Enhancing Survey and Administrative Data Analysis

1. NCRM Annual MeetingJanuary 2009

2. People

3. ADMIN • Remit of ADMIN is to develop and disseminate methodologies for making best use of administrative data by exploiting survey data (and vice versa) • Training and capacity building (Mac McDonald)

4. ADMIN • Strength of administrative data is that they have information on almost everyone. • Weakness is that they are not rich in covariates • NPD has detailed information on educational outcomes but no information on parental education. • We can link richer survey to admin data to enhance the admin data.

5. ADMIN • Weakness of survey data is non-response and attrition. • Administrative data is virtually (but not fully) complete. • Scope for using linked survey-administrative data to enhance survey data by telling us about those who are missing from survey data.

6. Aim is to develop methods to.. • make inferences when covariates or responses are missing in administrative data. • use administrative data to overcome measurement error in survey variables (e.g. recalled event histories) and vice versa (e.g. ethnicity) • to tackle bias due to attrition in longitudinal surveys. • using administrative data to improve small-area estimates of the means and quantiles of survey variables.

7. Programme 1 (Vignoles) • Using survey data to enhance methods for the analysis of administrative data • Measuring the effect of family background and ethnicity on pupil attainment • To what extent does school attendance reflect real school preferences?

8. Programme 2 (Brown) • Using administrative data to enhance methods for the analysis of survey data • Attrition, non-response and the determinants of school outcomes at 16 • Enhancing event history analysis of social surveys with administrative data

9. Examples of linked data • Linked administrative data schools data (NPD/PLASC), FE data (ILR) and higher education data (HESA) • Complete administrative data on entire cohort all the way through the education system • NPD/PLASC linked to survey data • LSYPE • MCS

10. Contribution to work on segregation • Measuring segregation • Socio-economic segregation • Ethnic segregation • Modelling causes of segregation • Parental school choice • Examples drawn from schools but could be applied more broadly

11. Measuring socio-economic segregation • Currently measured by FSM binary status • Problematic measure (Hobbs and Vignoles, 2008) • Only picks up bottom 16% of distribution at best • Measurement error in FSM status • E.g. children who do not eat at school not recorded as FSM • Changing FSM status in recession

12. Measuring socio-economic segregation • Linked data has already provided an assessment of the extent to which FSM really proxies socio-economic disadvantage • Can provide alternative measures of socio-economic background from surveys • Parental income/ high low income • Parental education/ high low education • Can assess use of alternative proxies from administrative data e.g. geographic data

13. Measuring socio-economic segregation • Linked data can test robustness of segregation work that uses FSM • Need to be aware of issues raised by Becky Allen on using samples to measure segregation

14. Ethnic Minority Project • Originally conceived of as a study of ethnic differences in outcomes • i.e. focusing on missing covariates in model of ethnic achievement • See work by Wilson et. al., 2005 • Data - PLASC/NPD data linked to LSYPE (cohort born 1990/91)

15. Ethnic Minority Project • Do we get estimates of ethnic differences in outcomes wrong if we just rely on administrative data ? • Do ethnic classifications capture what we are interested in? e.g. example of recent migrants versus long standing populations • What are differences by ethnicity once we take account of language (EAL)?

16. KS3 Results for Pakistani Males NB: Results show differences in standardized score outcomes NPD controls include gender, ethnicity, age, EAL, SEN, FSM, KS2 score

17. Measuring ethnicity and EAL • Is there measurement error in the ethnicity or EAL variables in PLASC? • If so, are there implications for measuring ethnic segregation and ethnic differences in outcomes ? • see Aspinall and Jacobson, 2007; Battistin and Sianesi, 2006

18. Measurement error in ethnicity and EAL • Multiple measures from LSYPE • Ethnicity and ethnic origin self report • Ethnicity and ethnic origin parents • Language spoken at home • Frequency of English spoken at home • Measures from PLASC • Ethnicity • EAL binary indicator

19. Measurement Error • Misclassification of ethnicity not huge • Sub sample for whom we have full data and who live with both natural parents 7814 • 136 individuals recorded as white British in PLASC but are not according to LSYPE • 57 individuals recorded white British in LSYPE but not in PLASC • Evidence of misclassified EAL • 7.2% young people labelled EAL in PLASC but appear not to be from LSYPE

20. Ethnicity of those “wrongly” coded EAL in PLASC

21. Correlations with error EAL/non white sample

22. Modelling causes of segregation • Linked MCS data with NPD/PLASC • Details of current school, ranked school choices, reasons for school choice • Can provide missing covariates in models of causes of segregation e.g. attitudes to school choice • Currently project investigating school choice in MCS (Burgess, Greaves, Vignoles and Wilson)

23. Short Courses • Introduction to Data Linkage: • The Value of Data Linkage for Research • Data Linkage – Methodological and Statistical Issues • Enhancing Longitudinal Surveys by Linking to Administrative Data: • Longitudinal Data Analysis • Event History Analysis • Using Longitudinal Data Linkage to Evaluate Area-Based Interventions • Data Linkage with the NPD