studies,8(1),pp.81-95 
  • Deprivation in Liverpool from public profiler. Also possible to show peoples grades according to location, Relates to AAB.Medicine, knowing how to widen participation will help you know how to do it for every subject,

Tammy thiele  june 2012 Tammy thiele june 2012 Document Transcript

  • Finding Finding the Vicars DaughterTheVicars Daughter
  • Overview• Educational Disadvantage• Contextual Data and Applications• Current Research Findings• Challenges• Conclusion
  • Educational Disadvantage• Social class remains the strongest predictor of educational achievement in the UK• Children from low SES slower at acquiring language , less proficient at mathematical tasks and gain significantly poorer paper qualifications (Aikens & Barbarin, 2008, Coley, 2002; Chowdry et al., 2009) – PISA (2009) gap between the mean scores of UK students in the 90th and 10th percentiles was 246 points – the equivalent of six years of schooling on the average
  • Impact of Educational Disadvantage• Hoar & Johnston (2008) school grades do not represent true ability• In England students from the highest social class groups are three times as likely to enter university than those from the lowest social class groups.• “ Graduates on average have better employment prospects and can expect to earn at least £100,000 (160,000 gross) net of tax, more than non-graduates across their working lives.”
  • Targeting “Disadvantage” NS SEC 4 to 7 Data routinely collectedSchools/ Colleges Low Participation neighbourhood Not routinely collectedDfE/Data Service Free School Meals (Pupil Premium) Indices of Multiple Deprivation / Gathered by (or can be 16 – 19 Bursary IDACI derived from) SEN or AP+ or AP? UCAS…good for baselines, target setting, In care No parental HE tracking and evaluation Geodemographics Disabled In care State school / college Low income backgrounds Information for Targeting & Contextual Data Ethnicity Gender HEIs own criteria
  • Practical Usage of Contextual Data• University of Bristol • Priority is given to students in the following categories:
Local students (resident in BA or BS postcodes) • Those who are part of the first generation in their family to go to university; • Those living in a Low Participation Area (LPN).” • Research Cluster (Hoare, 2009), justify admitting students with between one to two grades lower (for typical AAA offers) and three grades lower (for ABB offers).• University of Birmingham • Basket of measures- Family Education (little or no experience of HE) (1), Parental Occupation- based on household income (£42,600 or less) (2), Post code data, school/college/area rates of progression to HE (3), teacher recommendation of application (4), non- selective state school or college (5)If at selective state school the school where you did your GCSE’s must have achieved less than 49% A*-C grades at GCSE• University of Manchester • Flagged based on following criteria: Performance of school at level 1 ( GCSE or Equivalent) or Level 3 ( A level or equivalent) – flagged if school performs below national average, post code data ( use ACORN and LPN) , care or disabled. • Overall contextual flag is produced if you meet at least one of the social/educational indicators plus the postcode indicator. You will also receive an overall flag if you have been in care for more than three months. – Geo-demographic indicator – An online postcode look-up facility – Educational Indicators (PDF document, 2 MB) – a list of schools and contextual flags
  • Identification of SESSocial – Geo-demographic indicators of disadvantage and lowprogression to HE USED by most Universities• CACI ACORN provides the smallest granulation of analysis, on the full postcode with detailed descriptors for each type• ACORN data consists of 5 categories, 17 groups and 56 types.• HEFCE POLAR2 data assigns all electoral wards into quintiles based on progression to HE. Those wards in the lowest quintile according to HE progression are classified as Low Participation Neighbourhoods.• NS-SEC : – Information obtained directly from UCAS application form – Unknown data ~ 25% – NS-SEC 8 not included – Who categorises the labels?
  • University of Liverpool (2010-2011): Proportion of students and their degree classification in relation to NS-SEC 60 50 40 NS-SEC 1-3 30% NS-SEC 4-7 Unknown 20 10 0 I II-1 II-2 III PASS
  • “Analysis based on NS SEC 4-7 excluded students frombenefit-dependentfamilies, and Aimhigher wastargeting those families,““Participation rates fromstudents receiving free schoolmeals rose from 13 per cent in2005 to 17 per cent in 2008.”"Aimhigher was busyincreasing applications, but alot were not able to getuniversity places… there was abig excess of demand oversupply”Aimhigher targeted children asyoung as 13, so many of thesepupils have not even applied touniversity yet.“
  • Current Research– PILOT PHASE– Stage 1 Analysis INCLUDES ALL degree programmes between 2004/5 and 2009/10 at UoL– ISSUES-Old Faculties (How can we map on to new faculties?), data difficult to obtain, limited to those that completed degree programmes– However, findings from stage 1 highlight meaningful predictors and differences between faculties
  • Flexible and cost- effective approaches: •Use baskets of • Eligible for FSM OR • Receipt of 16-19 Bursary OR measures for macro-A • School Performance targeting to avoid vicar’s daughter and • No Parental HE or overcome limitations of • Parents in manual / semi skilled occupations singular measures and or unemployed (NS-SEC 4-8)B • Deprived Postcode (IMD, LPN, ACORN) OR statistics OR • Disabled OR • In careC
  • UoL - Percentage of Students in Each of the Faculties and forCombination of ALL undergraduate 3yr programmes 2004/5-2009/10 E A V S S&E M 3yrs Male 84.5 42.3 23.2 38.9 44.6 23.4 41.4 Ethnic Minorities 18.3 6 8.7 10.3 8.2 8.2 8.5 26+ 7.7 3.5 0 2.3 2.5 11.2 3.6 Disability 2.8 6.6 8.7 6.1 6.4 8.2 6.5E- Engineering S- ScienceA- Arts S& E- Social and Environmental SciencesV- Veterinary Sciences M- Medical Sciences 3yrs- All 3 year degree programmes combined
  • UoL: Percentage of Students in Lowest and Highest Quintiles by IMD 2004/5-2009/10 E A V S S&E M 3yrs 4yrs 35.2 25.1 18.8 28 23.2 20.2 25 27.5 Quintile 1& 2 Quintile 49.4 48.2 62.3 44.9 50.1 48.7 47.5 44.6 4&5E- Engineering S- ScienceA- Arts S& E- Social and Environmental SciencesV- Veterinary Sciences M- Medical Sciences 3yrs- All 3 year degree programmes combined
  • Potential analysis planWe are interested in what predictsoutcome (final % score or degreeclassification)Variables we are looking to explore; UCAS entry score IMD (as a proxy for SE status) State school performance Gender
  • • How/when are • How/ when will we we going to assess what difference monitor the data we have made? (5 or and related more years?) audit?• How are we going to assess progression and retention? • How are we going to ensure• Stage 2– that we take Interviews/Focus into account Groups Demographic Changes?
  • Conclusion-The ChallengesWhat do we need to know:• To target effectively? – What indicators to include?What data do we need, who holds it, and what do we need to collect? – SPA suggested Basket of Measures How can data be “sourced once and used many times”/ what are the resource implications? – Collaboration with Universities?
  • References & Useful Sources• https://www.zotero.org/tammyt/items/• http://www.maptube.org/map.aspx?s=DBHFOjWDOMsqg ol5yWDAp1wcCnY8CghN• Association of Geographic Information Laboratories for Europe (AGILE) – promoting academic teaching and research on GIS at the European level• Cartography and Geographic Information Society (CaGIS)• Directions Magazine – All Things Location• Federal Geographic Data Committee—United States federal government standards agency.• Geographic Information System (GIS) Educational website— Educational site with PDF lessons and videos to accompany free GIS software.